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@InProceedings{Chen2015,
  Title                    = {{Compressing Neural Networks with the Hashing Trick.}},
  Author                   = {Wenlin Chen and James T. Wilson and Stephen Tyree and Kilian Q. Weinberger and Yixin Chen},
  Booktitle                = {{ICML}},
  Year                     = {2015},
  Editor                   = {Francis R. Bach and David M. Blei},
  Pages                    = {2285--2294},
  Publisher                = {JMLR.org},
  Series                   = {{JMLR Proceedings}},
  Volume                   = {37},

  __markedentry            = {[yani:6]},
  Crossref                 = {conf/icml/2015},
  Keywords                 = {dblp},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@InProceedings{Fahlman1989,
  Title                    = {{The Cascade-Correlation Learning Architecture.}},
  Author                   = {Scott E. Fahlman and Christian Lebiere},
  Booktitle                = {{NIPS}},
  Year                     = {1989},
  Editor                   = {David S. Touretzky},
  Pages                    = {524--532},
  Publisher                = {Morgan Kaufmann},

  __markedentry            = {[yani:6]},
  Crossref                 = {conf/nips/1989},
  ISBN                     = {1-55860-100-7},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@InProceedings{He2015b,
  Title                    = {{Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification.}},
  Author                   = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
  Booktitle                = {{ICCV}},
  Year                     = {2015},
  Pages                    = {1026--1034},
  Publisher                = {IEEE},

  __markedentry            = {[yani:6]},
  Crossref                 = {conf/iccv/2015},
  Keywords                 = {dblp},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@InProceedings{Krizhevsky2012,
  Title                    = {{ImageNet Classification with Deep Convolutional Neural Networks.}},
  Author                   = {Alex Krizhevsky and Ilya Sutskever and Geoffrey E. Hinton},
  Booktitle                = {{NIPS}},
  Year                     = {2012},
  Editor                   = {Peter L. Bartlett and Fernando C. N. Pereira and Christopher J. C. Burges and L{\'e}on Bottou and Kilian Q. Weinberger},
  Pages                    = {1106--1114},

  __markedentry            = {[yani:6]},
  Crossref                 = {conf/nips/2012},
  Keywords                 = {Convolutional with Classification Deep ImageNet Networks. Neural},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@InProceedings{conf/icml/NairH10,
  Title                    = {{Rectified Linear Units Improve Restricted Boltzmann Machines.}},
  Author                   = {Vinod Nair and Geoffrey E. Hinton},
  Booktitle                = {{ICML}},
  Year                     = {2010},
  Editor                   = {Johannes F{\"u}rnkranz and Thorsten Joachims},
  Pages                    = {807--814},
  Publisher                = {Omnipress},

  Added-at                 = {2010-09-06T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/2059683ca9b2457d248942520babbe000/dblp},
  Crossref                 = {conf/icml/2010},
  Ee                       = {http://www.icml2010.org/papers/432.pdf},
  Interhash                = {acefcb0a5d1a937232f02f3fe0d5ab86},
  Intrahash                = {059683ca9b2457d248942520babbe000},
  Keywords                 = {dblp},
  Url                      = {http://dblp.uni-trier.de/db/conf/icml/icml2010.html#NairH10},
  X-fetchedfrom            = {Bibsonomy}
}

@InProceedings{conf/cvpr/RigamontiSLF13,
  Title                    = {{Learning Separable Filters.}},
  Author                   = {Roberto Rigamonti and Amos Sironi and Vincent Lepetit and Pascal Fua},
  Booktitle                = {{CVPR}},
  Year                     = {2013},
  Pages                    = {2754--2761},
  Publisher                = {IEEE},

  __markedentry            = {[yani:]},
  Added-at                 = {2013-10-17T00:00:00.000+0200},
  Crossref                 = {conf/cvpr/2013},
  Ee                       = {http://dx.doi.org/10.1109/CVPR.2013.355},
  Interhash                = {e712dfc89230b494110efa966e4a1b9e},
  Intrahash                = {9970ae7fa950009901ecbf616fa03011},
  Keywords                 = {dblp},
  Owner                    = {yani},
  Timestamp                = {2016.07.27},
  X-fetchedfrom            = {Bibsonomy}
}

@InProceedings{Sutskever2013momentum,
  Title                    = {On the importance of initialization and momentum in deep learning},
  Author                   = {Ilya Sutskever and
 James Martens and
 George E. Dahl and
 Geoffrey E. Hinton},
  Booktitle                = {Proceedings of the 30th International Conference on Machine Learning,
 {ICML} 2013, Atlanta, GA, USA, 16-21 June 2013},
  Year                     = {2013},
  Pages                    = {1139--1147},

  Crossref                 = {conf/icml/2013},
  Url                      = {http://jmlr.org/proceedings/papers/v28/sutskever13.html}
}

@Article{journals/neco/AmitG97,
  Title                    = {{Shape Quantization And Recognition With Randomized Trees.}},
  Author                   = {Yali Amit and Donald Geman},
  Journal                  = {Neural Computation},
  Year                     = {1997},
  Number                   = {7},
  Pages                    = {1545--1588},
  Volume                   = {9},

  Added-at                 = {2011-11-17T00:00:00.000+0100},
  Biburl                   = {http://www.bibsonomy.org/bibtex/28c6c1c1e70a8a960dbb7d991012f231c/dblp},
  Ee                       = {http://dx.doi.org/10.1162/neco.1997.9.7.1545},
  Interhash                = {b9f60c8c731f18e6fb2d7629c2ebc6ef},
  Intrahash                = {8c6c1c1e70a8a960dbb7d991012f231c},
  Keywords                 = {dblp},
  Url                      = {http://dblp.uni-trier.de/db/journals/neco/neco9.html#AmitG97},
  X-fetchedfrom            = {Bibsonomy}
}

@InProceedings{Ba2013dothey,
  Title                    = {{Do Deep Nets Really Need to be Deep}},
  Author                   = {L. J. Ba and R. Caruana},
  Booktitle                = {{eprint ar{X}iv:1312.6184v5}},
  Year                     = {2013}
}

@InProceedings{bastani2016measuring,
  Title                    = {{Measuring Neural Net Robustness with Constraints}},
  Author                   = {Osbert Bastani and Yani Ioannou and Leonidas Lampropoulos and Dimitrios Vytiniotis and Aditya Nori and Antonio Criminisi},
  Booktitle                = {{Neural Information Processing Systems (NIPS), 2016}},
  Year                     = {2016},

  Owner                    = {yani},
  Timestamp                = {2016.08.30}
}

@InProceedings{Bengio2010labeltree,
  Title                    = {{Label Embedding Trees for Large Multi-Class Tasks}},
  Author                   = {S. Bengio and J. Weston and D. Grangier},
  Booktitle                = {{Conference and Workshop on Neural Information Processing Systems}},
  Year                     = {2010}
}

@Article{bengio:ieeenn94,
  Title                    = {{Learning Long-Term Dependencies With Gradient Descent Is Difficult}},
  Author                   = {Yoshua Bengio and Patrick Simard and Paolo Frasconi},
  Journal                  = {IEEE Transactions on Neural Networks},
  Year                     = {1994},
  Number                   = {2},
  Pages                    = {157--166},
  Volume                   = {5},

  Added-at                 = {2008-03-11T14:52:34.000+0100},
  Biburl                   = {http://www.bibsonomy.org/bibtex/2b0be73f780814059555520de2e79a1cb/idsia},
  Citeulike-article-id     = {2375859},
  Interhash                = {dc63076a6924e888ceda10f23158c23d},
  Intrahash                = {b0be73f780814059555520de2e79a1cb},
  Keywords                 = {nn},
  Priority                 = {2},
  X-fetchedfrom            = {Bibsonomy}
}

@Book{Bishop1995,
  Title                    = {{Neural Networks for Pattern Recognition}},
  Author                   = {Christopher M. Bishop},
  Publisher                = {Oxford University Press},
  Year                     = {1995},

  Address                  = {Oxford},

  Added-at                 = {2010-02-27T01:05:18.000+0100},
  Biburl                   = {http://www.bibsonomy.org/bibtex/2d0617b6f86ffb0e84b77a14f2810e72b/tb2332},
  Interhash                = {e7e5ff5e89d29b7fa2787aad12bea123},
  Intrahash                = {d0617b6f86ffb0e84b77a14f2810e72b},
  Keywords                 = {imported},
  Owner                    = {thierry},
  Timestamp                = {2009.04.18},
  X-fetchedfrom            = {Bibsonomy}
}

@InCollection{Bottou2012sgdtricks,
  Title                    = {{Stochastic Gradient Descent Tricks.}},
  Author                   = {L{\'e}on Bottou},
  Booktitle                = {{Neural Networks: Tricks of the Trade (2nd ed.)}},
  Publisher                = {Springer},
  Year                     = {2012},
  Editor                   = {Gr{\'e}goire Montavon and Genevieve B. Orr and Klaus-Robert M{\"u}ller},
  Pages                    = {421--436},
  Series                   = {{Lecture Notes in Computer Science}},
  Volume                   = {7700},

  ISBN                     = {978-3-642-35288-1},
  Keywords                 = {dblp}
}

@Article{breiman2001random,
  Title                    = {{Random Forests}},
  Author                   = {Leo Breiman},
  Journal                  = {Machine Learning},
  Year                     = {2001},
  Pages                    = {5--32},
  Volume                   = {45},

  Added-at                 = {2012-10-21T11:54:04.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/2d1ad08473f94d4faeb5f01e37a851f25/nosebrain},
  Interhash                = {4450d2e56555e7cb8f3817578e1dd4da},
  Intrahash                = {d1ad08473f94d4faeb5f01e37a851f25},
  Keywords                 = {forests random},
  X-fetchedfrom            = {Bibsonomy}
}

@Article{breiman1996bagging,
  Title                    = {Bagging predictors},
  Author                   = {Breiman, Leo},
  Journal                  = {Machine learning},
  Year                     = {1996},
  Number                   = {2},
  Pages                    = {123--140},
  Volume                   = {24},

  Owner                    = {yani},
  Publisher                = {Springer},
  Timestamp                = {2016.10.28}
}

@Book{breiman84,
  Title                    = {{Classification and regression trees}},
  Author                   = {Leo Breiman and Jerome Friedman and Charles J Stone and Richard A Olshen},
  Publisher                = {CRC press},
  Year                     = {1984},

  Owner                    = {yani},
  Timestamp                = {2016.08.07}
}

@InProceedings{Ciresan2012,
  Title                    = {{Multi-column Deep Neural Networks for Image Classification}},
  Author                   = {Dan Claudiu Ciresan and Ueli Meier and J{\"u}rgen Schmidhuber},
  Booktitle                = {{arXiv:1202.2745v1 [cs.CV]}},
  Year                     = {2012},

  __markedentry            = {[yani:6]},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@InProceedings{Cogswell2016,
  Title                    = {{Reducing Overfitting in Deep Networks by Decorrelating Representations.}},
  Author                   = {Michael Cogswell and Faruk Ahmed and Ross B. Girshick and Larry Zitnick and Dhruv Batra},
  Booktitle                = {{International Conference on Learning Representations}},
  Year                     = {2016},

  __markedentry            = {[yani:6]},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@Article{journals/mcss/Cybenko92,
  Title                    = {{Approximation by superpositions of a sigmoidal function.}},
  Author                   = {George Cybenko},
  Journal                  = {MCSS},
  Year                     = {1992},
  Number                   = {4},
  Pages                    = {455},
  Volume                   = {5},

  Added-at                 = {2013-09-30T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/2d9af5e76699fe5b1c4152990919bd15a/dblp},
  Ee                       = {http://dx.doi.org/10.1007/BF02134016},
  Interhash                = {bfc986f38332331caee260e024268ad0},
  Intrahash                = {d9af5e76699fe5b1c4152990919bd15a},
  Keywords                 = {dblp},
  Url                      = {http://dblp.uni-trier.de/db/journals/mcss/mcss5.html#Cybenko92},
  X-fetchedfrom            = {Bibsonomy}
}

@Book{damelin2011,
  Title                    = {The Mathematics of Signal Processing:},
  Author                   = {Steven B. Damelin and Willard Miller, Jr},
  Publisher                = {Cambridge University Press},
  Year                     = {2011},

  Address                  = {Cambridge},
  Month                    = {12},

  Abstract                 = {Arising from courses taught by the authors, this largely self-contained treatment is ideal for mathematicians who are interested in applications or for students from applied fields who want to understand the mathematics behind their subject. Early chapters cover Fourier analysis, functional analysis, probability and linear algebra, all of which have been chosen to prepare the reader for the applications to come. The book includes rigorous proofs of core results in compressive sensing and wavelet convergence. Fundamental is the treatment of the linear system y=Φx in both finite and infinite dimensions. There are three possibilities: the system is determined, overdetermined or underdetermined, each with different aspects. The authors assume only basic familiarity with advanced calculus, linear algebra and matrix theory and modest familiarity with signal processing, so the book is accessible to students from the advanced undergraduate level. Many exercises are also included.},
  Day                      = {15},
  Doi                      = {10.1017/CBO9781139003896},
  ISBN                     = {9781139003896},
  Owner                    = {yani},
  Timestamp                = {2016.10.28},
  Url                      = {https://www.cambridge.org/core/books/the-mathematics-of-signal-processing/A3F20BC5FBB820E923E66C8CCB13B173}
}

@InProceedings{Deng2011fastbalanced,
  Title                    = {{Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition}},
  Author                   = {J. Deng and S. Satheesh and A. C. Berg and F.-F. Li},
  Booktitle                = {{Conference and Workshop on Neural Information Processing Systems}},
  Year                     = {2011}
}

@InProceedings{Denil2013predicting,
  Title                    = {{Predicting Parameters in Deep Learning}},
  Author                   = {M. Denil and B. Shakibi and L. Dinh and M. A. Ranzato and N deFreitas},
  Booktitle                = {{eprint ar{X}iv:1306.0543v2}},
  Year                     = {2013}
}

@InProceedings{Denton2014efficient,
  Title                    = {{Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation}},
  Author                   = {E. Denton and W. Zaremba and J. Bruna and Y. LeCun and R.Fergus},
  Booktitle                = {{eprint ar{X}iv:1404.0736v2}},
  Year                     = {2014}
}

@Misc{fodor2002survey,
  Title                    = {{A survey of dimension reduction techniques}},

  Author                   = {Imola K Fodor},
  Year                     = {2002},

  Publisher                = {Technical Report UCRL-ID-148494, Lawrence Livermore National Laboratory}
}

@Article{fukushima2013artificial,
  Title                    = {{Artificial vision by multi-layered neural networks: Neocognitron and its advances}},
  Author                   = {Kunihiko Fukushima},
  Journal                  = {Neural Networks},
  Year                     = {2013},
  Pages                    = {103--119},
  Volume                   = {37},

  Publisher                = {Elsevier}
}

@Article{Fuk80,
  Title                    = {{Neocognitron: A self-organizing neural network model for a mechanish of pattern recognition unaffected by shifts in position}},
  Author                   = {K. Fukushima},
  Journal                  = {Biological Cybernetics},
  Year                     = {1980},
  Pages                    = {193--202},
  Volume                   = {36},

  Added-at                 = {2013-12-07T19:21:28.000+0100},
  Biburl                   = {http://www.bibsonomy.org/bibtex/25b916ae8612805f9c0caaf04cf2abe46/prlz77},
  File                     = {#F\#},
  Interhash                = {70eb829b8f392bf15eb600c9c0dbab33},
  Intrahash                = {5b916ae8612805f9c0caaf04cf2abe46},
  Keywords                 = {deep fukushima learning neocognitron networks neural},
  X-fetchedfrom            = {Bibsonomy}
}

@InProceedings{girshick2015deformable,
  Title                    = {{Deformable Part Models are Convolutional Neural Networks}},
  Author                   = {Ross Girshick and Forrest Iandola and Trevor Darrell and Jitendra Malik},
  Booktitle                = {{Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}},
  Year                     = {2015},
  Pages                    = {437--446},

  __markedentry            = {[yani:]},
  Owner                    = {yani},
  Timestamp                = {2016.07.27}
}

@InProceedings{glorot2010understanding,
  Title                    = {{Understanding the difficulty of training deep feedforward neural networks}},
  Author                   = {Xavier Glorot and Yoshua Bengio},
  Booktitle                = {{International conference on artificial intelligence and statistics}},
  Year                     = {2010},
  Pages                    = {249--256},

  __markedentry            = {[yani:]},
  Owner                    = {yani},
  Timestamp                = {2016.07.27}
}

@InProceedings{goodfellow2013maxout,
  Title                    = {{Maxout Networks}},
  Author                   = {Ian Goodfellow and David Warde-farley and Mehdi Mirza and Aaron Courville and Yoshua Bengio},
  Booktitle                = {{Proceedings of the 30th International Conference on Machine Learning (ICML-13)}},
  Year                     = {2013},
  Pages                    = {1319--1327},

  __markedentry            = {[yani:]},
  Owner                    = {yani},
  Timestamp                = {2016.07.27}
}

@Unpublished{Goodfellow-et-al-2016-Book,
  Title                    = {Deep Learning},
  Author                   = {Ian Goodfellow and Yoshua Bengio and Aaron Courville},
  Note                     = {Book in preparation for MIT Press},
  Year                     = {2016},

  Owner                    = {yani},
  Timestamp                = {2016.10.27},
  Url                      = {http://www.deeplearningbook.org}
}

@InProceedings{Gupta2015,
  Title                    = {{Deep Learning with Limited Numerical Precision}},
  Author                   = {Suyog Gupta and Ankur Agrawal and Kailash Gopalakrishnan and Pritish Narayanan},
  Booktitle                = {{Proceedings of the 32nd International Conference on Machine Learning (ICML-15)}},
  Year                     = {2015},
  Editor                   = {David Blei and Francis Bach},
  Pages                    = {1737--1746},
  Publisher                = {JMLR Workshop and Conference Proceedings},

  __markedentry            = {[yani:6]},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@Misc{1502.02551v1,
  Title                    = {{Deep Learning with Limited Numerical Precision}},

  Author                   = {Suyog Gupta and Ankur Agrawal and Kailash Gopalakrishnan and Pritish Narayanan},
  Month                    = feb,
  Year                     = {2015},

  __markedentry            = {[yani:]},
  Abstract                 = {Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computation on neural network training. Within the context of low-precision fixed-point computations, we observe the rounding scheme to play a crucial role in determining the network's behavior during training. Our results show that deep networks can be trained using only 16-bit wide fixed-point number representation when using stochastic rounding, and incur little to no degradation in the classification accuracy. We also demonstrate an energy-efficient hardware accelerator that implements low-precision fixed-point arithmetic with stochastic rounding.},
  Archiveprefix            = {arXiv},
  Comment                  = {published = 2015-02-09T16:37:29Z, updated = 2015-02-09T16:37:29Z, 10 pages, 6 figures, 1 table},
  Eprint                   = {1502.02551v1},
  Owner                    = {yani},
  Primaryclass             = {cs.LG},
  Timestamp                = {2016.07.27},
  X-fetchedfrom            = {arXiv.org}
}

@Article{journals/iandc/HancockJLT96,
  Title                    = {{Lower Bounds on Learning Decision Lists and Trees}},
  Author                   = {Thomas R. Hancock and Tao Jiang and Ming Li and John Tromp},
  Journal                  = {Inf. Comput.},
  Year                     = {1996},
  Number                   = {2},
  Pages                    = {114--122},
  Volume                   = {126},

  Bibsource                = {dblp computer science bibliography, http://dblp.org},
  Biburl                   = {http://dblp.uni-trier.de/rec/bib/journals/iandc/HancockJLT96},
  Doi                      = {10.1006/inco.1996.0040},
  Owner                    = {yani},
  Timestamp                = {Thu, 31 Mar 2016 18:10:36 +0200},
  Url                      = {http://dx.doi.org/10.1006/inco.1996.0040}
}

@Article{Happel1994,
  Title                    = {{Design and evolution of modular neural network architectures.}},
  Author                   = {Bart L. M. Happel and Jacob M. J. Murre},
  Journal                  = {Neural Networks},
  Year                     = {1994},
  Number                   = {6-7},
  Pages                    = {985--1004},
  Volume                   = {7},

  __markedentry            = {[yani:6]},
  Date                     = {2005-12-15},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@InProceedings{he2015convolutional,
  Title                    = {{Convolutional Neural Networks at Constrained Time Cost}},
  Author                   = {Kaiming He and Jian Sun},
  Booktitle                = {{Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}},
  Year                     = {2015},
  Pages                    = {5353--5360},

  __markedentry            = {[yani:]},
  Owner                    = {yani},
  Timestamp                = {2016.07.27}
}

@Article{He2016,
  Title                    = {{Identity Mappings in Deep Residual Networks}},
  Author                   = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
  Year                     = {2016},
  Volume                   = {abs/1603.05027},

  __markedentry            = {[yani:6]},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@Article{He2015,
  Title                    = {{Deep Residual Learning for Image Recognition}},
  Author                   = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
  Journal                  = {arXiv preprint arXiv:1512.03385},
  Year                     = {2015},

  __markedentry            = {[yani:6]},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@Article{ieee7005506,
  Title                    = {{Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition}},
  Author                   = {K. He and X. Zhang and S. Ren and J. Sun},
  Journal                  = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
  Year                     = {2015},
  Number                   = {99},
  Pages                    = {1--1},
  Volume                   = {PP},

  Abstract                 = {Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224224) input image. This requirement is ``artificial'' and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, ``spatial pyramid pooling'', to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-theart classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102 faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank \#2 in object detection and \#3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition.},
  Arnumber                 = {7005506},
  Doi                      = {10.1109/TPAMI.2015.2389824},
  ISSN                     = {0162-8828},
  Keywords                 = {Accuracy; Agriculture; Convolutional codes; Feature extraction; Testing; Training; Vectors; Convolutional Neural Networks; Image Classification; Object Detection; Spatial Pyramid Pooling},
  X-fetchedfrom            = {IEEEXplore}
}

@Article{hinton2006reducing,
  Title                    = {Reducing the dimensionality of data with neural networks},
  Author                   = {Hinton, Geoffrey E and Salakhutdinov, Ruslan R},
  Journal                  = {Science},
  Year                     = {2006},
  Number                   = {5786},
  Pages                    = {504--507},
  Volume                   = {313},

  Owner                    = {yani},
  Publisher                = {American Association for the Advancement of Science},
  Timestamp                = {2016.10.26}
}

@Misc{Hinton2012,
  Title                    = {{Improving neural networks by preventing co-adaptation of feature detectors}},

  Author                   = {Geoffrey E. Hinton and Nitish Srivastava and Alex Krizhevsky and Ilya Sutskever and Ruslan R. Salakhutdinov},
  Month                    = jul,
  Year                     = {2012},

  __markedentry            = {[yani:6]},
  Archiveprefix            = {arXiv},
  Eprint                   = {1207.0580v1},
  Owner                    = {yani},
  Primaryclass             = {cs.NE},
  Timestamp                = {2016.07.31},
  X-fetchedfrom            = {arXiv.org}
}

@PhdThesis{hochreiter1991untersuchungen,
  Title                    = {Untersuchungen zu dynamischen neuronalen Netzen},
  Author                   = {Hochreiter, Sepp},
  School                   = {Technische Universit{\"a}t M{\"u}nchen},
  Year                     = {1991},

  Journal                  = {Diploma, Technische Universit{\"a}t M{\"u}nchen},
  Pages                    = {91}
}

@Misc{Hochreiter01gradientflow,
  Title                    = {{Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies}},

  Author                   = {Sepp Hochreiter and Yoshua Bengio and Paolo Frasconi and J{\"u}rgen Schmidhuber},
  Year                     = {2001},

  __markedentry            = {[yani:]},
  Owner                    = {yani},
  Timestamp                = {2016.07.27}
}

@Article{hornik89a,
  Title                    = {{Multilayer feedforward networks are universal approximators}},
  Author                   = {K. Hornik and M. Stinchcombe and H. White},
  Journal                  = {Neural Networks},
  Year                     = {1989},
  Pages                    = {356--366},
  Volume                   = {2},

  Abstract                 = {Thesis BIB},
  Added-at                 = {2009-10-27T06:49:28.000+0100},
  Biburl                   = {http://www.bibsonomy.org/bibtex/2895e5e2654209932aa72b1a31feb35b4/chrmina},
  Interhash                = {9948713640c726f50c4c5a57e121f7ac},
  Intrahash                = {895e5e2654209932aa72b1a31feb35b4},
  Keywords                 = {imported},
  X-fetchedfrom            = {Bibsonomy}
}

@Article{ioannou2016deep,
  Title                    = {{Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups}},
  Author                   = {Yani Ioannou and Duncan Robertson and Roberto Cipolla and Antonio Criminisi},
  Journal                  = {arXiv preprint arXiv:1605.06489},
  Year                     = {2016}
}

@TechReport{Ioannou2015,
  Title                    = {{Decision Forests, Convolutional Networks and the Models in-Between}},
  Author                   = {Yani Ioannou and Duncan Robertson and Darko Zikic and Peter Kontschieder and Jamie Shotton and Matthew Brown and Antonio Criminisi},
  Institution              = {Microsoft Research},
  Year                     = {2015},
  Month                    = apr,
  Number                   = {MSR-TR-2015-58},

  __markedentry            = {[yani:6]},
  Archiveprefix            = {arXiv},
  Journal                  = {Technical Report},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@InProceedings{Ioannou2016,
  Title                    = {{Training CNNs with Low-Rank Filters for Efficient Image Classification}},
  Author                   = {Yani Ioannou and Duncan P. Robertson and Jamie Shotton and Roberto Cipolla and Antonio Criminisi},
  Booktitle                = {{International Conference on Learning Representations}},
  Year                     = {2016},

  __markedentry            = {[yani:6]},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@InProceedings{Ioffe2015,
  Title                    = {{Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.}},
  Author                   = {Sergey Ioffe and Christian Szegedy},
  Booktitle                = {{Proceedings of the 32 nd International Conference on Machine Learning, Lille, France, 2015}},
  Year                     = {2015},

  __markedentry            = {[yani:6]},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@InProceedings{journals/corr/JaderbergVZ14,
  Title                    = {{Speeding up Convolutional Neural Networks with Low Rank Expansions.}},
  Author                   = {Max Jaderberg and Andrea Vedaldi and Andrew Zisserman},
  Booktitle                = {{British Machine Vision Conference}},
  Year                     = {2014},

  __markedentry            = {[yani:]},
  Journal                  = {British Machine Vision Conference},
  Keywords                 = {dblp},
  Owner                    = {yani},
  Timestamp                = {2016.07.27}
}

@Article{Jhurani2015,
  Title                    = {{A GEMM interface and implementation on NVIDIA GPUs for multiple small matrices}},
  Author                   = {Chetan Jhurani and Paul Mullowney},
  Journal                  = {Journal of Parallel and Distributed Computing},
  Year                     = {2015},
  Pages                    = {133--140},
  Volume                   = {75},

  __markedentry            = {[yani:6]},
  Owner                    = {yani},
  Publisher                = {Elsevier},
  Timestamp                = {2016.07.31}
}

@Article{Jia2014,
  Title                    = {{Caffe: Convolutional Architecture for Fast Feature Embedding}},
  Author                   = {Yangqing Jia and Evan Shelhamer and Jeff Donahue and Sergey Karayev and Jonathan Long and Ross Girshick and Sergio Guadarrama and Trevor Darrell},
  Journal                  = {arXiv preprint arXiv:1408.5093},
  Year                     = {2014},

  __markedentry            = {[yani:6]},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@Article{johnson1984extensions,
  Title                    = {{Extensions of Lipschhitz maps into a Hilbert space}},
  Author                   = {W Johnson and J Lindenstrauss},
  Journal                  = {Contemporary Math},
  Year                     = {1984},
  Volume                   = {26},

  Owner                    = {yani},
  Timestamp                = {2016.10.12}
}

@InProceedings{kaski1998dimensionality,
  Title                    = {{Dimensionality reduction by random mapping: Fast similarity computation for clustering}},
  Author                   = {Samuel Kaski},
  Booktitle                = {{Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on}},
  Year                     = {1998},
  Organization             = {IEEE},
  Pages                    = {413--418},
  Volume                   = {1}
}

@InProceedings{Kim2016,
  Title                    = {{Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications}},
  Author                   = {Yong{-}Deok Kim and Eunhyeok Park and Sungjoo Yoo and Taelim Choi and Lu Yang and Dongjun Shin},
  Booktitle                = {{International Conference on Learning Representations}},
  Year                     = {2016},

  __markedentry            = {[yani:6]},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@InProceedings{Krizhevsky2014,
  Title                    = {{One Weird Trick for Parallelizing Convolutional Neural Networks}},
  Author                   = {A. Krizhevsky},
  Booktitle                = {{eprint ar{X}iv:1404.5997v2}},
  Year                     = {2014}
}

@Article{Krizhevsky2014a,
  Title                    = {{One weird trick for parallelizing convolutional neural networks}},
  Author                   = {Alex Krizhevsky},
  Journal                  = {arXiv preprint arXiv:1404.5997},
  Year                     = {2014},

  __markedentry            = {[yani:6]},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@TechReport{CIFAR10,
  Title                    = {{Learning Multiple Layers of Features from Tiny Images}},
  Author                   = {A. Krizhevsky},
  Institution              = {Univ. Toronto},
  Year                     = {2009},
  Type                     = {Technical Report}
}

@InProceedings{Krizhevsky2012imanet,
  Title                    = {{Image{N}et Classification with Deep Convolutional Neural Networks}},
  Author                   = {A. Krizhevsky and I. Sutskever and G. Hinton},
  Year                     = {2012}
}

@Article{larsen2016optimality,
  Title                    = {{Optimality of the Johnson-Lindenstrauss Lemma}},
  Author                   = {Kasper Green Larsen and Jelani Nelson},
  Journal                  = {arXiv preprint arXiv:1609.02094},
  Year                     = {2016}
}

@InProceedings{Lebedev2015,
  Title                    = {{Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition.}},
  Author                   = {V. Lebedev and Y. Ganin and M. Rakhuba and I. Oseledets and V. Lempitsky},
  Booktitle                = {{International Conference on Learning Representations}},
  Year                     = {2015},

  __markedentry            = {[yani:6]},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@Article{journals/corr/LebedevGROL14,
  Title                    = {{Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition.}},
  Author                   = {Vadim Lebedev and Yaroslav Ganin and Maksim Rakhuba and Ivan V. Oseledets and Victor S. Lempitsky},
  Journal                  = {CoRR},
  Year                     = {2014},
  Volume                   = {abs/1412.6553},

  __markedentry            = {[yani:]},
  Added-at                 = {2015-01-01T00:00:00.000+0100},
  Ee                       = {http://arxiv.org/abs/1412.6553},
  Interhash                = {25af9f394cbece05c7cee601a306de7c},
  Intrahash                = {199e5821916b1eec932dde3ad74c8d97},
  Keywords                 = {dblp},
  Owner                    = {yani},
  Timestamp                = {2016.07.27},
  X-fetchedfrom            = {Bibsonomy}
}

@Article{Lecun1998,
  Title                    = {{Gradient-based learning applied to document recognition}},
  Author                   = {Y. Lecun and L. Bottou and Y. Bengio and P. Haffner},
  Journal                  = {Proceedings of the IEEE},
  Year                     = {1998},
  Number                   = {11},
  Pages                    = {2278--2324},
  Volume                   = {86},

  Abstract                 = {Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day},
  Added-at                 = {2009-09-12T19:19:34.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/24d0e761be0edc4f631b3af679c2b33ab/mozaher},
  File                     = {00726791.pdf:Lecun1998.pdf:PDF},
  Interhash                = {7a82cccacd23cf06b25ff5325a6c86c7},
  Intrahash                = {4d0e761be0edc4f631b3af679c2b33ab},
  ISSN                     = {0018-9219},
  Keywords                 = {2D GTN; back-propagation; backpropagation; based character cheque complex convolution; convolutional decision digit document extraction; field gradient gradient-based graph handwritten high-dimensional language learning learning; measure minimization; modeling; multilayer multimodule network networks; neural optical patterns; perceptrons; performance reading; recognition recognition; recognizers; segmentation shape surface synthesis; systems; task; technique; transformer variability},
  Owner                    = {mozaher},
  Timestamp                = {2007.05.15},
  X-fetchedfrom            = {Bibsonomy}
}

@InProceedings{lecun1989optimal,
  Title                    = {{Optimal brain damage.}},
  Author                   = {Yann LeCun and John S Denker and Sara A Solla and Richard E Howard and Lawrence D Jackel},
  Booktitle                = {{NIPs}},
  Year                     = {1989},
  Volume                   = {89},

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  Owner                    = {yani},
  Timestamp                = {2016.07.27}
}

@Article{lee2014deeply,
  Title                    = {{Deeply-supervised nets}},
  Author                   = {Chen-Yu Lee and Saining Xie and Patrick Gallagher and Zhengyou Zhang and Zhuowen Tu},
  Journal                  = {arXiv preprint arXiv:1409.5185},
  Year                     = {2014}
}

@InProceedings{Lin2014,
  Title                    = {{Network in network}},
  Author                   = {Min Lin and Qiang Chen and Shuicheng Yan},
  Booktitle                = {{International Conference on Learning Representations}},
  Year                     = {2014},
  Series                   = {{2014}},

  __markedentry            = {[yani:6]},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@Article{Lin2013NiN,
  Title                    = {{Network In Network.}},
  Author                   = {Min Lin and Qiang Chen and Shuicheng Yan},
  Journal                  = {CoRR},
  Year                     = {2013},
  Volume                   = {abs/1312.4400},

  Added-at                 = {2014-06-05T19:00:44.000+0200},
  Interhash                = {67da1ef3d0d91a16dbe3ec25474b9aaf},
  Intrahash                = {78171ed7a70a99e98b4e320a5b32b837},
  Keywords                 = {In Network},
  Url                      = {http://dblp.uni-trier.de/db/journals/corr/corr1312.html#LinCY13; http://arxiv.org/abs/1312.4400; http://www.bibsonomy.org/bibtex/278171ed7a70a99e98b4e320a5b32b837/prlz77},
  X-fetchedfrom            = {Bibsonomy}
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@InProceedings{Luo2010switchable,
  Title                    = {{Switchable Deep Networks for Pedestrian Detection}},
  Author                   = {P. Luo and Y. Tian and X. Wang and X. Tang},
  Booktitle                = {{Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition}},
  Year                     = {2010}
}

@InCollection{mamalet2012simplifying,
  Title                    = {{Simplifying convnets for fast learning}},
  Author                   = {Franck Mamalet and Christophe Garcia},
  Booktitle                = {{Artificial Neural Networks and Machine Learning--ICANN 2012}},
  Publisher                = {Springer},
  Year                     = {2012},
  Pages                    = {58--65},

  __markedentry            = {[yani:]},
  Owner                    = {yani},
  Timestamp                = {2016.07.27}
}

@InProceedings{martens2010deep,
  Title                    = {{Deep learning via Hessian-free optimization}},
  Author                   = {James Martens},
  Booktitle                = {{Proceedings of the 27th International Conference on Machine Learning (ICML-10)}},
  Year                     = {2010},
  Pages                    = {735--742}
}

@InProceedings{Mathieu2014,
  Title                    = {{Fast training of convolutional networks through {FFTs}}},
  Author                   = {Michael Mathieu and Mikael Henaff and Yann LeCun},
  Booktitle                = {{International Conference on Learning Representations}},
  Year                     = {2014},

  __markedentry            = {[yani:6]},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@Article{mathieu2013fast,
  Title                    = {{Fast training of convolutional networks through {FFTs}}},
  Author                   = {Michael Mathieu and Mikael Henaff and Yann LeCun},
  Journal                  = {arXiv preprint arXiv:1312.5851},
  Year                     = {2013},

  __markedentry            = {[yani:]},
  Owner                    = {yani},
  Timestamp                = {2016.07.27}
}

@Book{minsky1988perceptrons,
  Title                    = {{Perceptrons}},
  Author                   = {Marvin Minsky and Seymour Papert},
  Publisher                = {MIT press},
  Year                     = {1988}
}

@InProceedings{montillo2011entangled,
  Title                    = {{Entangled decision forests and their application for semantic segmentation of {CT} images}},
  Author                   = {A. Montillo and J. Shotton and J. Winn and J. Iglesias and D. Metaxas and A. Criminisi},
  Year                     = {2011}
}

@InProceedings{Oquab:2014:LTM:2679600.2680210,
  Title                    = {{Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks}},
  Author                   = {Maxime Oquab and Leon Bottou and Ivan Laptev and Josef Sivic},
  Booktitle                = {{Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition}},
  Year                     = {2014},

  Address                  = {Washington, DC, USA},
  Pages                    = {1717--1724},
  Publisher                = {IEEE Computer Society},
  Series                   = {{CVPR '14}},

  Acmid                    = {2680210},
  Doi                      = {10.1109/CVPR.2014.222},
  ISBN                     = {978-1-4799-5118-5},
  Numpages                 = {8},
  Url                      = {http://dx.doi.org/10.1109/CVPR.2014.222},
  X-fetchedfrom            = {ACM Digital Library}
}

@InProceedings{Wu2015scalingup,
  Title                    = {{Deep Image: Scaling up Image Recognition}},
  Author                   = {R.Wu and S.Yan and Y.Shan and Q.Dang and G.Sun},
  Booktitle                = {{eprint ar{X}iv:1501.02876v2}},
  Year                     = {2015}
}

@Article{ren2015noc,
  Title                    = {{Object Detection Networks on Convolutional Feature Maps}},
  Author                   = {Shaoqing Ren and Kaiming He and Ross Girshick and Xiangyu Zhang and Jian Sun},
  Journal                  = {arXiv preprint arXiv:1504.06066},
  Year                     = {2015},

  __markedentry            = {[yani:]},
  Owner                    = {yani},
  Timestamp                = {2016.07.27}
}

@InProceedings{Rippel2015,
  Title                    = {{Spectral Representations for Convolutional Neural Networks}},
  Author                   = {Oren Rippel and Jasper Snoek and Ryan P Adams},
  Booktitle                = {{Advances in Neural Information Processing Systems}},
  Year                     = {2015},
  Pages                    = {2440--2448},

  __markedentry            = {[yani:6]},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@Article{rippel2015spectral,
  Title                    = {{Spectral Representations for Convolutional Neural Networks}},
  Author                   = {Oren Rippel and Jasper Snoek and Ryan P Adams},
  Journal                  = {arXiv preprint arXiv:1506.03767},
  Year                     = {2015},

  __markedentry            = {[yani:]},
  Owner                    = {yani},
  Timestamp                = {2016.07.27}
}

@InProceedings{BuloKontsch2014,
  Title                    = {{Neural Decision Forests for Semantic Image Labelling}},
  Author                   = {S. {Rota Bul{\`o}} and P. Kontschieder},
  Booktitle                = {{Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition}},
  Year                     = {2014},
  Month                    = {June}
}

@Article{ILSVRC2015,
  Title                    = {{ImageNet Large Scale Visual Recognition Challenge}},
  Author                   = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
  Journal                  = {International Journal of Computer Vision (IJCV)},
  Year                     = {2015},

  Doi                      = {10.1007/s11263-015-0816-y}
}

@InProceedings{Sermanet2013overfeat,
  Title                    = {{OverFeat: Integrated Recognition, localization and Detection using Convolutional Networks}},
  Author                   = {P. Sermanet and D. Eigen and X. Zhang and M. Mathieu and R. Fergus and Y. LeCun},
  Booktitle                = {{eprint ar{X}iv:1312.6229}},
  Year                     = {2013}
}

@TechReport{Sethi1990,
  Title                    = {{Entropy nets: from decision trees to neural networks}},
  Author                   = {I. K. Sethi},
  Institution              = {Dept. of Computer Sci, Wayne State Univ.},
  Year                     = {1990},

  Location                 = {Detroit, MI}
}

@InProceedings{shankar2016refining,
  Title                    = {{Refining Architectures of Deep Convolutional Neural Networks}},
  Author                   = {Sukrit Shankar and Duncan Robertson and Yani Ioannou and Antonio Criminisi and Roberto Cipolla},
  Booktitle                = {{Conference on Computer Vision and Pattern Recognition (CVPR), 2016}},
  Year                     = {2016},

  Owner                    = {yani},
  Timestamp                = {2016.08.30}
}

@InProceedings{conf/cvpr/ShottonFCSFMKB11,
  Title                    = {{Real-time human pose recognition in parts from single depth images.}},
  Author                   = {Jamie Shotton and Andrew W. Fitzgibbon and Mat Cook and Toby Sharp and Mark Finocchio and Richard Moore and Alex Kipman and Andrew Blake 0001},
  Booktitle                = {{CVPR}},
  Year                     = {2011},
  Pages                    = {1297--1304},
  Publisher                = {IEEE Computer Society},

  Added-at                 = {2015-03-17T00:00:00.000+0100},
  Interhash                = {0c12d216c4aabdb0ed58c644e1e0bed2},
  Intrahash                = {ab5a4d0987288d0dc819144e5c77eff5},
  ISBN                     = {978-1-4577-0394-2},
  Keywords                 = {dblp},
  Url                      = {http://dblp.uni-trier.de/db/conf/cvpr/cvpr2011.html#ShottonFCSFMKB11; http://doi.ieeecomputersociety.org/10.1109/CVPR.2011.5995316; http://www.bibsonomy.org/bibtex/2ab5a4d0987288d0dc819144e5c77eff5/dblp},
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@InProceedings{Simonyan2014verydeep,
  Title                    = {{Very Deep Convolutional networks for Large-Scale Image Recognition}},
  Author                   = {K. Simonyan and A. Zisserman},
  Booktitle                = {{eprint ar{X}iv:arXiv:1409.1556v5}},
  Year                     = {2014}
}

@Article{journals/pami/SironiTRLF15,
  Title                    = {{Learning Separable Filters.}},
  Author                   = {Amos Sironi and Bugra Tekin and Roberto Rigamonti and Vincent Lepetit and Pascal Fua},
  Journal                  = {IEEE Trans. Pattern Anal. Mach. Intell.},
  Year                     = {2015},
  Number                   = {1},
  Pages                    = {94--106},
  Volume                   = {37},

  __markedentry            = {[yani:]},
  Added-at                 = {2014-12-11T00:00:00.000+0100},
  Ee                       = {http://dx.doi.org/10.1109/TPAMI.2014.2343229},
  Interhash                = {aaf08eb99237ab565195bc37e5078e2d},
  Intrahash                = {a5254aeb2b90e6fb2f83dfbe008c4302},
  Keywords                 = {dblp},
  Owner                    = {yani},
  Timestamp                = {2016.07.27},
  X-fetchedfrom            = {Bibsonomy}
}

@InProceedings{Snoek2012,
  Title                    = {{Practical Bayesian Optimization of Machine Learning Algorithms}},
  Author                   = {J. Snoek and H. Larochelle and R. P. Adams},
  Booktitle                = {{nips}},
  Year                     = {2012}
}

@Article{Srivastava:2014:DSW:2627435.2670313,
  Title                    = {{Dropout: A Simple Way to Prevent Neural Networks from Overfitting}},
  Author                   = {Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov},
  Journal                  = {J. Mach. Learn. Res.},
  Year                     = {2014},

  Month                    = jan,
  Number                   = {1},
  Pages                    = {1929--1958},
  Volume                   = {15},

  __markedentry            = {[yani:]},
  Acmid                    = {2670313},
  ISSN                     = {1532-4435},
  Issue_date               = {January 2014},
  Keywords                 = {deep learning; model combination; neural networks; regularization},
  Numpages                 = {30},
  Owner                    = {yani},
  Publisher                = {JMLR.org},
  Timestamp                = {2016.07.27},
  X-fetchedfrom            = {ACM Digital Library}
}

@InProceedings{Szegedy2014going,
  Title                    = {{Going Deeper with Convolutions}},
  Author                   = {C. Szegedy and W. Liu and Y. Jia and P. Sermanet and S. Reed and D. Anguelov and D. Erhan and V. Vanhoucke and A. Rabinovich},
  Booktitle                = {{eprint ar{X}iv:1409.4842}},
  Year                     = {2014}
}

@Article{szegedy2015rethinking,
  Title                    = {{Rethinking the Inception Architecture for Computer Vision}},
  Author                   = {Christian Szegedy and Vincent Vanhoucke and Sergey Ioffe and Jonathon Shlens and Zbigniew Wojna},
  Journal                  = {arXiv preprint arXiv:1512.00567},
  Year                     = {2015},

  __markedentry            = {[yani:]},
  Owner                    = {yani},
  Timestamp                = {2016.07.27}
}

@InProceedings{Deselaers2011visual,
  Title                    = {{Visual and Semantic Similarity in {I}mage{N}et}},
  Author                   = {V. Ferrari {T. Deselaers}},
  Booktitle                = {{Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition}},
  Year                     = {2011}
}

@InProceedings{Tompson_2015_CVPR,
  Title                    = {{Efficient Object Localization Using Convolutional Networks}},
  Author                   = {Jonathan Tompson and Ross Goroshin and Arjun Jain and Yann LeCun and Christoph Bregler},
  Year                     = {2015},
  Month                    = {June},

  __markedentry            = {[yani:]},
  Journal                  = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  Owner                    = {yani},
  Timestamp                = {2016.07.27}
}

@InProceedings{vanhoucke2011improving,
  Title                    = {{Improving the speed of neural networks on CPUs}},
  Author                   = {Vincent Vanhoucke and Andrew Senior and Mark Z Mao},
  Booktitle                = {{Proc. Deep Learning and Unsupervised Feature Learning NIPS Workshop}},
  Year                     = {2011},
  Volume                   = {1},

  __markedentry            = {[yani:]},
  Owner                    = {yani},
  Timestamp                = {2016.07.27}
}

@InProceedings{Welbl2014casting,
  Title                    = {{Casting Random Forests as Artificial Neural Networks (and Profiting from It)}},
  Author                   = {J. Welbl},
  Booktitle                = {{GCPR}},
  Year                     = {2014}
}

@InProceedings{xu2014deep,
  Title                    = {{Deep convolutional neural network for image deconvolution}},
  Author                   = {Li Xu and Jimmy S Ren and Ce Liu and Jiaya Jia},
  Booktitle                = {{Advances in Neural Information Processing Systems}},
  Year                     = {2014},
  Pages                    = {1790--1798},

  __markedentry            = {[yani:]},
  Owner                    = {yani},
  Timestamp                = {2016.07.27}
}

@InProceedings{yi2016lift,
  Title                    = {LIFT: Learned Invariant Feature Transform},
  Author                   = {Yi, Kwang Moo and Trulls, Eduard and Lepetit, Vincent and Fua, Pascal},
  Booktitle                = {European Conference on Computer Vision (ECCV), 2016},
  Year                     = {2016},

  Address                  = {Amsterdarm},

  Owner                    = {yani},
  Timestamp                = {2016.10.26}
}

@Book{yu2014book,
  Title                    = {{Automatic Speech Recognition: A Deep Learning Approach}},
  Author                   = {D. Yu and L. Deng},
  Publisher                = {Springer},
  Year                     = {2014}
}

@Misc{1311.2901v3,
  Title                    = {{Visualizing and Understanding Convolutional Networks}},

  Author                   = {Matthew D Zeiler and Rob Fergus},
  Month                    = nov,
  Year                     = {2013},

  __markedentry            = {[yani:]},
  Abstract                 = {Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark. However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we address both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. We also perform an ablation study to discover the performance contribution from different model layers. This enables us to find model architectures that outperform Krizhevsky \etal on the ImageNet classification benchmark. We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets.},
  Archiveprefix            = {arXiv},
  Comment                  = {published = 2013-11-12T20:02:22Z, updated = 2013-11-28T23:04:01Z},
  Eprint                   = {1311.2901v3},
  Owner                    = {yani},
  Primaryclass             = {cs.CV},
  Timestamp                = {2016.07.27},
  X-fetchedfrom            = {arXiv.org}
}

@InProceedings{Zeiler2010Deconv,
  Title                    = {{Deconvolutional Networks}},
  Author                   = {M. D. Zeiler and D. Krishnan and G. W. Taylor and R.Fergus},
  Booktitle                = {{Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition}},
  Year                     = {2010}
}

@Misc{1505.06798v1,
  Title                    = {{Accelerating Very Deep Convolutional Networks for Classification and Detection}},

  Author                   = {Xiangyu Zhang and Jianhua Zou and Kaiming He and Jian Sun},
  Month                    = may,
  Year                     = {2015},

  __markedentry            = {[yani:]},
  Abstract                 = {This paper aims to accelerate the test-time computation of convolutional neural networks (CNNs), especially very deep CNNs that have substantially impacted the computer vision community. Unlike existing methods that are designed for approximating linear filters or linear responses, our method takes the nonlinear units into account. We develop an effective solution to the resulting nonlinear optimization problem without the need of stochastic gradient descent (SGD). More importantly, while current methods mainly focus on optimizing one or two layers, our nonlinear method enables an asymmetric reconstruction that reduces the rapidly accumulated error when multiple (e.g., \>=10) layers are approximated. For the widely used very deep VGG-16 model, our method achieves a whole-model speedup of 4x with merely a 0.3\% increase of top-5 error in ImageNet classification. Our 4x accelerated VGG-16 model also shows a graceful accuracy degradation for object detection when plugged into the latest Fast R-CNN detector.},
  Archiveprefix            = {arXiv},
  Comment                  = {published = 2015-05-26T03:30:59Z, updated = 2015-05-26T03:30:59Z, Technical report. arXiv admin note: substantial text overlap with arXiv:1411.4229},
  Eprint                   = {1505.06798v1},
  Owner                    = {yani},
  Primaryclass             = {cs.CV},
  Timestamp                = {2016.07.27},
  X-fetchedfrom            = {arXiv.org}
}

@Misc{1411.4229v1,
  Title                    = {{Efficient and Accurate Approximations of Nonlinear Convolutional Networks}},

  Author                   = {Xiangyu Zhang and Jianhua Zou and Xiang Ming and Kaiming He and Jian Sun},
  Month                    = nov,
  Year                     = {2014},

  __markedentry            = {[yani:]},
  Abstract                 = {This paper aims to accelerate the test-time computation of deep convolutional neural networks (CNNs). Unlike existing methods that are designed for approximating linear filters or linear responses, our method takes the nonlinear units into account. We minimize the reconstruction error of the nonlinear responses, subject to a low-rank constraint which helps to reduce the complexity of filters. We develop an effective solution to this constrained nonlinear optimization problem. An algorithm is also presented for reducing the accumulated error when multiple layers are approximated. A whole-model speedup ratio of 4x is demonstrated on a large network trained for ImageNet, while the top-5 error rate is only increased by 0.9\%. Our accelerated model has a comparably fast speed as the "AlexNet", but is 4.7\% more accurate.},
  Archiveprefix            = {arXiv},
  Comment                  = {published = 2014-11-16T08:37:25Z, updated = 2014-11-16T08:37:25Z},
  Eprint                   = {1411.4229v1},
  Owner                    = {yani},
  Primaryclass             = {cs.CV},
  Timestamp                = {2016.07.27},
  X-fetchedfrom            = {arXiv.org}
}

@InProceedings{Zhang2015efficient,
  Title                    = {{Efficient and Accurate Approximations of Nonlinear Convolutional Networks}},
  Author                   = {X. Zhang and J. Zou and X. Ming and K. He and J. Sun},
  Booktitle                = {{eprint ar{X}iv:1411.4229v1}},
  Year                     = {2014}
}

@InProceedings{zhou2014learning,
  Title                    = {Learning deep features for scene recognition using places database},
  Author                   = {Zhou, Bolei and Lapedriza, Agata and Xiao, Jianxiong and Torralba, Antonio and Oliva, Aude},
  Booktitle                = {Advances in neural information processing systems},
  Year                     = {2014},
  Pages                    = {487--495},

  Owner                    = {yani},
  Timestamp                = {2016.10.27}
}

@InProceedings{segmentation-of-brain-tumor-tissues-with-convolutional-neural-networks,
  Title                    = {{Segmentation of Brain Tumor Tissues with Convolutional Neural Networks}},
  Author                   = {Darko Zikic and Yani Ioannou and Matthew Brown and Antonio Criminisi},
  Booktitle                = {{MICCAI workshop on Multimodal Brain Tumor Segmentation Challenge (BRATS)}},
  Year                     = {2014},
  Month                    = {October},
  Publisher                = {Springer}
}

@Proceedings{Bach2015,
  Title                    = {{Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015}},
  Year                     = {2015},
  Editor                   = {Francis R. Bach and David M. Blei},
  Volume                   = {37},

  __markedentry            = {[yani:6]},
  Booktitle                = {{ICML}},
  Owner                    = {yani},
  Timestamp                = {2016.07.31}
}

@Proceedings{conf/icml/2015,
  Title                    = {Proceedings of the 32nd International Conference on Machine Learning,
 {ICML} 2015, Lille, France, 6-11 July 2015},
  Year                     = {2015},
  Editor                   = {Francis R. Bach and
 David M. Blei},
  Publisher                = {JMLR.org},
  Series                   = {{JMLR} Workshop and Conference Proceedings},
  Volume                   = {37},

  Bibsource                = {dblp computer science bibliography, http://dblp.org},
  Biburl                   = {http://dblp.uni-trier.de/rec/bib/conf/icml/2015},
  Owner                    = {yani},
  Timestamp                = {Tue, 12 Jul 2016 21:51:15 +0200},
  Url                      = {http://jmlr.org/proceedings/papers/v37/}
}

@Proceedings{conf/icml/2010,
  Title                    = {{Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel}},
  Year                     = {2010},
  Editor                   = {Johannes F{\"u}rnkranz and Thorsten Joachims},
  Publisher                = {Omnipress},

  Added-at                 = {2015-06-12T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/233816134b2d4cfe56614bed998e59e0f/dblp},
  Booktitle                = {{ICML}},
  Interhash                = {0b8f41e46562dc8b98dddb84d6a4adbe},
  Intrahash                = {33816134b2d4cfe56614bed998e59e0f},
  Keywords                 = {dblp},
  Url                      = {http://dblp.uni-trier.de/db/conf/icml/icml2010.html},
  X-fetchedfrom            = {Bibsonomy}
}

@Proceedings{conf/iccv/2015,
  Title                    = {2015 {IEEE} International Conference on Computer Vision, {ICCV} 2015,
 Santiago, Chile, December 7-13, 2015},
  Year                     = {2015},
  Publisher                = {{IEEE} Computer Society},

  Biburl                   = {http://dblp.uni-trier.de/rec/bib/conf/iccv/2015},
  ISBN                     = {978-1-4673-8391-2},
  Url                      = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7407725}
}

@Proceedings{conf/cvpr/2013,
  Title                    = {{2013 {IEEE} Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, June 23-28, 2013}},
  Year                     = {2013},
  Publisher                = {{IEEE}},

  __markedentry            = {[yani:]},
  Bibsource                = {dblp computer science bibliography, http://dblp.org},
  Owner                    = {yani},
  Timestamp                = {Thu, 17 Oct 2013 19:11:58 +0200}
}

@Proceedings{conf/icml/2013,
  Title                    = {Proceedings of the 30th International Conference on Machine Learning,
 {ICML} 2013, Atlanta, GA, USA, 16-21 June 2013},
  Year                     = {2013},
  Publisher                = {JMLR.org},
  Series                   = {{JMLR} Workshop and Conference Proceedings},
  Volume                   = {28},

  Bibsource                = {dblp computer science bibliography, http://dblp.org},
  Biburl                   = {http://dblp.uni-trier.de/rec/bib/conf/icml/2013},
  Owner                    = {yani},
  Timestamp                = {Tue, 12 Jul 2016 21:51:14 +0200},
  Url                      = {http://jmlr.org/proceedings/papers/v28/}
}

repository: SaqibMamoon/phd-thesis

repo_name: SaqibMamoon/phd-thesis


text:
@ARTICLE{Szegedy2013,
       author = {{Szegedy}, Christian and {Zaremba}, Wojciech and {Sutskever}, Ilya and
         {Bruna}, Joan and {Erhan}, Dumitru and {Goodfellow}, Ian and
         {Fergus}, Rob},
        title = "{Intriguing properties of neural networks}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing},
         year = "2013",
        month = "Dec",
          eid = {arXiv:1312.6199},
        pages = {arXiv:1312.6199},
archivePrefix = {arXiv},
       eprint = {1312.6199},
 primaryClass = {cs.CV},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2013arXiv1312.6199S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System},
			url			=	{https://arxiv.org/pdf/1312.6199v1.pdf}
},

@ARTICLE{He2018,
    title={Decision Boundary Analysts of Adversarial Examples},
    author={Warren, He and Bo, Li and Dawn, Song},
    year={2018},
		journal={ICLR 2018},
    url={https://pdfs.semanticscholar.org/08c5/88465b7d801ad912ef3e9107fa511ea0e403.pdf}
},

@ARTICLE{Chattopadhyay2017,
       author = {{Chattopadhyay}, Aditya and {Sarkar}, Anirban and {Howlader}, Prantik and {Balasubramanian}, Vineeth N},
        title = "{Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computer Vision and Pattern Recognition},
         year = "2017",
        month = "Oct",
          eid = {arXiv:1710.11063},
        pages = {arXiv:1710.11063},
archivePrefix = {arXiv},
       eprint = {1710.11063},
 primaryClass = {cs.CV},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2017arXiv171011063C},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System},
			url={https://arxiv.org/abs/1710.11063}
},

@ARTICLE{Goodfellow2015,
       author = {{Goodfellow}, Ian J. and {Shlens}, Jonathon and {Szegedy}, Christian},
        title = "{Explaining and Harnessing Adversarial Examples}",
      journal = {ICLR 2015},
     keywords = {Statistics - Machine Learning, Computer Science - Machine Learning},
					 url={https://arxiv.org/pdf/1412.6572.pdf}
},

@ARTICLE{Kurakin2016,
       author = {{Kurakin}, Alexey and {Goodfellow}, Ian and {Bengio}, Samy},
        title = "{Adversarial examples in the physical world}",
      journal = {ICLR 2017},
     keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Cryptography and Security, Computer Science - Machine Learning, Statistics - Machine Learning},
         year = "2017",
			url={https://arxiv.org/abs/1607.02533}
},

@ARTICLE{Jiawei2017,
       author = {{Su}, Jiawei and {Vasconcellos Vargas}, Danilo and {Kouichi}, Sakurai},
        title = "{One pixel attack for fooling deep neural networks}",
      journal = {IEEE Transactions on Evolutionary Computation},
     keywords = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning},
         year = "2019",
					url = {https://arxiv.org/abs/1710.08864}
},

@ARTICLE{Ross2017,
       author = {{Slavin Ross}, Andrew and {Doshi-Velez}, Finale},
        title = "{Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients}",
      journal = {AAAI-18},
         year = "2018",
					url={https://arxiv.org/abs/1711.09404}
},

@ARTICLE{Blumer1987,
	title={Occam's Razor},
	author={Anselm, Blumer and Andrzej, Ehrenfeucht and David, Haussler and Manfred K., Warmuth},
	year={1987},
	journal={Information Processing Letters},
	volume={24},
	pages={377-380},
	url={https://users.soe.ucsc.edu/~manfred/pubs/J9.pdf}
},

@misc{Idahl2018,
	title={gtsrb-models-keras},
	author={Maximilian, Idhal},
	year={2018},
	url={https://github.com/MaximilianIdahl/gtsrb-models-keras}
}

repository: plassma/GTSRB-adversarial-training

license_type: permissive

repo_name: plassma/GTSRB-adversarial-training

branch_name: refs/heads/master

extension: bib


text:
require webkitgtk.inc

DEPENDS += " libgcrypt"

SRC_URI = "\
   https://www.webkitgtk.org/releases/webkitgtk-${PV}.tar.xz \
"

SRC_URI[md5sum] = "efb69a0cc3cc67ef2647efec22e44c69"
SRC_URI[sha256sum] = "579b307d78fa16a73f112790178022166d7ad17d4147492ced9b9fba48f20dd8"

PACKAGECONFIG ?= " ${@bb.utils.contains('DISTRO_FEATURES', 'x11', 'x11', '', d)} \
                   ${@bb.utils.contains('DISTRO_FEATURES', 'wayland', 'wayland', '', d)} \
                   ${@bb.utils.contains('DISTRO_FEATURES', 'opengl', 'opengl gles2 gst_gl webgl', '', d)} \
                   video \
                   webcrypto \
                   woff2 "
PACKAGECONFIG[webcrypto] = "-DENABLE_WEB_CRYPTO=ON,-DENABLE_WEB_CRYPTO=OFF,libgcrypt libtasn1"
PACKAGECONFIG[gst_gl] = "-DUSE_GSTREAMER_GL=ON,-DUSE_GSTREAMER_GL=OFF,gstreamer1.0-plugins-bad"
PACKAGECONFIG[woff2] = "-DUSE_WOFF2=ON,-DUSE_WOFF2=OFF,woff2"

license: MIT

repository: kopera/meta-webkit

repo_name: kopera/meta-webkit

gha_license_id: MIT

extension: bb


text:
p{
	border: 2px solid blue;
	width:40%;
	padding:70px;
	margin:2px;
}

extension: css


text:
@book{地质学基础,
    title={地质学基础（第四版）},
    author={宋青春 and 邱维理 and 张振春},
    year={2005},
    pages={108},
    publisher={高等教育出版社},
}
@article{参数估计,
  title={Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions},
  author={Tang, Biao and Wang, Xia and Li, Qian and Bragazzi, Nicola Luigi and Tang, Sanyi and Xiao, Yanni and Wu, Jianhong},
  journal={Journal of clinical medicine},
  volume={9},
  number={2},
  pages={462},
  year={2020},
  publisher={Multidisciplinary Digital Publishing Institute}
}

repository: Plutoset/fieldpractice-geo

repo_name: Plutoset/fieldpractice-geo


text:
@article{Reynolds1987,
 key = {Reynolds1987},
 author = {Reynolds C. W.},
 title = {Flocks, Herds, and Schools: A Distributed Behavioral Model},
 journal = {SIGGRAPH '87 Conference Proceedings},
 year = 1987,
 pages = {25-34},
 url = {https://www.red3d.com/cwr/papers/1987/boids.html},
 publisher = {ACM},
 keywords = {flock, herd, school, bird, fish, aggregate motion, particle system, actor, flight, behavioral animation, constraints, path planning},
}

@article{BAJEC2009777,
title = {Organized flight in birds},
journal = {Animal Behaviour},
volume = {78},
number = {4},
pages = {777-789},
year = {2009},
issn = {0003-3472},
doi = {https://doi.org/10.1016/j.anbehav.2009.07.007},
url = {https://www.sciencedirect.com/science/article/pii/S0003347209002966},
author = {Iztok Lebar Bajec and Frank H. Heppner},
keywords = {animat, bird flock, boid, cluster formation, Canada goose, European starling, flight formation, flock simulation, line formation, V formation},
abstract = {The organized flight of birds is one of the most easily observed, yet challenging to study, phenomena in biology. Birds that fly in organized groups generally do so in one of two fashions: Line formations and Cluster formations. The former groups are typical of large birds such as waterfowl, where birds fly arranged in single lines, often joined together. The scientific questions about these groups usually involve potential adaptive functions, such as why geese fly in a V. Cluster formations are typically made up of large numbers of smaller birds such as pigeons or starlings flying in more irregular arrangements that have a strong three-dimensional character. The groups are defined by synchronized and apparently simultaneous rapid changes in direction. Scientific questions about these groups are usually concerned with mechanism such as how synchrony is achieved. Although field observations about the phenomenon date to the origins of natural history, experimental studies did not begin until the 1970s. Early experimenters and theoreticians were primarily biologists, but more recently aeronautical engineers, mathematicians, computer scientists and, currently, physicists have been attracted to the study of organized flight. Computer modelling has recently generated striking visual representations of organized flight and a number of hypotheses about its functions and mechanisms, but the ability to test these hypotheses lags behind the capacity to generate them. We suggest that a multi disciplinary approach to the phenomenon will be necessary to resolve apparently conflicting current hypotheses.}
}

@inproceedings{Davis2003MultipleAS,
  title={Multiple Agent Simulation System in a Virtual Environment},
  author={G. Davis},
  year={2003}
}

@article{YUAN20114210,
title = {A model for simulation of crowd behaviour in the evacuation from a smoke-filled compartment},
journal = {Physica A: Statistical Mechanics and its Applications},
volume = {390},
number = {23},
pages = {4210-4218},
year = {2011},
issn = {0378-4371},
doi = {https://doi.org/10.1016/j.physa.2011.07.044},
url = {https://www.sciencedirect.com/science/article/pii/S0378437111005930},
author = {Weifeng Yuan and Kang Hai Tan},
keywords = {Evacuation, Cellular automaton, Visibility range, Flow with the stream},
abstract = {The modelling of crowd evacuation from a building has been studied over the past decades. In this study, a numerical model based on cellular automaton is proposed to simulate the human behaviour termed “flow with the stream” in emergency evacuation from a large smoke-filled compartment. In the model, the smoke effect in the context of visibility is considered since visibility range can affect the human behaviour significantly. To simulate the reality that the smoke concentration in a fire compartment is not constant, the proposed model is developed to deal with the scenario in which the visibility range varies in the course of time. An empirical formula is incorporated into the proposed model to estimate the visibility range. The results of numerical tests show that the proposed model can also be used to investigate the effect of the number of guiders through case study.}
}

@article{Zhou2010,
author = {Zhou, Suiping and Chen, Dan and Cai, Wentong and Luo, Linbo and Low, Malcolm Yoke Hean and Tian, Feng and Tay, Victor Su-Han and Ong, Darren Wee Sze and Hamilton, Benjamin D.},
title = {Crowd Modeling and Simulation Technologies},
year = {2010},
issue_date = {October 2010},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {20},
number = {4},
issn = {1049-3301},
url = {https://doi.org/10.1145/1842722.1842725},
doi = {10.1145/1842722.1842725},
abstract = {As a collective and highly dynamic social group, the human crowd is a fascinating phenomenon that has been frequently studied by experts from various areas. Recently, computer-based modeling and simulation technologies have emerged to support investigation of the dynamics of crowds, such as a crowd's behaviors under normal and emergent situations. This article assesses the major existing technologies for crowd modeling and simulation. We first propose a two-dimensional categorization mechanism to classify existing work depending on the size of crowds and the time-scale of the crowd phenomena of interest. Four evaluation criteria have also been introduced to evaluate existing crowd simulation systems from the point of view of both a modeler and an end-user.We have discussed some influential existing work in crowd modeling and simulation regarding their major features, performance as well as the technologies used in this work. We have also discussed some open problems in the area. This article will provide the researchers with useful information and insights on the state of the art of the technologies in crowd modeling and simulation as well as future research directions.},
journal = {ACM Trans. Model. Comput. Simul.},
month = nov,
articleno = {20},
numpages = {35},
keywords = {multi-agent system, crowd dynamics, Crowd simulation, human behavior}
}

@online{StanleyStellaIMDb,
  key = {StanleyStellaIMDb},
  year  =        1987,
  title =        "Stanley and Stella in 'Breaking the Ice' IMDb page",
  url =          "https://www.imdb.com/title/tt0302371/",
  lastaccessed = "Feb 7, 2021",
}

@online{Half-LifeWiki,
  key = {Half-LifeWiki},
  year  =        2010,
  title =        "The Half-Life \& Portal Encyclopedia - Boids",
  url =          "https://half-life.fandom.com/wiki/Boid",
  lastaccessed = "Feb 13, 2021",
}

@online{wovenCity,
  key = {wovencity},
  year  =        2020,
  title =        "Toyota to Build Prototype City of the Future",
  url =          "https://pressroom.toyota.com/toyota-to-build-prototype-city-of-the-future/",
  lastaccessed = "Feb 15, 2021",
}

@online{urbanPlanning,
  key = {urbanPlanning},
  year  =        2020,
  title =        "School of Urban Planning - About urban planning",
  url =          "https://www.mcgill.ca/urbanplanning/planning",
  lastaccessed = "Feb 16, 2021",
}

@online{crowdControl,
  key = {crowdControl},
  year  =        2020,
  title =        "VSTEP Crowd Control Traine",
  url =          "https://www.mdai.de/wp-content/uploads/2015/01/Crowd-Control-Trainer-Brochure_v2.1_online1.pdf",
  lastaccessed = "Feb 17, 2021",
}

@online{nvidiaAI,
  key = {nvideAI},
  year  =        2020,
  title =        "NVIDIA Tensor Cores - Unprecedented Acceleration for HPC and AI",
  url =          "https://www.nvidia.com/en-us/data-center/tensor-cores/",
  lastaccessed = "Feb 17, 2021",
}

@online{amdAI,
  key = {amdAI},
  year  =        2020,
  title =        "AMD CDNA -Compute DNA for the Data Center",
  url =          "https://www.amd.com/en/technologies/cdna",
  lastaccessed = "Feb 17, 2021",
}

@MISC{StanleyStella1987,
  key = {StanleyStella1987},
  title =   {Stanley and Stella in 'Breaking the Ice'},
  producer = {Michael Wahrman},
  author = {Larry Malone},
  url = {https://www.youtube.com/watch?v=3bTqWsVqyzE},
  year = {1987},
  publisher = {United States: Symbolics}
}

@MISC{Terzopoulos94artificialfishes:,
    author = {Demetri Terzopoulos and Xiaoyuan Tu and Radek Grzeszczuk},
    title = {Artificial Fishes: Autonomous Locomotion, Perception, Behavior, and Learning in a Simulated Physical World},
    year = {1994}
}

@article{Evacuation,
  author    = {Jo{\~{a}}o Em{\'{\i}}lio Almeida and
               Rosaldo J. F. Rossetti and
               Ant{\'{o}}nio Le{\c{c}}a Coelho},
  title     = {Crowd Simulation Modeling Applied to Emergency and Evacuation Simulations
               using Multi-Agent Systems},
  journal   = {CoRR},
  volume    = {abs/1303.4692},
  year      = {2013},
  url       = {http://arxiv.org/abs/1303.4692},
  archivePrefix = {arXiv},
  eprint    = {1303.4692},
  timestamp = {Mon, 13 Aug 2018 16:46:03 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1303-4692.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@INPROCEEDINGS{evacuationRobots,
  author={P. {Robinette} and A. M. {Howard}},
  booktitle={2011 RO-MAN},
  title={Incorporating a model of human panic behavior for robotic-based emergency evacuation},
  year={2011},
  volume={},
  number={},
  pages={47-52},
  doi={10.1109/ROMAN.2011.6005265}}

@article{SIDIROPOULOS2020100009,
title = {Crowd simulation for crisis management: The outcomes of the last decade},
journal = {Machine Learning with Applications},
volume = {2},
pages = {100009},
year = {2020},
issn = {2666-8270},
doi = {https://doi.org/10.1016/j.mlwa.2020.100009},
url = {https://www.sciencedirect.com/science/article/pii/S2666827020300098},
author = {George Sidiropoulos and Chairi Kiourt and Lefteris Moussiades},
keywords = {Crowd simulation, Crisis management, Multi-agent systems, Machine learning},
abstract = {The last decades, crowd simulation for crisis management is highlighted as an important topic of interest for many scientific fields. As the continuous evolution of computational resources increases, along with the capabilities of Artificial Intelligence, the demand for better and more realistic simulation has become more attractive and popular to scientists. Along those years, there have been published hundreds of research articles and numerous different systems have been created that aim to simulate crowd behaviors, crisis cases and emergency evacuation scenarios. For better outcomes, recent research has focused on the separation of the problem of crisis management, to multiple research sub-fields (categories), such as the navigation of the simulated pedestrians, their psychology, group dynamics etc. There have been extended research works suggesting new methods and techniques for those categories of problems. In this paper, we propose three main research categories, each one consisting of several sub-categories, relying on crowd simulation for crisis management aspects and we present the outcomes of the last decade, focusing mostly on works exploiting multi-agent technologies. We analyze a number of technologies, methodologies, techniques, tools and systems introduced throughout the last years. A comparative review and discussion of the proposed categories is presented toward the identification of the most efficient aspects of the proposed categories. A general framework toward the future crowd simulation for crisis management is presented to yield the most realistic outcomes. The paper is concluded with some highlights and open questions for future directions.}
}

@article{drones,
author = {Xu, Ming-Liang and Jiang, Hao and Jin, Xiaogang},
year = {2014},
month = {09},
pages = {799-811},
title = {Crowd Simulation and Its Applications: Recent Advances},
volume = {29},
journal = {Journal of Computer Science and Technology},
doi = {10.1007/s11390-014-1469-y}
}

@misc{2014outdoor,
      title={Outdoor flocking and formation flight with autonomous aerial robots},
      author={Gábor Vásárhelyi and Csaba Virágh and Gergő Somorjai and Norbert Tarcai and Tamás Szörényi and Tamás Nepusz and Tamás Vicsek},
      year={2014},
      eprint={1402.3588},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
}

repository: JoseFilipeFerreira/Boids

repo_name: JoseFilipeFerreira/Boids