id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
465933d948c88afcf144da6787f6300aca815629b6666f4fcb097ba84743e14c | [
"arxiv",
"semantic_scholar"
] | Lifelong Wandering: A realistic few-shot online continual learning setting | Online few-shot learning describes a setting where models are trained and evaluated on a stream of data while learning emerging classes. While prior work in this setting has achieved very promising performance on instance classification when learning from data-streams composed of a single indoor environment, we propose... | [
"Mayank Lunayach",
"James Smith",
"Zsolt Kira"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2022-06-16T00:00:00 | https://arxiv.org/abs/2206.07932 | https://arxiv.org/pdf/2206.07932v1 | 2206.07932 | 10.48550/arXiv.2206.07932 | 3 | 0 | false | null | arXiv.org | 0.1505 |
5103ca9b57e06127ce91a3dd2fe68c8a14c8076681546a69e86182e49f23d396 | [
"arxiv",
"semantic_scholar"
] | Few-Shot Learning by Dimensionality Reduction in Gradient Space | We introduce SubGD, a novel few-shot learning method which is based on the recent finding that stochastic gradient descent updates tend to live in a low-dimensional parameter subspace. In experimental and theoretical analyses, we show that models confined to a suitable predefined subspace generalize well for few-shot l... | [
"Martin Gauch",
"Maximilian Beck",
"Thomas Adler",
"Dmytro Kotsur",
"Stefan Fiel",
"Hamid Eghbal-zadeh",
"Johannes Brandstetter",
"Johannes Kofler",
"Markus Holzleitner",
"Werner Zellinger",
"Daniel Klotz",
"Sepp Hochreiter",
"Sebastian Lehner"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2022-06-07T00:00:00 | https://arxiv.org/abs/2206.03483 | https://arxiv.org/pdf/2206.03483v1 | 2206.03483 | 10.48550/arXiv.2206.03483 | 11 | 0 | true | https://github.com/ml-jku/subgd | Proceedings of The 1st Conference on Lifelong Learning Agents, PMLR 199:1043-1064 (2022) | 0.2698 |
1693803ddf3539c934dfdbf489be968a9eac8cf4e8e7d8b321bfcefbe3068c2e | [
"arxiv",
"semantic_scholar"
] | Discretization Invariant Networks for Learning Maps between Neural Fields | With the emergence of powerful representations of continuous data in the form of neural fields, there is a need for discretization invariant learning: an approach for learning maps between functions on continuous domains without being sensitive to how the function is sampled. We present a new framework for understandin... | [
"Clinton J. Wang",
"Polina Golland"
] | [
"cs.LG",
"cs.CV",
"cs.NE"
] | [
"Computer Science"
] | 2022-06-02T00:00:00 | https://arxiv.org/abs/2206.01178 | https://arxiv.org/pdf/2206.01178v4 | 2206.01178 | null | 1 | 0 | true | https://github.com/clintonjwang/DI-net | null | 0.0753 |
3f844961f61eb5884e96708f8bdea24cecc0005da458b619075b9da2b9cb4475 | [
"arxiv",
"semantic_scholar"
] | Feature Forgetting in Continual Representation Learning | In continual and lifelong learning, good representation learning can help increase performance and reduce sample complexity when learning new tasks. There is evidence that representations do not suffer from "catastrophic forgetting" even in plain continual learning, but little further fact is known about its characteri... | [
"Xiao Zhang",
"Dejing Dou",
"Ji Wu"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2022-05-26T00:00:00 | https://arxiv.org/abs/2205.13359 | https://arxiv.org/pdf/2205.13359v1 | 2205.13359 | 10.48550/arXiv.2205.13359 | 8 | 1 | false | null | arXiv.org | 0.2386 |
a03a2d55c1d3d493605b906d00846c8b1773d0489bda6d34c01ef0471a61f9fb | [
"arxiv",
"semantic_scholar"
] | Acute Lymphoblastic Leukemia Detection Using Hypercomplex-Valued Convolutional Neural Networks | This paper features convolutional neural networks defined on hypercomplex algebras applied to classify lymphocytes in blood smear digital microscopic images. Such classification is helpful for the diagnosis of acute lymphoblast leukemia (ALL), a type of blood cancer. We perform the classification task using eight hyper... | [
"Guilherme Vieira",
"Marcos Eduardo Valle"
] | [
"cs.CV",
"cs.LG",
"cs.NE",
"eess.IV"
] | [
"Computer Science",
"Engineering"
] | 2022-05-26T00:00:00 | https://arxiv.org/abs/2205.13273 | https://arxiv.org/pdf/2205.13273v1 | 2205.13273 | 10.1109/IJCNN55064.2022.9892036 | 26 | 5 | false | null | IEEE International Joint Conference on Neural Network | 0.3891 |
349c5ca89598f57ffbbdad0e0bc913d001d9c9bc5d8018ef446cfc28ca8e55ad | [
"arxiv",
"semantic_scholar"
] | Explanatory machine learning for sequential human teaching | The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive Logic Programming (ILP) uses logic programming to derive logic theories from small data based on abduction and induction techniques. Learned theories are represented in the form of rules as declarative descript... | [
"Lun Ai",
"Johannes Langer",
"Stephen H. Muggleton",
"Ute Schmid"
] | [
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2022-05-20T00:00:00 | https://arxiv.org/abs/2205.10250 | https://arxiv.org/pdf/2205.10250v2 | 2205.10250 | 10.1007/s10994-023-06351-8 | 8 | 0 | false | null | Machine-mediated learning | 0.2386 |
85c531ddee786e57de0f8e67051354ef27f1d5721cad6230f39cc0d1abab7aff | [
"arxiv",
"semantic_scholar"
] | Transfer Learning with Pre-trained Conditional Generative Models | Transfer learning is crucial in training deep neural networks on new target tasks. Current transfer learning methods always assume at least one of (i) source and target task label spaces overlap, (ii) source datasets are available, and (iii) target network architectures are consistent with source ones. However, holding... | [
"Shin'ya Yamaguchi",
"Sekitoshi Kanai",
"Atsutoshi Kumagai",
"Daiki Chijiwa",
"Hisashi Kashima"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2022-04-27T00:00:00 | https://arxiv.org/abs/2204.12833 | https://arxiv.org/pdf/2204.12833v3 | 2204.12833 | 10.1007/s10994-025-06748-7 | 7 | 1 | false | null | Machine-mediated learning | 0.2258 |
ddcc258df45fb681b4bbd468d7cdeb29cda3f2e4d6083fb945a177f3a1eecb81 | [
"arxiv",
"semantic_scholar"
] | On Feature Learning in Neural Networks with Global Convergence Guarantees | We study the optimization of wide neural networks (NNs) via gradient flow (GF) in setups that allow feature learning while admitting non-asymptotic global convergence guarantees. First, for wide shallow NNs under the mean-field scaling and with a general class of activation functions, we prove that when the input dimen... | [
"Zhengdao Chen",
"Eric Vanden-Eijnden",
"Joan Bruna"
] | [
"cs.LG",
"math.OC",
"math.PR",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2022-04-22T00:00:00 | https://arxiv.org/abs/2204.10782 | https://arxiv.org/pdf/2204.10782v1 | 2204.10782 | 10.48550/arXiv.2204.10782 | 15 | 2 | false | null | International Conference on Learning Representations | 0.301 |
cd3ed73e79a90f92d1b8dd064d0b64dc3fe530422e8105b3767cd10a8e3e88ea | [
"arxiv",
"semantic_scholar"
] | CNLL: A Semi-supervised Approach For Continual Noisy Label Learning | The task of continual learning requires careful design of algorithms that can tackle catastrophic forgetting. However, the noisy label, which is inevitable in a real-world scenario, seems to exacerbate the situation. While very few studies have addressed the issue of continual learning under noisy labels, long training... | [
"Nazmul Karim",
"Umar Khalid",
"Ashkan Esmaeili",
"Nazanin Rahnavard"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2022-04-21T00:00:00 | https://arxiv.org/abs/2204.09881 | https://arxiv.org/pdf/2204.09881v1 | 2204.09881 | 10.1109/CVPRW56347.2022.00433 | 25 | 5 | false | null | null | 0.3891 |
3444e2077930eb72226d9a13ed45fc515434e2d28a19458cd4e2abb0209d8c06 | [
"arxiv",
"semantic_scholar"
] | Forgetting and Imbalance in Robot Lifelong Learning with Off-policy Data | Robots will experience non-stationary environment dynamics throughout their lifetime: the robot dynamics can change due to wear and tear, or its surroundings may change over time. Eventually, the robots should perform well in all of the environment variations it has encountered. At the same time, it should still be abl... | [
"Wenxuan Zhou",
"Steven Bohez",
"Jan Humplik",
"Abbas Abdolmaleki",
"Dushyant Rao",
"Markus Wulfmeier",
"Tuomas Haarnoja",
"Nicolas Heess"
] | [
"cs.RO",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2022-04-12T00:00:00 | https://arxiv.org/abs/2204.05893 | https://arxiv.org/pdf/2204.05893v2 | 2204.05893 | null | 8 | 1 | false | null | null | 0.2386 |
04f329fbe4fb56eb1f4dffda2a3f2f667deea7864d8ea43c400673c5691f17c2 | [
"arxiv",
"semantic_scholar"
] | A Closer Look at Rehearsal-Free Continual Learning | Continual learning is a setting where machine learning models learn novel concepts from continuously shifting training data, while simultaneously avoiding degradation of knowledge on previously seen classes which may disappear from the training data for extended periods of time (a phenomenon known as the catastrophic f... | [
"James Seale Smith",
"Junjiao Tian",
"Shaunak Halbe",
"Yen-Chang Hsu",
"Zsolt Kira"
] | [
"cs.LG",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2022-03-31T00:00:00 | https://arxiv.org/abs/2203.17269 | https://arxiv.org/pdf/2203.17269v2 | 2203.17269 | 10.1109/CVPRW59228.2023.00239 | 86 | 5 | false | null | null | 0.4849 |
258aa7d6092ff9741c5b7d5bc647ca8d94b64c91cc412cc6f2fead2b88440874 | [
"arxiv",
"semantic_scholar"
] | Continual Normalization: Rethinking Batch Normalization for Online Continual Learning | Existing continual learning methods use Batch Normalization (BN) to facilitate training and improve generalization across tasks. However, the non-i.i.d and non-stationary nature of continual learning data, especially in the online setting, amplify the discrepancy between training and testing in BN and hinder the perfor... | [
"Quang Pham",
"Chenghao Liu",
"Steven Hoi"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2022-03-30T00:00:00 | https://arxiv.org/abs/2203.16102 | https://arxiv.org/pdf/2203.16102v1 | 2203.16102 | 10.48550/arXiv.2203.16102 | 74 | 8 | true | https://github.com/phquang/Continual-Normalization} | International Conference on Learning Representations | 0.4771 |
eb00181bd45f657f9dbd423e5fd63394ced48078f9861bf3fb8d102d45a4d803 | [
"arxiv",
"semantic_scholar"
] | Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization | In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM). Our work takes an initial step towards a surgical investigation of SAM for designing... | [
"Francesco Pelosin",
"Saurav Jha",
"Andrea Torsello",
"Bogdan Raducanu",
"Joost van de Weijer"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2022-03-24T00:00:00 | https://arxiv.org/abs/2203.13167 | https://arxiv.org/pdf/2203.13167v4 | 2203.13167 | 10.1109/CVPRW56347.2022.00427 | 41 | 3 | false | null | null | 0.4058 |
926a6d14949d83ac5fc5053aac82a00d71a34903e5ee29a21d12a45f3048fc55 | [
"arxiv",
"semantic_scholar"
] | Continual Learning and Private Unlearning | As intelligent agents become autonomous over longer periods of time, they may eventually become lifelong counterparts to specific people. If so, it may be common for a user to want the agent to master a task temporarily but later on to forget the task due to privacy concerns. However enabling an agent to \emph{forget p... | [
"Bo Liu",
"Qiang Liu",
"Peter Stone"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2022-03-24T00:00:00 | https://arxiv.org/abs/2203.12817 | https://arxiv.org/pdf/2203.12817v2 | 2203.12817 | 10.48550/arXiv.2203.12817 | 147 | 20 | true | https://github.com/Cranial-XIX/Continual-Learning-Private-Unlearning | null | 0.6611 |
49d67a3b43d4b2a66f2434805ea55527f0c90c8296dd7f707c1a9b8f5776cdee | [
"arxiv",
"semantic_scholar"
] | Probing Representation Forgetting in Supervised and Unsupervised Continual Learning | Continual Learning research typically focuses on tackling the phenomenon of catastrophic forgetting in neural networks. Catastrophic forgetting is associated with an abrupt loss of knowledge previously learned by a model when the task, or more broadly the data distribution, being trained on changes. In supervised learn... | [
"MohammadReza Davari",
"Nader Asadi",
"Sudhir Mudur",
"Rahaf Aljundi",
"Eugene Belilovsky"
] | [
"cs.LG",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2022-03-24T00:00:00 | https://arxiv.org/abs/2203.13381 | https://arxiv.org/pdf/2203.13381v2 | 2203.13381 | 10.1109/CVPR52688.2022.01621 | 97 | 7 | false | null | Computer Vision and Pattern Recognition | 0.4978 |
f4f0fe525d733b45b1c49c6f45e1a93f25d46d1d1333c28573ef881c624a220a | [
"arxiv",
"semantic_scholar"
] | Online Continual Learning for Embedded Devices | Real-time on-device continual learning is needed for new applications such as home robots, user personalization on smartphones, and augmented/virtual reality headsets. However, this setting poses unique challenges: embedded devices have limited memory and compute capacity and conventional machine learning models suffer... | [
"Tyler L. Hayes",
"Christopher Kanan"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2022-03-21T00:00:00 | https://arxiv.org/abs/2203.10681 | https://arxiv.org/pdf/2203.10681v3 | 2203.10681 | 10.48550/arXiv.2203.10681 | 72 | 9 | false | null | null | 0.5 |
98b7506ea76780551ce2aced32c5d9184938a2b4e309ad731114637ef784e730 | [
"arxiv",
"semantic_scholar"
] | A Framework and Benchmark for Deep Batch Active Learning for Regression | The acquisition of labels for supervised learning can be expensive. To improve the sample efficiency of neural network regression, we study active learning methods that adaptively select batches of unlabeled data for labeling. We present a framework for constructing such methods out of (network-dependent) base kernels,... | [
"David Holzmüller",
"Viktor Zaverkin",
"Johannes Kästner",
"Ingo Steinwart"
] | [
"stat.ML",
"cs.LG",
"cs.NE"
] | [
"Computer Science",
"Mathematics"
] | 2022-03-17T00:00:00 | https://arxiv.org/abs/2203.09410 | https://arxiv.org/pdf/2203.09410v4 | 2203.09410 | 10.48550/arXiv.2203.09410 | 63 | 12 | true | https://github.com/dholzmueller/bmdal_reg | Journal of machine learning research | 0.557 |
6fdcf04c181a83d1a71c26431f9a9bfb2f5a55f91db79d27a91203ac108e811f | [
"arxiv",
"semantic_scholar"
] | Overcoming Catastrophic Forgetting beyond Continual Learning: Balanced Training for Neural Machine Translation | Neural networks tend to gradually forget the previously learned knowledge when learning multiple tasks sequentially from dynamic data distributions. This problem is called \textit{catastrophic forgetting}, which is a fundamental challenge in the continual learning of neural networks. In this work, we observe that catas... | [
"Chenze Shao",
"Yang Feng"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2022-03-08T00:00:00 | https://arxiv.org/abs/2203.03910 | https://arxiv.org/pdf/2203.03910v2 | 2203.03910 | 10.48550/arXiv.2203.03910 | 42 | 2 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.4084 |
cdcde6cf2870ca46be96609c1ee4f407013cace4f77683b4c147a5bf595c8ce3 | [
"arxiv",
"semantic_scholar"
] | Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations | In this paper, we will evaluate the performance of graph neural networks in two distinct domains: computer vision and reinforcement learning. In the computer vision section, we seek to learn whether a novel non-redundant representation for images as graphs can improve performance over trivial pixel to node mapping on a... | [
"Naman Goyal",
"David Steiner"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2022-03-07T00:00:00 | https://arxiv.org/abs/2203.03457 | https://arxiv.org/pdf/2203.03457v2 | 2203.03457 | 10.48550/arXiv.2203.03457 | 5 | 0 | false | null | arXiv.org | 0.1945 |
ce04e1ee3c3336291dd3614484d869086ec7289497a91042c71dd5737cff6aa9 | [
"arxiv",
"semantic_scholar"
] | Acceleration of Federated Learning with Alleviated Forgetting in Local Training | Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy by independently training local models on each client and then aggregating parameters on a central server, thereby producing an effective global model. Although a variety of FL algorithms have been proposed, the... | [
"Chencheng Xu",
"Zhiwei Hong",
"Minlie Huang",
"Tao Jiang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2022-03-05T00:00:00 | https://arxiv.org/abs/2203.02645 | https://arxiv.org/pdf/2203.02645v1 | 2203.02645 | 10.48550/arXiv.2203.02645 | 64 | 4 | true | https://github.com/Zoesgithub/FedReg | International Conference on Learning Representations | 0.4532 |
b8c1f22aaa4fbaa87adbad941c7cfdb1d8c0da2ff340a760ff13e02023b72f5c | [
"arxiv",
"semantic_scholar"
] | Continual Learning Beyond a Single Model | A growing body of research in continual learning focuses on the catastrophic forgetting problem. While many attempts have been made to alleviate this problem, the majority of the methods assume a single model in the continual learning setup. In this work, we question this assumption and show that employing ensemble mod... | [
"Thang Doan",
"Seyed Iman Mirzadeh",
"Mehrdad Farajtabar"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2022-02-20T00:00:00 | https://arxiv.org/abs/2202.09826 | https://arxiv.org/pdf/2202.09826v3 | 2202.09826 | null | 24 | 3 | false | null | null | 0.3495 |
b8e133823ab53af787082b27f43cd8f2e47e8383973e5016ca4a0e334d6e5078 | [
"arxiv",
"semantic_scholar"
] | A Neural Network Model of Continual Learning with Cognitive Control | Neural networks struggle in continual learning settings from catastrophic forgetting: when trials are blocked, new learning can overwrite the learning from previous blocks. Humans learn effectively in these settings, in some cases even showing an advantage of blocking, suggesting the brain contains mechanisms to overco... | [
"Jacob Russin",
"Maryam Zolfaghar",
"Seongmin A. Park",
"Erie Boorman",
"Randall C. O'Reilly"
] | [
"q-bio.NC",
"cs.LG",
"cs.NE"
] | [
"Computer Science",
"Biology",
"Medicine"
] | 2022-02-09T00:00:00 | https://arxiv.org/abs/2202.04773 | https://arxiv.org/pdf/2202.04773v2 | 2202.04773 | null | 15 | 2 | false | null | Annual Meeting of the Cognitive Science Society | 0.301 |
d38959ed162e3866161eae0f48ccd74ebf1b12dd916faa3e9d4f8eafd358a17a | [
"arxiv",
"semantic_scholar"
] | Learning Curves for Decision Making in Supervised Machine Learning: A Survey | Learning curves are a concept from social sciences that has been adopted in the context of machine learning to assess the performance of a learning algorithm with respect to a certain resource, e.g., the number of training examples or the number of training iterations. Learning curves have important applications in sev... | [
"Felix Mohr",
"Jan N. van Rijn"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2022-01-28T00:00:00 | https://arxiv.org/abs/2201.12150 | https://arxiv.org/pdf/2201.12150v2 | 2201.12150 | 10.1007/s10994-024-06619-7 | 90 | 5 | false | null | Machine-mediated learning | 0.4898 |
83f4e60da703bac4a360206f1d0184f046f1fc4aaa272d5e9c410914caba78b7 | [
"arxiv",
"semantic_scholar"
] | Combining Optimal Path Search With Task-Dependent Learning in a Neural Network | Finding optimal paths in connected graphs requires determining the smallest total cost for traveling along the graph's edges. This problem can be solved by several classical algorithms where, usually, costs are predefined for all edges. Conventional planning methods can, thus, normally not be used when wanting to chang... | [
"Tomas Kulvicius",
"Minija Tamosiunaite",
"Florentin Wörgötter"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science",
"Medicine"
] | 2022-01-26T00:00:00 | https://arxiv.org/abs/2201.11104 | https://arxiv.org/pdf/2201.11104v6 | 2201.11104 | 10.1109/TNNLS.2023.3327103 | 1 | 0 | false | null | IEEE Transactions on Neural Networks and Learning Systems | 0.0753 |
43e4af76da24024b8ba53ad43e5b3c68bb3fe6a734d4e12fe7aa2d1ae9aff7fa | [
"arxiv",
"semantic_scholar"
] | Visualizing the Diversity of Representations Learned by Bayesian Neural Networks | Explainable Artificial Intelligence (XAI) aims to make learning machines less opaque, and offers researchers and practitioners various tools to reveal the decision-making strategies of neural networks. In this work, we investigate how XAI methods can be used for exploring and visualizing the diversity of feature repres... | [
"Dennis Grinwald",
"Kirill Bykov",
"Shinichi Nakajima",
"Marina M. -C. Höhne"
] | [
"cs.LG",
"cs.AI",
"cs.CV",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2022-01-26T00:00:00 | https://arxiv.org/abs/2201.10859 | https://arxiv.org/pdf/2201.10859v2 | 2201.10859 | null | 7 | 0 | false | null | Published in Transactions on Machine Learning Research (11/2023) | 0.2258 |
f5371aef333465fc855a1243b6b34b6ae193fa9a1348c1ab15636b0c95bb5717 | [
"arxiv",
"semantic_scholar"
] | Learning to Predict Gradients for Semi-Supervised Continual Learning | A key challenge for machine intelligence is to learn new visual concepts without forgetting the previously acquired knowledge. Continual learning is aimed towards addressing this challenge. However, there is a gap between existing supervised continual learning and human-like intelligence, where human is able to learn f... | [
"Yan Luo",
"Yongkang Wong",
"Mohan Kankanhalli",
"Qi Zhao"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science",
"Medicine"
] | 2022-01-23T00:00:00 | https://arxiv.org/abs/2201.09196 | https://arxiv.org/pdf/2201.09196v2 | 2201.09196 | 10.1109/TNNLS.2024.3361375 | 14 | 2 | true | https://github.com/luoyan407/grad_prediction.git} | IEEE Transactions on Neural Networks and Learning Systems | 0.294 |
9309167c5db651ec09bbea94c7518cb59df6ce1813e2bc72ae4cfacc1239328e | [
"arxiv",
"semantic_scholar"
] | Automatic Sparse Connectivity Learning for Neural Networks | Since sparse neural networks usually contain many zero weights, these unnecessary network connections can potentially be eliminated without degrading network performance. Therefore, well-designed sparse neural networks have the potential to significantly reduce FLOPs and computational resources. In this work, we propos... | [
"Zhimin Tang",
"Linkai Luo",
"Bike Xie",
"Yiyu Zhu",
"Rujie Zhao",
"Lvqing Bi",
"Chao Lu"
] | [
"cs.CV",
"cs.AI",
"cs.LG"
] | [
"Computer Science",
"Medicine"
] | 2022-01-13T00:00:00 | https://arxiv.org/abs/2201.05020 | https://arxiv.org/pdf/2201.05020v1 | 2201.05020 | 10.1109/TNNLS.2022.3141665 | 50 | 1 | false | null | IEEE Transactions on Neural Networks and Learning Systems | 0.4269 |
b75dab3e845cb4527ca0679885582cc2e75c5e595416b4893e3eec4116b0a7b5 | [
"arxiv",
"semantic_scholar"
] | Continually Learning Self-Supervised Representations with Projected Functional Regularization | Recent self-supervised learning methods are able to learn high-quality image representations and are closing the gap with supervised approaches. However, these methods are unable to acquire new knowledge incrementally -- they are, in fact, mostly used only as a pre-training phase over IID data. In this work we investig... | [
"Alex Gomez-Villa",
"Bartlomiej Twardowski",
"Lu Yu",
"Andrew D. Bagdanov",
"Joost van de Weijer"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2021-12-30T00:00:00 | https://arxiv.org/abs/2112.15022 | https://arxiv.org/pdf/2112.15022v2 | 2112.15022 | 10.1109/CVPRW56347.2022.00432 | 55 | 2 | false | null | null | 0.437 |
5949321441070c37c5f3c6bec6b5f2b2b0e2782d23aaa7c3d637601c7764e85d | [
"arxiv"
] | Federated Learning with Superquantile Aggregation for Heterogeneous Data | We present a federated learning framework that is designed to robustly deliver good predictive performance across individual clients with heterogeneous data. The proposed approach hinges upon a superquantile-based learning objective that captures the tail statistics of the error distribution over heterogeneous clients.... | [
"Krishna Pillutla",
"Yassine Laguel",
"Jérôme Malick",
"Zaid Harchaoui"
] | [
"cs.LG",
"math.OC",
"stat.ML"
] | [] | 2021-12-17T00:00:00 | https://arxiv.org/abs/2112.09429 | https://arxiv.org/pdf/2112.09429v2 | 2112.09429 | null | 0 | 0 | false | null | Machine Learning (2023): 1-68 | 0 |
8c300fed7da60ed87352081870afad00617caa2d2ba3d1adafd6d94538411557 | [
"arxiv",
"semantic_scholar"
] | An Empirical Investigation of the Role of Pre-training in Lifelong Learning | The lifelong learning paradigm in machine learning is an attractive alternative to the more prominent isolated learning scheme not only due to its resemblance to biological learning but also its potential to reduce energy waste by obviating excessive model re-training. A key challenge to this paradigm is the phenomenon... | [
"Sanket Vaibhav Mehta",
"Darshan Patil",
"Sarath Chandar",
"Emma Strubell"
] | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2021-12-16T00:00:00 | https://arxiv.org/abs/2112.09153 | https://arxiv.org/pdf/2112.09153v2 | 2112.09153 | null | 178 | 5 | false | null | Journal of machine learning research | 0.5632 |
8d6f0008cee3a14d8e085fefd83b387d1b845cd84a80dd255bb7767d98c14fbc | [
"arxiv",
"semantic_scholar"
] | CSG0: Continual Urban Scene Generation with Zero Forgetting | With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving. We address in this work a continual scene generation setup in which GANs are trained on a stream of distinct domains; id... | [
"Himalaya Jain",
"Tuan-Hung Vu",
"Patrick Pérez",
"Matthieu Cord"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2021-12-06T00:00:00 | https://arxiv.org/abs/2112.03252 | https://arxiv.org/pdf/2112.03252v2 | 2112.03252 | 10.1109/CVPRW56347.2022.00412 | 0 | 0 | false | null | null | 0 |
d329e034c642241f31160980318568097c73584846fc73c871875987b98e2af8 | [
"arxiv",
"semantic_scholar"
] | Learning Curves for Continual Learning in Neural Networks: Self-Knowledge Transfer and Forgetting | Sequential training from task to task is becoming one of the major objects in deep learning applications such as continual learning and transfer learning. Nevertheless, it remains unclear under what conditions the trained model's performance improves or deteriorates. To deepen our understanding of sequential training, ... | [
"Ryo Karakida",
"Shotaro Akaho"
] | [
"stat.ML",
"cond-mat.dis-nn",
"cs.LG"
] | [
"Computer Science",
"Mathematics",
"Physics"
] | 2021-12-03T00:00:00 | https://arxiv.org/abs/2112.01653 | https://arxiv.org/pdf/2112.01653v2 | 2112.01653 | null | 16 | 1 | false | null | International Conference on Learning Representations | 0.3076 |
b8110a0c62fda8dc7b5c0d9f143a400561636ce491564a46ae0423d52a0f722a | [
"arxiv",
"semantic_scholar"
] | Learning by Active Forgetting for Neural Networks | Remembering and forgetting mechanisms are two sides of the same coin in a human learning-memory system. Inspired by human brain memory mechanisms, modern machine learning systems have been working to endow machine with lifelong learning capability through better remembering while pushing the forgetting as the antagonis... | [
"Jian Peng",
"Xian Sun",
"Min Deng",
"Chao Tao",
"Bo Tang",
"Wenbo Li",
"Guohua Wu",
" QingZhu",
"Yu Liu",
"Tao Lin",
"Haifeng Li"
] | [
"cs.LG",
"cs.AI",
"cs.NE"
] | [
"Computer Science"
] | 2021-11-21T00:00:00 | https://arxiv.org/abs/2111.10831 | https://arxiv.org/pdf/2111.10831v1 | 2111.10831 | null | 4 | 0 | false | null | arXiv.org | 0.1747 |
78a197446f10b90defbdf191e328e2721f86fb030985033d480550a4ff2bf8b0 | [
"arxiv",
"semantic_scholar"
] | Lifelong Learning from Event-based Data | Lifelong learning is a long-standing aim for artificial agents that act in dynamic environments, in which an agent needs to accumulate knowledge incrementally without forgetting previously learned representations. We investigate methods for learning from data produced by event cameras and compare techniques to mitigate... | [
"Vadym Gryshchuk",
"Cornelius Weber",
"Chu Kiong Loo",
"Stefan Wermter"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2021-11-11T00:00:00 | https://arxiv.org/abs/2111.08458 | https://arxiv.org/pdf/2111.08458v1 | 2111.08458 | 10.14428/esann/2021.es2021-146 | 0 | 0 | false | null | The European Symposium on Artificial Neural Networks | 0 |
58d2ea335e1e4ab6ce4f983f802ea97b84f99a8cc6c94ec10404bcfa03714eca | [
"arxiv",
"semantic_scholar"
] | d3rlpy: An Offline Deep Reinforcement Learning Library | In this paper, we introduce d3rlpy, an open-sourced offline deep reinforcement learning (RL) library for Python. d3rlpy supports a set of offline deep RL algorithms as well as off-policy online algorithms via a fully documented plug-and-play API. To address a reproducibility issue, we conduct a large-scale benchmark wi... | [
"Takuma Seno",
"Michita Imai"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2021-11-06T00:00:00 | https://arxiv.org/abs/2111.03788 | https://arxiv.org/pdf/2111.03788v2 | 2111.03788 | null | 144 | 10 | true | https://github.com/takuseno/d3rlpy} | Journal of machine learning research | 0.5403 |
44ca0c5cd2838a2f6b6a511bd8d229e0aa97abff573077d0d06b70b6903771dc | [
"arxiv",
"semantic_scholar"
] | AFEC: Active Forgetting of Negative Transfer in Continual Learning | Continual learning aims to learn a sequence of tasks from dynamic data distributions. Without accessing to the old training samples, knowledge transfer from the old tasks to each new task is difficult to determine, which might be either positive or negative. If the old knowledge interferes with the learning of a new ta... | [
"Liyuan Wang",
"Mingtian Zhang",
"Zhongfan Jia",
"Qian Li",
"Chenglong Bao",
"Kaisheng Ma",
"Jun Zhu",
"Yi Zhong"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-10-23T00:00:00 | https://arxiv.org/abs/2110.12187 | https://arxiv.org/pdf/2110.12187v2 | 2110.12187 | null | 120 | 13 | false | null | Neural Information Processing Systems | 0.5731 |
c0462459f0c8bbde63263fad20d2c8638061189c3b83cd8aa2b13e0a78e6cd99 | [
"arxiv",
"semantic_scholar"
] | Wide Neural Networks Forget Less Catastrophically | A primary focus area in continual learning research is alleviating the "catastrophic forgetting" problem in neural networks by designing new algorithms that are more robust to the distribution shifts. While the recent progress in continual learning literature is encouraging, our understanding of what properties of neur... | [
"Seyed Iman Mirzadeh",
"Arslan Chaudhry",
"Dong Yin",
"Huiyi Hu",
"Razvan Pascanu",
"Dilan Gorur",
"Mehrdad Farajtabar"
] | [
"cs.LG",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2021-10-21T00:00:00 | https://arxiv.org/abs/2110.11526 | https://arxiv.org/pdf/2110.11526v3 | 2110.11526 | null | 87 | 11 | false | null | International Conference on Machine Learning | 0.5396 |
d3644669f55c61283267f091c6e2d05097e175848e3851a18fc2f90ca2cd75d2 | [
"arxiv",
"semantic_scholar"
] | Carousel Memory: Rethinking the Design of Episodic Memory for Continual Learning | Continual Learning (CL) is an emerging machine learning paradigm that aims to learn from a continuous stream of tasks without forgetting knowledge learned from the previous tasks. To avoid performance decrease caused by forgetting, prior studies exploit episodic memory (EM), which stores a subset of the past observed s... | [
"Soobee Lee",
"Minindu Weerakoon",
"Jonghyun Choi",
"Minjia Zhang",
"Di Wang",
"Myeongjae Jeon"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2021-10-14T00:00:00 | https://arxiv.org/abs/2110.07276 | https://arxiv.org/pdf/2110.07276v3 | 2110.07276 | null | 2 | 0 | false | null | arXiv.org | 0.1193 |
657a6f766a7557c748d26e79c0441e847f9ef4e04ea5ad46bd60e956bd1c9ae8 | [
"arxiv",
"semantic_scholar"
] | LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5 | Existing approaches to lifelong language learning rely on plenty of labeled data for learning a new task, which is hard to obtain in most real scenarios. Considering that humans can continually learn new tasks from a handful of examples, we expect the models also to be able to generalize well on new few-shot tasks with... | [
"Chengwei Qin",
"Shafiq Joty"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2021-10-14T00:00:00 | https://arxiv.org/abs/2110.07298 | https://arxiv.org/pdf/2110.07298v3 | 2110.07298 | null | 133 | 13 | true | https://github.com/qcwthu/Lifelong-Fewshot-Language-Learning | International Conference on Learning Representations | 0.5731 |
445920c0865aa39b3b4559bd89fc1216d03a848db6132e1f9adb4c7a4275c6b9 | [
"arxiv",
"semantic_scholar"
] | Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse Networks | Using task-specific components within a neural network in continual learning (CL) is a compelling strategy to address the stability-plasticity dilemma in fixed-capacity models without access to past data. Current methods focus only on selecting a sub-network for a new task that reduces forgetting of past tasks. However... | [
"Ghada Sokar",
"Decebal Constantin Mocanu",
"Mykola Pechenizkiy"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2021-10-11T00:00:00 | https://arxiv.org/abs/2110.05329 | https://arxiv.org/pdf/2110.05329v3 | 2110.05329 | 10.1007/978-3-031-26409-2_6 | 10 | 0 | false | null | null | 0.2603 |
f5dc40b4b1f68bc61bafe9b3ebc6dca8c0c7e099ce273f2ac4eb68f4707696a8 | [
"arxiv",
"semantic_scholar"
] | QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks | The advent of noisy intermediate-scale quantum (NISQ) computers raises a crucial challenge to design quantum neural networks for fully quantum learning tasks. To bridge the gap, this work proposes an end-to-end learning framework named QTN-VQC, by introducing a trainable quantum tensor network (QTN) for quantum embeddi... | [
"Jun Qi",
"Chao-Han Huck Yang",
"Pin-Yu Chen"
] | [
"quant-ph",
"cs.AI",
"cs.CL",
"cs.CV",
"cs.LG",
"cs.NE"
] | [
"Physics",
"Computer Science"
] | 2021-10-06T00:00:00 | https://arxiv.org/abs/2110.03861 | https://arxiv.org/pdf/2110.03861v3 | 2110.03861 | 10.1088/1402-4896/ad14d6 | 65 | 4 | false | null | Physica Scripta | 0.4549 |
911493f35fdab11c6dfaa6bf450439429ea6b25aa44842e029e1a059a2b6d70f | [
"arxiv",
"semantic_scholar"
] | Assisted Learning for Organizations with Limited Imbalanced Data | In the era of big data, many big organizations are integrating machine learning into their work pipelines to facilitate data analysis. However, the performance of their trained models is often restricted by limited and imbalanced data available to them. In this work, we develop an assisted learning framework for assist... | [
"Cheng Chen",
"Jiaying Zhou",
"Jie Ding",
"Yi Zhou"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-09-20T00:00:00 | https://arxiv.org/abs/2109.09307 | https://arxiv.org/pdf/2109.09307v4 | 2109.09307 | null | 3 | 0 | false | null | C. Chen, J. Zhou, J. Ding, and Y. Zhou, "Assisted Learning for Organizations with Limited Imbalanced Data," Transactions on Machine Learning Research (TMLR), 2023 | 0.1505 |
1c12afc65a9eb52c71c6485436ca9f10430a7e020532bab66e9bd15f9602f466 | [
"arxiv",
"semantic_scholar"
] | Concave Utility Reinforcement Learning with Zero-Constraint Violations | We consider the problem of tabular infinite horizon concave utility reinforcement learning (CURL) with convex constraints. For this, we propose a model-based learning algorithm that also achieves zero constraint violations. Assuming that the concave objective and the convex constraints have a solution interior to the s... | [
"Mridul Agarwal",
"Qinbo Bai",
"Vaneet Aggarwal"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2021-09-12T00:00:00 | https://arxiv.org/abs/2109.05439 | https://arxiv.org/pdf/2109.05439v3 | 2109.05439 | null | 19 | 1 | false | null | Transactions on Machine Learning Research, Dec 2022 | 0.3253 |
b8550b3a313508224578a451d96ca8ffbdf7eb450d7d20f3c7760bc0029b35ef | [
"arxiv",
"semantic_scholar"
] | Quantum Continual Learning Overcoming Catastrophic Forgetting | Catastrophic forgetting describes the fact that machine learning models will likely forget the knowledge of previously learned tasks after the learning process of a new one. It is a vital problem in the continual learning scenario and recently has attracted tremendous concern across different communities. In this paper... | [
"Wenjie Jiang",
"Zhide Lu",
"Dong-Ling Deng"
] | [
"cs.LG",
"cond-mat.mes-hall",
"quant-ph"
] | [
"Physics",
"Computer Science"
] | 2021-08-05T00:00:00 | https://arxiv.org/abs/2108.02786 | https://arxiv.org/pdf/2108.02786v1 | 2108.02786 | 10.1088/0256-307X/39/5/050303 | 13 | 2 | false | null | Chinese Physics Letters | 0.2865 |
c9054c2960a3bd170d8d9041ece091e8ab44bdefbf46c85f79bdccd03c29c14b | [
"arxiv",
"semantic_scholar"
] | A Pragmatic Look at Deep Imitation Learning | The introduction of the generative adversarial imitation learning (GAIL) algorithm has spurred the development of scalable imitation learning approaches using deep neural networks. Many of the algorithms that followed used a similar procedure, combining on-policy actor-critic algorithms with inverse reinforcement learn... | [
"Kai Arulkumaran",
"Dan Ogawa Lillrank"
] | [
"cs.LG",
"cs.NE",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-08-04T00:00:00 | https://arxiv.org/abs/2108.01867 | https://arxiv.org/pdf/2108.01867v2 | 2108.01867 | null | 13 | 1 | false | null | Asian Conference on Machine Learning | 0.2865 |
e4fe80112dae1d20f6c5594c8971b7d2a20bc745e3c3ff486e7627b906132e29 | [
"arxiv",
"semantic_scholar"
] | Open-Ended Learning Leads to Generally Capable Agents | In this work we create agents that can perform well beyond a single, individual task, that exhibit much wider generalisation of behaviour to a massive, rich space of challenges. We define a universe of tasks within an environment domain and demonstrate the ability to train agents that are generally capable across this ... | [
" Open Ended Learning Team",
"Adam Stooke",
"Anuj Mahajan",
"Catarina Barros",
"Charlie Deck",
"Jakob Bauer",
"Jakub Sygnowski",
"Maja Trebacz",
"Max Jaderberg",
"Michael Mathieu",
"Nat McAleese",
"Nathalie Bradley-Schmieg",
"Nathaniel Wong",
"Nicolas Porcel",
"Roberta Raileanu",
"Step... | [
"cs.LG",
"cs.AI",
"cs.MA"
] | [
"Computer Science"
] | 2021-07-27T00:00:00 | https://arxiv.org/abs/2107.12808 | https://arxiv.org/pdf/2107.12808v2 | 2107.12808 | null | 229 | 20 | false | null | arXiv.org | 0.6611 |
5a585a2db2622fe24415f8e4c579a92c27323ef908b42a8535de8e977c7e2cae | [
"arxiv",
"semantic_scholar"
] | Continual Learning in the Teacher-Student Setup: Impact of Task Similarity | Continual learning-the ability to learn many tasks in sequence-is critical for artificial learning systems. Yet standard training methods for deep networks often suffer from catastrophic forgetting, where learning new tasks erases knowledge of earlier tasks. While catastrophic forgetting labels the problem, the theoret... | [
"Sebastian Lee",
"Sebastian Goldt",
"Andrew Saxe"
] | [
"stat.ML",
"cond-mat.stat-mech",
"cs.LG"
] | [
"Computer Science",
"Mathematics",
"Physics"
] | 2021-07-09T00:00:00 | https://arxiv.org/abs/2107.04384 | https://arxiv.org/pdf/2107.04384v1 | 2107.04384 | null | 101 | 8 | false | null | International Conference on Machine Learning | 0.5022 |
96831650473938045b2bb97bf70d143a7a2696506da667980858772ef2783209 | [
"arxiv",
"semantic_scholar"
] | Asymptotics of Network Embeddings Learned via Subsampling | Network data are ubiquitous in modern machine learning, with tasks of interest including node classification, node clustering and link prediction. A frequent approach begins by learning an Euclidean embedding of the network, to which algorithms developed for vector-valued data are applied. For large networks, embedding... | [
"Andrew Davison",
"Morgane Austern"
] | [
"stat.ML",
"cs.LG",
"math.ST"
] | [
"Mathematics",
"Computer Science"
] | 2021-07-06T00:00:00 | https://arxiv.org/abs/2107.02363 | https://arxiv.org/pdf/2107.02363v4 | 2107.02363 | null | 14 | 5 | false | null | arXiv.org | 0.3891 |
b9de67d24dcb926dd5996e0ececb23c39960df6f127bf6b57b8fc1e7128b786f | [
"arxiv",
"semantic_scholar"
] | Autoencoder based Randomized Learning of Feedforward Neural Networks for Regression | Feedforward neural networks are widely used as universal predictive models to fit data distribution. Common gradient-based learning, however, suffers from many drawbacks making the training process ineffective and time-consuming. Alternative randomized learning does not use gradients but selects hidden node parameters ... | [
"Grzegorz Dudek"
] | [
"cs.LG",
"cs.NE"
] | [
"Computer Science"
] | 2021-07-04T00:00:00 | https://arxiv.org/abs/2107.01711 | https://arxiv.org/pdf/2107.01711v1 | 2107.01711 | 10.1109/IJCNN52387.2021.9534263 | 1 | 0 | false | null | IEEE International Joint Conference on Neural Network | 0.0753 |
50e26fff9b8c9bb47849fab7f5447a6797786f8fa0f474254db30b11d9c78b07 | [
"arxiv",
"semantic_scholar"
] | Continual Competitive Memory: A Neural System for Online Task-Free Lifelong Learning | In this article, we propose a novel form of unsupervised learning, continual competitive memory (CCM), as well as a computational framework to unify related neural models that operate under the principles of competition. The resulting neural system is shown to offer an effective approach for combating catastrophic forg... | [
"Alexander G. Ororbia"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-06-24T00:00:00 | https://arxiv.org/abs/2106.13300 | https://arxiv.org/pdf/2106.13300v1 | 2106.13300 | 10.31219/osf.io/yw6ua | 7 | 1 | false | null | arXiv.org | 0.2258 |
d4364f12085a5faa51a1560f84b3b901044a2443217e2aeea8fb47f4a170d022 | [
"arxiv",
"semantic_scholar"
] | Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positions | Most existing graph neural networks (GNNs) learn node embeddings using the framework of message passing and aggregation. Such GNNs are incapable of learning relative positions between graph nodes within a graph. To empower GNNs with the awareness of node positions, some nodes are set as anchors. Then, using the distanc... | [
"Zhenyue Qin",
"Yiqun Zhang Saeed Anwar",
"Dongwoo Kim",
"Yang Liu",
"Pan Ji",
"Tom Gedeon"
] | [
"cs.LG"
] | [
"Computer Science",
"Medicine"
] | 2021-05-24T00:00:00 | https://arxiv.org/abs/2105.11346 | https://arxiv.org/pdf/2105.11346v2 | 2105.11346 | 10.1109/TNNLS.2024.3374464 | 2 | 0 | true | https://github.com/ZhenyueQin/PSGNN | IEEE Transactions on Neural Networks and Learning Systems | 0.1193 |
6a13a14848f5b7550fef3e4cbc75f1ba1b998adb328b15835b68994c65e2216b | [
"arxiv",
"semantic_scholar"
] | Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection | Weakly-supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data. Recent works build deep neural networks for anomaly detection by discriminatively mapping the normal samples and abnormal samples to different regions in the feature space or fitti... | [
"Yingjie Zhou",
"Xucheng Song",
"Yanru Zhang",
"Fanxing Liu",
"Ce Zhu",
"Lingqiao Liu"
] | [
"cs.LG",
"cs.NI"
] | [
"Computer Science",
"Medicine"
] | 2021-05-22T00:00:00 | https://arxiv.org/abs/2105.10500 | https://arxiv.org/pdf/2105.10500v3 | 2105.10500 | 10.1109/TNNLS.2021.3086137 | 150 | 22 | false | null | IEEE Transactions on Neural Networks and Learning Systems | 0.6809 |
65d13e6c4d80606088db0b8158b0f3783869b858e2c72d8239288f77b76651fc | [
"arxiv",
"semantic_scholar"
] | Statistical Mechanical Analysis of Catastrophic Forgetting in Continual Learning with Teacher and Student Networks | When a computational system continuously learns from an ever-changing environment, it rapidly forgets its past experiences. This phenomenon is called catastrophic forgetting. While a line of studies has been proposed with respect to avoiding catastrophic forgetting, most of the methods are based on intuitive insights i... | [
"Haruka Asanuma",
"Shiro Takagi",
"Yoshihiro Nagano",
"Yuki Yoshida",
"Yasuhiko Igarashi",
"Masato Okada"
] | [
"stat.ML",
"cs.LG"
] | [
"Mathematics",
"Computer Science"
] | 2021-05-16T00:00:00 | https://arxiv.org/abs/2105.07385 | https://arxiv.org/pdf/2105.07385v1 | 2105.07385 | 10.7566/JPSJ.90.104001 | 25 | 2 | false | null | Journal of the Physical Society of Japan | 0.3537 |
18c813deeb0c7e87652b14f7bb33405205f7f27e2c6517e52a44a3768f043854 | [
"arxiv",
"semantic_scholar"
] | TAG: Task-based Accumulated Gradients for Lifelong learning | When an agent encounters a continual stream of new tasks in the lifelong learning setting, it leverages the knowledge it gained from the earlier tasks to help learn the new tasks better. In such a scenario, identifying an efficient knowledge representation becomes a challenging problem. Most research works propose to e... | [
"Pranshu Malviya",
"Balaraman Ravindran",
"Sarath Chandar"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-05-11T00:00:00 | https://arxiv.org/abs/2105.05155 | https://arxiv.org/pdf/2105.05155v3 | 2105.05155 | null | 8 | 1 | false | null | null | 0.2386 |
0b7a74ce55c275b9c3b2462d1c4eb3776b1640577e9134b046076442bc7fda11 | [
"arxiv",
"semantic_scholar"
] | The Modern Mathematics of Deep Learning | We describe the new field of mathematical analysis of deep learning. This field emerged around a list of research questions that were not answered within the classical framework of learning theory. These questions concern: the outstanding generalization power of overparametrized neural networks, the role of depth in de... | [
"Julius Berner",
"Philipp Grohs",
"Gitta Kutyniok",
"Philipp Petersen"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-05-09T00:00:00 | https://arxiv.org/abs/2105.04026 | https://arxiv.org/pdf/2105.04026v2 | 2105.04026 | 10.1017/9781009025096.002 | 134 | 8 | false | null | arXiv.org | 0.5326 |
493262ba23a2233010fcc66ebdec692bcb68760006b87efda2ea4d692825f166 | [
"arxiv",
"semantic_scholar"
] | Noether's Learning Dynamics: Role of Symmetry Breaking in Neural Networks | In nature, symmetry governs regularities, while symmetry breaking brings texture. In artificial neural networks, symmetry has been a central design principle to efficiently capture regularities in the world, but the role of symmetry breaking is not well understood. Here, we develop a theoretical framework to study the ... | [
"Hidenori Tanaka",
"Daniel Kunin"
] | [
"cs.LG",
"cond-mat.dis-nn",
"cond-mat.stat-mech",
"q-bio.NC",
"stat.ML"
] | [
"Computer Science",
"Physics",
"Biology",
"Mathematics"
] | 2021-05-06T00:00:00 | https://arxiv.org/abs/2105.02716 | https://arxiv.org/pdf/2105.02716v2 | 2105.02716 | null | 50 | 6 | false | null | Neural Information Processing Systems | 0.4269 |
6349c1f85dca1e78446dfb9d249fead664a46ed09a42dd0711313950631a9b9b | [
"arxiv",
"semantic_scholar"
] | Continual Distributed Learning for Crisis Management | Social media platforms such as Twitter, Facebook etc can be utilised as an important source of information during disaster events. This information can be used for disaster response and crisis management if processed accurately and quickly. However, the data present in such situations is ever-changing, and using consid... | [
"Aman Priyanshu",
"Mudit Sinha",
"Shreyans Mehta"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2021-04-26T00:00:00 | https://arxiv.org/abs/2104.12876 | https://arxiv.org/pdf/2104.12876v2 | 2104.12876 | null | 5 | 0 | false | null | arXiv.org | 0.1945 |
c0b39f14661b63a42b0d49c31cfd39b68e69f476b4aa8154d629ebffd723a830 | [
"arxiv",
"semantic_scholar"
] | Class-Incremental Learning with Generative Classifiers | Incrementally training deep neural networks to recognize new classes is a challenging problem. Most existing class-incremental learning methods store data or use generative replay, both of which have drawbacks, while 'rehearsal-free' alternatives such as parameter regularization or bias-correction methods do not consis... | [
"Gido M. van de Ven",
"Zhe Li",
"Andreas S. Tolias"
] | [
"cs.LG",
"cs.AI",
"cs.CV",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-04-20T00:00:00 | https://arxiv.org/abs/2104.10093 | https://arxiv.org/pdf/2104.10093v2 | 2104.10093 | 10.1109/CVPRW53098.2021.00400 | 76 | 5 | false | null | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021, pp. 3611-3620 | 0.4716 |
7fd685d28f9f6768fb2fbc7205907ab54ca1b7405ad9cc2767087dede0c573f0 | [
"arxiv",
"semantic_scholar"
] | Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference | In this paper we analyze the classification performance of neural network structures without parametric inference. Making use of neural architecture search, we empirically demonstrate that it is possible to find random weight architectures, a deep prior, that enables a linear classification to perform on par with fully... | [
"Martin Mundt",
"Iuliia Pliushch",
"Visvanathan Ramesh"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-04-14T00:00:00 | https://arxiv.org/abs/2104.06788 | https://arxiv.org/pdf/2104.06788v1 | 2104.06788 | 10.1109/CVPRW53098.2021.00391 | 8 | 0 | false | null | null | 0.2386 |
55ac9b9b66e071fa4a8319ca7bb31a8e8d94bdfb68eb9855a64bc6500079c1c8 | [
"arxiv",
"semantic_scholar"
] | FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks | Graph Neural Network (GNN) research is rapidly growing thanks to the capacity of GNNs in learning distributed representations from graph-structured data. However, centralizing a massive amount of real-world graph data for GNN training is prohibitive due to privacy concerns, regulation restrictions, and commercial compe... | [
"Chaoyang He",
"Keshav Balasubramanian",
"Emir Ceyani",
"Carl Yang",
"Han Xie",
"Lichao Sun",
"Lifang He",
"Liangwei Yang",
"Philip S. Yu",
"Yu Rong",
"Peilin Zhao",
"Junzhou Huang",
"Murali Annavaram",
"Salman Avestimehr"
] | [
"cs.LG",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2021-04-14T00:00:00 | https://arxiv.org/abs/2104.07145 | https://arxiv.org/pdf/2104.07145v2 | 2104.07145 | null | 2 | 0 | true | https://github.com/FedML-AI/FedGraphNN | null | 0.1193 |
9c11cd8234e09e1a3636b413a2c41cf57bf1fabe986fc44b01fdf3f1276f2d60 | [
"arxiv",
"semantic_scholar"
] | Towards Lifelong Learning of End-to-end ASR | Automatic speech recognition (ASR) technologies today are primarily optimized for given datasets; thus, any changes in the application environment (e.g., acoustic conditions or topic domains) may inevitably degrade the performance. We can collect new data describing the new environment and fine-tune the system, but thi... | [
"Heng-Jui Chang",
"Hung-yi Lee",
"Lin-shan Lee"
] | [
"cs.CL",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2021-04-04T00:00:00 | https://arxiv.org/abs/2104.01616 | https://arxiv.org/pdf/2104.01616v3 | 2104.01616 | 10.21437/interspeech.2021-563 | 40 | 4 | false | null | Interspeech | 0.4032 |
dde51736d56d0d5121be26b086371fc46e5a116299574a5669f7c165e086d860 | [
"arxiv",
"semantic_scholar"
] | Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning | Online class-incremental continual learning (CL) studies the problem of learning new classes continually from an online non-stationary data stream, intending to adapt to new data while mitigating catastrophic forgetting. While memory replay has shown promising results, the recency bias in online learning caused by the ... | [
"Zheda Mai",
"Ruiwen Li",
"Hyunwoo Kim",
"Scott Sanner"
] | [
"cs.LG",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2021-03-22T00:00:00 | https://arxiv.org/abs/2103.13885 | https://arxiv.org/pdf/2103.13885v3 | 2103.13885 | 10.1109/CVPRW53098.2021.00398 | 234 | 46 | false | null | null | 0.836 |
4491f0c310151840e7f5761a9241bcd24352159229e872b25ee31846cac0f7a1 | [
"arxiv",
"semantic_scholar"
] | Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification | In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario. The primary objective of the analysis is to understand whether classical continual learning techniques for flat and sequential data have a tangible impact on performances when applied to graph data. To do so,... | [
"Antonio Carta",
"Andrea Cossu",
"Federico Errica",
"Davide Bacciu"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2021-03-22T00:00:00 | https://arxiv.org/abs/2103.11750 | https://arxiv.org/pdf/2103.11750v1 | 2103.11750 | null | 20 | 1 | true | https://github.com/diningphil/continual_learning_for_graphs | arXiv.org | 0.3306 |
3893663afda3168eb4047e81d6d9c6bc25b721dd4c67d50549903de416f27f0d | [
"arxiv",
"semantic_scholar"
] | Gradient Projection Memory for Continual Learning | The ability to learn continually without forgetting the past tasks is a desired attribute for artificial learning systems. Existing approaches to enable such learning in artificial neural networks usually rely on network growth, importance based weight update or replay of old data from the memory. In contrast, we propo... | [
"Gobinda Saha",
"Isha Garg",
"Kaushik Roy"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2021-03-17T00:00:00 | https://arxiv.org/abs/2103.09762 | https://arxiv.org/pdf/2103.09762v1 | 2103.09762 | null | 439 | 83 | false | null | International Conference on Learning Representations | 0.9621 |
3e6e194ab04d772f7c9a4b361d812ce0f755d0c92dfa14b49eea8c1f8c98ae8b | [
"arxiv",
"semantic_scholar"
] | Continual Learning for Recurrent Neural Networks: an Empirical Evaluation | Learning continuously during all model lifetime is fundamental to deploy machine learning solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) with recurrent neural networks could pave the way to a large number of applications where incoming data is non stationary, like natural langu... | [
"Andrea Cossu",
"Antonio Carta",
"Vincenzo Lomonaco",
"Davide Bacciu"
] | [
"cs.LG",
"cs.AI"
] | [
"Medicine",
"Computer Science"
] | 2021-03-12T00:00:00 | https://arxiv.org/abs/2103.07492 | https://arxiv.org/pdf/2103.07492v4 | 2103.07492 | 10.1016/j.neunet.2021.07.021 | 125 | 5 | false | null | Neural Networks | 0.5251 |
49ee26fdf5ce1cb708a1a8d9f9cbe68cc512340a6bb08a30388741bcb790bb70 | [
"arxiv",
"semantic_scholar"
] | Selective Replay Enhances Learning in Online Continual Analogical Reasoning | In continual learning, a system learns from non-stationary data streams or batches without catastrophic forgetting. While this problem has been heavily studied in supervised image classification and reinforcement learning, continual learning in neural networks designed for abstract reasoning has not yet been studied. H... | [
"Tyler L. Hayes",
"Christopher Kanan"
] | [
"cs.AI",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2021-03-06T00:00:00 | https://arxiv.org/abs/2103.03987 | https://arxiv.org/pdf/2103.03987v2 | 2103.03987 | 10.1109/CVPRW53098.2021.00389 | 26 | 2 | false | null | null | 0.3578 |
9b54446bf32a252279c0920a4d83acb2b8805b3c53ed3419c6cc121cea8747ce | [
"arxiv",
"semantic_scholar"
] | Learning to Continually Learn Rapidly from Few and Noisy Data | Neural networks suffer from catastrophic forgetting and are unable to sequentially learn new tasks without guaranteed stationarity in data distribution. Continual learning could be achieved via replay -- by concurrently training externally stored old data while learning a new task. However, replay becomes less effectiv... | [
"Nicholas I-Hsien Kuo",
"Mehrtash Harandi",
"Nicolas Fourrier",
"Christian Walder",
"Gabriela Ferraro",
"Hanna Suominen"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-03-06T00:00:00 | https://arxiv.org/abs/2103.04066 | https://arxiv.org/pdf/2103.04066v1 | 2103.04066 | null | 4 | 0 | false | null | null | 0.1747 |
17e943dbee24d2e9baeaaf4eac66284ffc67248dd91721b2d2a1f8cd5eb067c5 | [
"arxiv",
"semantic_scholar"
] | Continuous Coordination As a Realistic Scenario for Lifelong Learning | Current deep reinforcement learning (RL) algorithms are still highly task-specific and lack the ability to generalize to new environments. Lifelong learning (LLL), however, aims at solving multiple tasks sequentially by efficiently transferring and using knowledge between tasks. Despite a surge of interest in lifelong ... | [
"Hadi Nekoei",
"Akilesh Badrinaaraayanan",
"Aaron Courville",
"Sarath Chandar"
] | [
"cs.LG",
"cs.AI",
"cs.MA"
] | [
"Computer Science"
] | 2021-03-04T00:00:00 | https://arxiv.org/abs/2103.03216 | https://arxiv.org/pdf/2103.03216v2 | 2103.03216 | null | 51 | 0 | true | https://github.com/chandar-lab/Lifelong-Hanabi | International Conference on Machine Learning | 0.429 |
3b171e001ccacc2bcb3d5b73d9f01d8823e5f7a3da6a00b7ed26bc9c10b96eaa | [
"arxiv",
"semantic_scholar"
] | Significance tests of feature relevance for a black-box learner | An exciting recent development is the uptake of deep neural networks in many scientific fields, where the main objective is outcome prediction with the black-box nature. Significance testing is promising to address the black-box issue and explore novel scientific insights and interpretation of the decision-making proce... | [
"Ben Dai",
"Xiaotong Shen",
"Wei Pan"
] | [
"stat.ML",
"cs.LG",
"stat.ME"
] | [
"Medicine",
"Computer Science",
"Mathematics"
] | 2021-03-02T00:00:00 | https://arxiv.org/abs/2103.04985 | https://arxiv.org/pdf/2103.04985v3 | 2103.04985 | 10.1109/TNNLS.2022.3185742 | 39 | 6 | false | null | IEEE Transactions on Neural Networks and Learning Systems | 0.4225 |
141ba209d9fed665f6ada9baef1fbc5f869d417c31888aa8101cad64827fca20 | [
"arxiv",
"semantic_scholar"
] | Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning | Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with hig... | [
"Yixin Liu",
"Zhao Li",
"Shirui Pan",
"Chen Gong",
"Chuan Zhou",
"George Karypis"
] | [
"cs.LG"
] | [
"Medicine",
"Computer Science"
] | 2021-02-27T00:00:00 | https://arxiv.org/abs/2103.00113 | https://arxiv.org/pdf/2103.00113v2 | 2103.00113 | 10.1109/TNNLS.2021.3068344 | 475 | 71 | false | null | IEEE Transactions on Neural Networks and Learning Systems | 0.9287 |
94cfe88f95a09aa9a76cf5ce18dd1d1ead579597464cc2c685e228004d8db172 | [
"arxiv",
"semantic_scholar"
] | Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping | Catastrophic forgetting in neural networks is a significant problem for continual learning. A majority of the current methods replay previous data during training, which violates the constraints of an ideal continual learning system. Additionally, current approaches that deal with forgetting ignore the problem of catas... | [
"Prakhar Kaushik",
"Alex Gain",
"Adam Kortylewski",
"Alan Yuille"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2021-02-22T00:00:00 | https://arxiv.org/abs/2102.11343 | https://arxiv.org/pdf/2102.11343v1 | 2102.11343 | null | 79 | 11 | false | null | arXiv.org | 0.5396 |
4254fdc4f52fcfd31e1846cdf9efb4472da4e76574381e9d8e932d5cdf564935 | [
"arxiv",
"semantic_scholar"
] | Does the Adam Optimizer Exacerbate Catastrophic Forgetting? | Catastrophic forgetting remains a severe hindrance to the broad application of artificial neural networks (ANNs), however, it continues to be a poorly understood phenomenon. Despite the extensive amount of work on catastrophic forgetting, we argue that it is still unclear how exactly the phenomenon should be quantified... | [
"Dylan R. Ashley",
"Sina Ghiassian",
"Richard S. Sutton"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2021-02-15T00:00:00 | https://arxiv.org/abs/2102.07686 | https://arxiv.org/pdf/2102.07686v4 | 2102.07686 | null | 9 | 1 | true | https://github.com/dylanashley/catastrophic-forgetting/tree/arxiv | null | 0.25 |
5987a4ca544db0665932633fb430a22f0fe5ff2bcba4b5c5a1b0e890a05bc104 | [
"arxiv",
"semantic_scholar"
] | How do Quadratic Regularizers Prevent Catastrophic Forgetting: The Role of Interpolation | Catastrophic forgetting undermines the effectiveness of deep neural networks (DNNs) in scenarios such as continual learning and lifelong learning. While several methods have been proposed to tackle this problem, there is limited work explaining why these methods work well. This paper has the goal of better explaining a... | [
"Ekdeep Singh Lubana",
"Puja Trivedi",
"Danai Koutra",
"Robert P. Dick"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2021-02-04T00:00:00 | https://arxiv.org/abs/2102.02805 | https://arxiv.org/pdf/2102.02805v5 | 2102.02805 | null | 19 | 2 | true | https://github.com/EkdeepSLubana/QRforgetting} | null | 0.3253 |
b656ea5efe362798d93d49f60d28ffacc551eb34462092244a52a1189e67a9b6 | [
"arxiv",
"semantic_scholar"
] | Local Critic Training for Model-Parallel Learning of Deep Neural Networks | In this paper, we propose a novel model-parallel learning method, called local critic training, which trains neural networks using additional modules called local critic networks. The main network is divided into several layer groups and each layer group is updated through error gradients estimated by the corresponding... | [
"Hojung Lee",
"Cho-Jui Hsieh",
"Jong-Seok Lee"
] | [
"cs.LG"
] | [
"Medicine",
"Computer Science"
] | 2021-02-03T00:00:00 | https://arxiv.org/abs/2102.01963 | https://arxiv.org/pdf/2102.01963v1 | 2102.01963 | 10.1109/TNNLS.2021.3057380 | 19 | 1 | true | https://github.com/hjdw2/Local-critic-training | IEEE Transactions on Neural Networks and Learning Systems | 0.3253 |
4723e04e60371d1cc0c30c74da1624a30b33c45a484fbe096bccd83d6c19c8b8 | [
"arxiv",
"semantic_scholar"
] | Online Continual Learning in Image Classification: An Empirical Survey | Online continual learning for image classification studies the problem of learning to classify images from an online stream of data and tasks, where tasks may include new classes (class incremental) or data nonstationarity (domain incremental). One of the key challenges of continual learning is to avoid catastrophic fo... | [
"Zheda Mai",
"Ruiwen Li",
"Jihwan Jeong",
"David Quispe",
"Hyunwoo Kim",
"Scott Sanner"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2021-01-25T00:00:00 | https://arxiv.org/abs/2101.10423 | https://arxiv.org/pdf/2101.10423v4 | 2101.10423 | 10.1016/j.neucom.2021.10.021 | 520 | 45 | true | https://github.com/RaptorMai/online-continual-learning | Neurocomputing | 0.8314 |
6ea65d21285bc406a3e29a1cca65a7ae43e7fc70cf712f95c91ab2ea779b41b8 | [
"arxiv",
"semantic_scholar"
] | Learning Invariant Representation for Continual Learning | Continual learning aims to provide intelligent agents that are capable of learning continually a sequence of tasks, building on previously learned knowledge. A key challenge in this learning paradigm is catastrophically forgetting previously learned tasks when the agent faces a new one. Current rehearsal-based methods ... | [
"Ghada Sokar",
"Decebal Constantin Mocanu",
"Mykola Pechenizkiy"
] | [
"cs.LG",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2021-01-15T00:00:00 | https://arxiv.org/abs/2101.06162 | https://arxiv.org/pdf/2101.06162v1 | 2101.06162 | null | 16 | 0 | false | null | arXiv.org | 0.3076 |
717163f298f717786ef8228ad3d619b838b50b7a1bf7a4d2fda3ebe778e9b7a8 | [
"arxiv",
"semantic_scholar"
] | EEC: Learning to Encode and Regenerate Images for Continual Learning | The two main impediments to continual learning are catastrophic forgetting and memory limitations on the storage of data. To cope with these challenges, we propose a novel, cognitively-inspired approach which trains autoencoders with Neural Style Transfer to encode and store images. During training on a new task, recon... | [
"Ali Ayub",
"Alan R. Wagner"
] | [
"cs.CV",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2021-01-13T00:00:00 | https://arxiv.org/abs/2101.04904 | https://arxiv.org/pdf/2101.04904v4 | 2101.04904 | null | 63 | 3 | false | null | International Conference on Learning Representations | 0.4515 |
57f415d05dd41f401cfc9b636090335f5a592bc921fb02114b61b6eb3071ea11 | [
"arxiv",
"semantic_scholar"
] | Overcoming Catastrophic Forgetting in Graph Neural Networks | Catastrophic forgetting refers to the tendency that a neural network "forgets" the previous learned knowledge upon learning new tasks. Prior methods have been focused on overcoming this problem on convolutional neural networks (CNNs), where the input samples like images lie in a grid domain, but have largely overlooked... | [
"Huihui Liu",
"Yiding Yang",
"Xinchao Wang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2020-12-10T00:00:00 | https://arxiv.org/abs/2012.06002 | https://arxiv.org/pdf/2012.06002v1 | 2012.06002 | 10.1609/aaai.v35i10.17049 | 175 | 30 | true | https://github.com/hhliu79/TWP} | AAAI Conference on Artificial Intelligence | 0.7457 |
6d53dba052c60671fcfb72204bd5e768c599811b6a845120e9c161c6f5f1921d | [
"arxiv",
"semantic_scholar"
] | Reset-Free Lifelong Learning with Skill-Space Planning | The objective of lifelong reinforcement learning (RL) is to optimize agents which can continuously adapt and interact in changing environments. However, current RL approaches fail drastically when environments are non-stationary and interactions are non-episodic. We propose Lifelong Skill Planning (LiSP), an algorithmi... | [
"Kevin Lu",
"Aditya Grover",
"Pieter Abbeel",
"Igor Mordatch"
] | [
"cs.LG",
"cs.AI",
"cs.RO"
] | [
"Computer Science"
] | 2020-12-07T00:00:00 | https://arxiv.org/abs/2012.03548 | https://arxiv.org/pdf/2012.03548v3 | 2012.03548 | null | 43 | 4 | false | null | International Conference on Learning Representations | 0.4109 |
b2f8a6ec7747a44b0256cb545ae4899f98eb710f41b2efde1ba4e2a75841ca1c | [
"arxiv",
"semantic_scholar"
] | Model-Agnostic Learning to Meta-Learn | In this paper, we propose a learning algorithm that enables a model to quickly exploit commonalities among related tasks from an unseen task distribution, before quickly adapting to specific tasks from that same distribution. We investigate how learning with different task distributions can first improve adaptability b... | [
"Arnout Devos",
"Yatin Dandi"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-12-04T00:00:00 | https://arxiv.org/abs/2012.02684 | https://arxiv.org/pdf/2012.02684v2 | 2012.02684 | null | 3 | 0 | false | null | null | 0.1505 |
9b6117bb55d6931dc985f0b556a1d0a2ac5cb92a7f05fb383d17c9fa8a1caf6f | [
"arxiv",
"semantic_scholar"
] | Energy-Based Models for Continual Learning | We motivate Energy-Based Models (EBMs) as a promising model class for continual learning problems. Instead of tackling continual learning via the use of external memory, growing models, or regularization, EBMs change the underlying training objective to cause less interference with previously learned information. Our p... | [
"Shuang Li",
"Yilun Du",
"Gido M. van de Ven",
"Igor Mordatch"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-11-24T00:00:00 | https://arxiv.org/abs/2011.12216 | https://arxiv.org/pdf/2011.12216v3 | 2011.12216 | null | 49 | 2 | false | null | Proceedings of The 1st Conference on Lifelong Learning Agents, PMLR 199: 1-22, 2022 | 0.4247 |
8c3948ded69b09f0cd102a8ea188cea6b7a19b0753b101c10db567f0593f60c3 | [
"arxiv",
"semantic_scholar"
] | Generalized Continual Zero-Shot Learning | Recently, zero-shot learning (ZSL) emerged as an exciting topic and attracted a lot of attention. ZSL aims to classify unseen classes by transferring the knowledge from seen classes to unseen classes based on the class description. Despite showing promising performance, ZSL approaches assume that the training samples f... | [
"Chandan Gautam",
"Sethupathy Parameswaran",
"Ashish Mishra",
"Suresh Sundaram"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2020-11-17T00:00:00 | https://arxiv.org/abs/2011.08508 | https://arxiv.org/pdf/2011.08508v3 | 2011.08508 | null | 12 | 3 | false | null | arXiv.org | 0.301 |
40942244c31047aeb70589fbf77db5c250b67c7e1f09007b29862f1e83d31dfd | [
"arxiv",
"semantic_scholar"
] | Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting | Deep learning is often criticized by two serious issues which rarely exist in natural nervous systems: overfitting and catastrophic forgetting. It can even memorize randomly labelled data, which has little knowledge behind the instance-label pairs. When a deep network continually learns over time by accommodating new t... | [
"Zeke Xie",
"Fengxiang He",
"Shaopeng Fu",
"Issei Sato",
"Dacheng Tao",
"Masashi Sugiyama"
] | [
"cs.LG"
] | [
"Medicine",
"Computer Science"
] | 2020-11-12T00:00:00 | https://arxiv.org/abs/2011.06220 | https://arxiv.org/pdf/2011.06220v3 | 2011.06220 | 10.1162/neco_a_01403 | 70 | 1 | true | https://github.com/zeke-xie/artificial-neural-variability-for-deep-learning} | Neural Computation | 0.4628 |
412ad1836b8b2f46854d24733bf726fd9703bc726e2d9e6ec828b76a930683cc | [
"arxiv",
"semantic_scholar"
] | Learning with Molecules beyond Graph Neural Networks | We demonstrate a deep learning framework which is inherently based in the highly expressive language of relational logic, enabling to, among other things, capture arbitrarily complex graph structures. We show how Graph Neural Networks and similar models can be easily covered in the framework by specifying the underlyin... | [
"Gustav Sourek",
"Filip Zelezny",
"Ondrej Kuzelka"
] | [
"cs.LG",
"cs.AI",
"cs.LO",
"cs.NE"
] | [
"Computer Science"
] | 2020-11-06T00:00:00 | https://arxiv.org/abs/2011.03488 | https://arxiv.org/pdf/2011.03488v1 | 2011.03488 | null | 3 | 0 | false | null | arXiv.org | 0.1505 |
e48464867f1ca1926e27025dc203839ae44cbffda76dbb5993b4976eb1970063 | [
"arxiv",
"semantic_scholar"
] | Learning Invariances in Neural Networks | Invariances to translations have imbued convolutional neural networks with powerful generalization properties. However, we often do not know a priori what invariances are present in the data, or to what extent a model should be invariant to a given symmetry group. We show how to \emph{learn} invariances and equivarianc... | [
"Gregory Benton",
"Marc Finzi",
"Pavel Izmailov",
"Andrew Gordon Wilson"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-10-22T00:00:00 | https://arxiv.org/abs/2010.11882 | https://arxiv.org/pdf/2010.11882v2 | 2010.11882 | null | 79 | 15 | true | https://github.com/g-benton/learning-invariances | Neural Information Processing Systems | 0.6021 |
636fdb24579e511324e0e173761210eff991b3748efae003124463d2a2bb159c | [
"arxiv",
"semantic_scholar"
] | Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control | The energy-efficient control of mobile robots is crucial as the complexity of their real-world applications increasingly involves high-dimensional observation and action spaces, which cannot be offset by limited on-board resources. An emerging non-Von Neumann model of intelligence, where spiking neural networks (SNNs) ... | [
"Guangzhi Tang",
"Neelesh Kumar",
"Raymond Yoo",
"Konstantinos P. Michmizos"
] | [
"cs.NE",
"cs.LG",
"cs.RO"
] | [
"Computer Science"
] | 2020-10-19T00:00:00 | https://arxiv.org/abs/2010.09635 | https://arxiv.org/pdf/2010.09635v1 | 2010.09635 | null | 120 | 18 | false | null | Conference on Robot Learning | 0.6394 |
459d8b194fb93e0a2173c0c8aa8fc08f5117d80a591ba7d2b673cdc1da9fc404 | [
"arxiv",
"semantic_scholar"
] | A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix | Continual learning (CL) is a setting in which an agent has to learn from an incoming stream of data during its entire lifetime. Although major advances have been made in the field, one recurring problem which remains unsolved is that of Catastrophic Forgetting (CF). While the issue has been extensively studied empirica... | [
"Thang Doan",
"Mehdi Bennani",
"Bogdan Mazoure",
"Guillaume Rabusseau",
"Pierre Alquier"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-10-07T00:00:00 | https://arxiv.org/abs/2010.04003 | https://arxiv.org/pdf/2010.04003v2 | 2010.04003 | null | 115 | 12 | false | null | International Conference on Artificial Intelligence and Statistics | 0.557 |
42cc21765d01f60d1568575bbf0059daf6d5a03e454fb67466b1003f7fcc62d9 | [
"arxiv",
"semantic_scholar"
] | Task Agnostic Continual Learning Using Online Variational Bayes with Fixed-Point Updates | Background: Catastrophic forgetting is the notorious vulnerability of neural networks to the changes in the data distribution during learning. This phenomenon has long been considered a major obstacle for using learning agents in realistic continual learning settings. A large body of continual learning research assumes... | [
"Chen Zeno",
"Itay Golan",
"Elad Hoffer",
"Daniel Soudry"
] | [
"stat.ML",
"cs.LG"
] | [
"Computer Science",
"Medicine",
"Mathematics"
] | 2020-10-01T00:00:00 | https://arxiv.org/abs/2010.00373 | https://arxiv.org/pdf/2010.00373v2 | 2010.00373 | 10.1162/neco_a_01430 | 52 | 4 | false | null | Neural Computation | 0.4311 |
ad03857bd8cdcd237fa2b2f91f01dd6c91d04a73b58846d2be46ed6a394f9134 | [
"arxiv",
"semantic_scholar"
] | Beneficial Perturbation Network for designing general adaptive artificial intelligence systems | The human brain is the gold standard of adaptive learning. It not only can learn and benefit from experience, but also can adapt to new situations. In contrast, deep neural networks only learn one sophisticated but fixed mapping from inputs to outputs. This limits their applicability to more dynamic situations, where i... | [
"Shixian Wen",
"Amanda Rios",
"Yunhao Ge",
"Laurent Itti"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science",
"Medicine"
] | 2020-09-27T00:00:00 | https://arxiv.org/abs/2009.13954 | https://arxiv.org/pdf/2009.13954v2 | 2009.13954 | 10.1109/TNNLS.2021.3054423 | 18 | 0 | false | null | IEEE Transactions on Neural Networks and Learning Systems | 0.3197 |
cdb32c2bd659d511059508210f9f078b2a3f99780428bf1c7219dc52761d6ddc | [
"arxiv",
"semantic_scholar"
] | Theoretical Analysis of the Advantage of Deepening Neural Networks | We propose two new criteria to understand the advantage of deepening neural networks. It is important to know the expressivity of functions computable by deep neural networks in order to understand the advantage of deepening neural networks. Unless deep neural networks have enough expressivity, they cannot have good pe... | [
"Yasushi Esaki",
"Yuta Nakahara",
"Toshiyasu Matsushima"
] | [
"cs.LG",
"cs.NE",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-09-24T00:00:00 | https://arxiv.org/abs/2009.11479 | https://arxiv.org/pdf/2009.11479v1 | 2009.11479 | 10.1109/ICMLA51294.2020.00081 | 1 | 0 | false | null | International Conference on Machine Learning and Applications | 0.0753 |
a9f48fd186c794d2131c1e9e1463d6c7b70906f41249d0c3aff05fe6132bada1 | [
"arxiv",
"semantic_scholar"
] | Online Learning With Adaptive Rebalancing in Nonstationary Environments | An enormous and ever-growing volume of data is nowadays becoming available in a sequential fashion in various real-world applications. Learning in nonstationary environments constitutes a major challenge, and this problem becomes orders of magnitude more complex in the presence of class imbalance. We provide new insigh... | [
"Kleanthis Malialis",
"Christos G. Panayiotou",
"Marios M. Polycarpou"
] | [
"cs.LG",
"stat.ML"
] | [
"Medicine",
"Computer Science",
"Mathematics"
] | 2020-09-24T00:00:00 | https://arxiv.org/abs/2009.11942 | https://arxiv.org/pdf/2009.11942v1 | 2009.11942 | 10.1109/TNNLS.2020.3017863 | 47 | 2 | false | null | IEEE Transactions on Neural Networks and Learning Systems | 0.4203 |
9803d1f7adcbb0946490a26bd1cf44f523fc48dc9a21f8001312df209f884b65 | [
"arxiv",
"semantic_scholar"
] | Anomalous diffusion dynamics of learning in deep neural networks | Learning in deep neural networks (DNNs) is implemented through minimizing a highly non-convex loss function, typically by a stochastic gradient descent (SGD) method. This learning process can effectively find good wide minima without being trapped in poor local ones. We present a novel account of how such effective dee... | [
"Guozhang Chen",
"Cheng Kevin Qu",
"Pulin Gong"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Medicine",
"Mathematics"
] | 2020-09-22T00:00:00 | https://arxiv.org/abs/2009.10588 | https://arxiv.org/pdf/2009.10588v2 | 2009.10588 | 10.1016/j.neunet.2022.01.019 | 27 | 1 | false | null | Neural Networks | 0.3618 |
ea31c7998913fbd1f0522ee1b75267f1d28588e0f9ed334293ef3bcc167e69d1 | [
"arxiv",
"semantic_scholar"
] | Few-Shot Unsupervised Continual Learning through Meta-Examples | In real-world applications, data do not reflect the ones commonly used for neural networks training, since they are usually few, unlabeled and can be available as a stream. Hence many existing deep learning solutions suffer from a limited range of applications, in particular in the case of online streaming data that ev... | [
"Alessia Bertugli",
"Stefano Vincenzi",
"Simone Calderara",
"Andrea Passerini"
] | [
"cs.LG",
"cs.CV",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-09-17T00:00:00 | https://arxiv.org/abs/2009.08107 | https://arxiv.org/pdf/2009.08107v3 | 2009.08107 | null | 8 | 0 | false | null | arXiv.org | 0.2386 |
ae29b1cad21cae8a1cf2b3743fac60a28416e76ff30ddad3bc76a77689db93f2 | [
"arxiv",
"semantic_scholar"
] | Routing Networks with Co-training for Continual Learning | The core challenge with continual learning is catastrophic forgetting, the phenomenon that when neural networks are trained on a sequence of tasks they rapidly forget previously learned tasks. It has been observed that catastrophic forgetting is most severe when tasks are dissimilar to each other. We propose the use of... | [
"Mark Collier",
"Efi Kokiopoulou",
"Andrea Gesmundo",
"Jesse Berent"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-09-09T00:00:00 | https://arxiv.org/abs/2009.04381 | https://arxiv.org/pdf/2009.04381v1 | 2009.04381 | null | 15 | 0 | false | null | arXiv.org | 0.301 |
e59fc03c9de24712a1263328ab7eb64ab8ba55f95e790d35881a31254b46a3ca | [
"arxiv",
"semantic_scholar"
] | A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning | Current deep learning methods are regarded as favorable if they empirically perform well on dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual learning, where consecutively arriving data is investigated. The core challenge is framed as protecting previously acquired represe... | [
"Martin Mundt",
"Yongwon Hong",
"Iuliia Pliushch",
"Visvanathan Ramesh"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Medicine",
"Mathematics"
] | 2020-09-03T00:00:00 | https://arxiv.org/abs/2009.01797 | https://arxiv.org/pdf/2009.01797v3 | 2009.01797 | 10.1016/j.neunet.2023.01.014 | 182 | 8 | false | null | Neural Networks | 0.5656 |
36750662c20d270de6e87816af0e39ddfdc6a93d460e879b3a3a79de902d52a4 | [
"arxiv",
"semantic_scholar"
] | Lifelong Graph Learning | Graph neural networks (GNN) are powerful models for many graph-structured tasks. Existing models often assume that the complete structure of the graph is available during training. In practice, however, graph-structured data is usually formed in a streaming fashion so that learning a graph continuously is often necessa... | [
"Chen Wang",
"Yuheng Qiu",
"Dasong Gao",
"Sebastian Scherer"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-09-01T00:00:00 | https://arxiv.org/abs/2009.00647 | https://arxiv.org/pdf/2009.00647v4 | 2009.00647 | 10.1109/CVPR52688.2022.01335 | 61 | 8 | true | https://github.com/wang-chen/LGL | Computer Vision and Pattern Recognition | 0.4771 |
eb42acf3efba04d6b5caa3bd772cbe08cb0a788d7aa26a9c31acfd70b3d1d570 | [
"arxiv",
"semantic_scholar"
] | Amortized learning of neural causal representations | Causal models can compactly and efficiently encode the data-generating process under all interventions and hence may generalize better under changes in distribution. These models are often represented as Bayesian networks and learning them scales poorly with the number of variables. Moreover, these approaches cannot le... | [
"Nan Rosemary Ke",
"Jane. X. Wang",
"Jovana Mitrovic",
"Martin Szummer",
"Danilo J. Rezende"
] | [
"stat.ML",
"cs.LG"
] | [
"Computer Science",
"Mathematics"
] | 2020-08-21T00:00:00 | https://arxiv.org/abs/2008.09301 | https://arxiv.org/pdf/2008.09301v1 | 2008.09301 | null | 22 | 1 | false | null | arXiv.org | 0.3404 |
ec5ba80fac68c7b04c55f4868179f4f35353e3bd2589aabfa58fffe4dab007b8 | [
"arxiv",
"semantic_scholar"
] | Interactive Imitation Learning in State-Space | Imitation Learning techniques enable programming the behavior of agents through demonstrations rather than manual engineering. However, they are limited by the quality of available demonstration data. Interactive Imitation Learning techniques can improve the efficacy of learning since they involve teachers providing fe... | [
"Snehal Jauhri",
"Carlos Celemin",
"Jens Kober"
] | [
"cs.RO",
"cs.LG"
] | [
"Computer Science"
] | 2020-08-02T00:00:00 | https://arxiv.org/abs/2008.00524 | https://arxiv.org/pdf/2008.00524v2 | 2008.00524 | null | 16 | 1 | false | null | Conference on Robot Learning | 0.3076 |
41ddf9b65d0ef986e2f7fe8a25b011f27925a03a84001063556134b6398a9572 | [
"arxiv",
"semantic_scholar"
] | Lifelong Incremental Reinforcement Learning with Online Bayesian Inference | A central capability of a long-lived reinforcement learning (RL) agent is to incrementally adapt its behavior as its environment changes, and to incrementally build upon previous experiences to facilitate future learning in real-world scenarios. In this paper, we propose LifeLong Incremental Reinforcement Learning (LLI... | [
"Zhi Wang",
"Chunlin Chen",
"Daoyi Dong"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science",
"Medicine"
] | 2020-07-28T00:00:00 | https://arxiv.org/abs/2007.14196 | https://arxiv.org/pdf/2007.14196v2 | 2007.14196 | 10.1109/TNNLS.2021.3055499 | 67 | 6 | false | null | IEEE Transactions on Neural Networks and Learning Systems | 0.4581 |
ff7965e5814e9639a868918e7a374c0cea96bb398b2f3cc9d480bb880e9066cc | [
"arxiv",
"semantic_scholar"
] | Tighter risk certificates for neural networks | This paper presents an empirical study regarding training probabilistic neural networks using training objectives derived from PAC-Bayes bounds. In the context of probabilistic neural networks, the output of training is a probability distribution over network weights. We present two training objectives, used here for t... | [
"María Pérez-Ortiz",
"Omar Rivasplata",
"John Shawe-Taylor",
"Csaba Szepesvári"
] | [
"cs.LG",
"cs.CV",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2020-07-25T00:00:00 | https://arxiv.org/abs/2007.12911 | https://arxiv.org/pdf/2007.12911v3 | 2007.12911 | null | 130 | 26 | false | null | Journal of machine learning research | 0.7157 |
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