Datasets:

---

mackelab

/

sbi-for-connectomics-data

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Machine Learning in Science 4

• "nflows.transforms.linear.LinearCache",
• "nflows.nn.nets.resnet.ResidualBlock",
• "torch._utils._rebuild_tensor_v2",
• "torch.distributions.transforms.ComposeTransform",
• "torch.nn.modules.linear.Identity",
• "torch.distributions.transforms.IndependentTransform",
• "sbi.inference.posteriors.direct_posterior.DirectPosterior",
• "torch.distributions.transforms.AffineTransform",
• "nflows.transforms.coupling.PiecewiseRationalQuadraticCouplingTransform",
• "nflows.flows.base.Flow",
• "builtins.getattr",
• "torch.distributions.transforms.SigmoidTransform",
• "numpy.core.multiarray._reconstruct",
• "torch.storage._load_from_bytes",
• "sbi.inference.potentials.posterior_based_potential.PosteriorBasedPotential",
• "torch.distributions.uniform.Uniform",
• "collections.OrderedDict",
• "torch.nn.modules.container.ModuleList",
• "numpy.ndarray",
• "torch.distributions.transforms._InverseTransform",
• "nflows.transforms.standard.AffineTransform",
• "nflows.transforms.base.CompositeTransform",
• "numpy.dtype",
• "torch._C._VariableFunctionsClass",
• "nflows.transforms.lu.LULinear",
• "nflows.nn.nets.resnet.ResidualNet",
• "torch.nn.modules.container.Sequential",
• "sbi.utils.sbiutils.Standardize",
• "torch.Size",
• "torch.nn.modules.dropout.Dropout",
• "nflows.distributions.normal.StandardNormal",
• "torch._utils._rebuild_parameter",
• "sbi.utils.torchutils.BoxUniform",
• "torch.nn.modules.linear.Linear",
• "builtins.set"

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