
Bayesian Deep Learning and a Probabilistic Perspective of Generalization
The key distinguishing property of a Bayesian approach is marginalizatio...
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A Simple Baseline for Bayesian Uncertainty in Deep Learning
We propose SWAGaussian (SWAG), a simple, scalable, and general purpose ...
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SemiSupervised Learning with Normalizing Flows
Normalizing flows transform a latent distribution through an invertible ...
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Why Normalizing Flows Fail to Detect OutofDistribution Data
Detecting outofdistribution (OOD) data is crucial for robust machine l...
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Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data
The translation equivariance of convolutional layers enables convolution...
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Subspace Inference for Bayesian Deep Learning
Bayesian inference was once a gold standard for learning with neural net...
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Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition
We propose a method (TTGP) for approximate inference in Gaussian Proces...
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Faster variational inducing input Gaussian process classification
Gaussian processes (GP) provide a prior over functions and allow finding...
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Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
The loss functions of deep neural networks are complex and their geometr...
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Averaging Weights Leads to Wider Optima and Better Generalization
Deep neural networks are typically trained by optimizing a loss function...
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Improving ConsistencyBased SemiSupervised Learning with Weight Averaging
Recent advances in deep unsupervised learning have renewed interest in s...
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Tensor Train decomposition on TensorFlow (T3F)
Tensor Train decomposition is used across many branches of machine learn...
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Pavel Izmailov
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