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06/19/2023
Simple and Fast Group Robustness by Automatic Feature Reweighting
A major challenge to out-of-distribution generalization is reliance on s...
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12/15/2022
FlexiViT: One Model for All Patch Sizes
Vision Transformers convert images to sequences by slicing them into pat...
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10/20/2022
On Feature Learning in the Presence of Spurious Correlations
Deep classifiers are known to rely on spurious features x2013 patterns w...
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04/06/2022
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations
Neural network classifiers can largely rely on simple spurious features,...
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03/30/2022
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
Aleatoric uncertainty captures the inherent randomness of the data, such...
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02/23/2022
Bayesian Model Selection, the Marginal Likelihood, and Generalization
How do we compare between hypotheses that are entirely consistent with o...
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06/22/2021
Dangers of Bayesian Model Averaging under Covariate Shift
Approximate Bayesian inference for neural networks is considered a robus...
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06/10/2021
Does Knowledge Distillation Really Work?
Knowledge distillation is a popular technique for training a small stude...
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04/29/2021
What Are Bayesian Neural Network Posteriors Really Like?
The posterior over Bayesian neural network (BNN) parameters is extremely...
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10/22/2020
Learning Invariances in Neural Networks
Invariances to translations have imbued convolutional neural networks wi...
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06/15/2020
Why Normalizing Flows Fail to Detect Out-of-Distribution Data
Detecting out-of-distribution (OOD) data is crucial for robust machine l...
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02/25/2020
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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02/20/2020
Bayesian Deep Learning and a Probabilistic Perspective of Generalization
The key distinguishing property of a Bayesian approach is marginalizatio...
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12/30/2019
Semi-Supervised Learning with Normalizing Flows
Normalizing flows transform a latent distribution through an invertible ...
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07/17/2019
Subspace Inference for Bayesian Deep Learning
Bayesian inference was once a gold standard for learning with neural net...
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02/07/2019
A Simple Baseline for Bayesian Uncertainty in Deep Learning
We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose ...
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06/14/2018
Improving Consistency-Based Semi-Supervised Learning with Weight Averaging
Recent advances in deep unsupervised learning have renewed interest in s...
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03/14/2018
Averaging Weights Leads to Wider Optima and Better Generalization
Deep neural networks are typically trained by optimizing a loss function...
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02/27/2018
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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01/05/2018
Tensor Train decomposition on TensorFlow (T3F)
Tensor Train decomposition is used across many branches of machine learn...
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10/19/2017
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition
We propose a method (TT-GP) for approximate inference in Gaussian Proces...
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11/18/2016