32 citations · 109 across the 7 of their papers we have counts for
6 papers · 1 filter
Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers
Wanqian Yang, Polina Kirichenko, Micah Goldblum +1
Deep neural networks are susceptible to shortcut learning, using simple features to achieve low training loss without discovering essential semantic structure. Contrary to prior be…
On Feature Learning in the Presence of Spurious Correlations
Pavel Izmailov, Polina Kirichenko, Nate Gruver +1
Deep classifiers are known to rely on spurious features $\unicode{x2013}$ patterns which are correlated with the target on the training data but not inherently relevant to the lear…
Task-agnostic Continual Learning with Hybrid Probabilistic Models
Polina Kirichenko, Mehrdad Farajtabar, Dushyant Rao +6
Learning new tasks continuously without forgetting on a constantly changing data distribution is essential for real-world problems but extremely challenging for modern deep learnin…
Semi-Supervised Learning with Normalizing Flows
Pavel Izmailov, Polina Kirichenko, Marc Finzi +1
Normalizing flows transform a latent distribution through an invertible neural network for a flexible and pleasingly simple approach to generative modelling, while preserving an ex…
Subspace Inference for Bayesian Deep Learning
Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko +3
Bayesian inference was once a gold standard for learning with neural networks, providing accurate full predictive distributions and well calibrated uncertainty. However, scaling Ba…
SWALP : Stochastic Weight Averaging in Low-Precision Training
Guandao Yang, Tianyi Zhang, Polina Kirichenko +3
Low precision operations can provide scalability, memory savings, portability, and energy efficiency. This paper proposes SWALP, an approach to low precision training that averages…