# Pavel Izmailov

According to our database

Collaborative distances:

^{1}, Pavel Izmailov authored at least 13 papers between 2016 and 2020.Collaborative distances:

## Timeline

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## Bibliography

2020

Tensor Train Decomposition on TensorFlow (T3F).

J. Mach. Learn. Res., 2020

Why Normalizing Flows Fail to Detect Out-of-Distribution Data.

CoRR, 2020

Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data.

CoRR, 2020

Bayesian Deep Learning and a Probabilistic Perspective of Generalization.

CoRR, 2020

2019

Semi-Supervised Learning with Normalizing Flows.

CoRR, 2019

Subspace Inference for Bayesian Deep Learning.

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

A Simple Baseline for Bayesian Uncertainty in Deep Learning.

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average.

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Improving Consistency-Based Semi-Supervised Learning with Weight Averaging.

CoRR, 2018

Averaging Weights Leads to Wider Optima and Better Generalization.

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs.

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition.

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2016

Faster variational inducing input Gaussian process classification.

CoRR, 2016