According to our database1, Pavel Izmailov authored at least 13 papers between 2016 and 2020.
Legend:Book In proceedings Article PhD thesis Other
Tensor Train Decomposition on TensorFlow (T3F).
J. Mach. Learn. Res., 2020
Why Normalizing Flows Fail to Detect Out-of-Distribution Data.
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data.
Bayesian Deep Learning and a Probabilistic Perspective of Generalization.
Semi-Supervised Learning with Normalizing Flows.
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
Improving Consistency-Based Semi-Supervised Learning with Weight Averaging.
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
Faster variational inducing input Gaussian process classification.