7 papers
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation
Dmitry Molchanov, Alexander Lyzhov, Yuliya Molchanova +2
Test-time data augmentationaveraging the predictions of a machine learning model across multiple augmented samples of datais a widely used technique that improves the predict…
Variational Dropout via Empirical Bayes
Valery Kharitonov, Dmitry Molchanov, Dmitry Vetrov
We study the Automatic Relevance Determination procedure applied to deep neural networks. We show that ARD applied to Bayesian DNNs with Gaussian approximate posterior distribution…
Doubly Semi-Implicit Variational Inference
Dmitry Molchanov, Valery Kharitonov, Artem Sobolev +1
We extend the existing framework of semi-implicit variational inference (SIVI) and introduce doubly semi-implicit variational inference (DSIVI), a way to perform variational infere…
Bayesian Incremental Learning for Deep Neural Networks
Max Kochurov, Timur Garipov, Dmitry Podoprikhin +3
In industrial machine learning pipelines, data often arrive in parts. Particularly in the case of deep neural networks, it may be too expensive to train the model from scratch each…
Uncertainty Estimation via Stochastic Batch Normalization
Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov +2
In this work, we investigate Batch Normalization technique and propose its probabilistic interpretation. We propose a probabilistic model and show that Batch Normalization maximaze…
Variance Networks: When Expectation Does Not Meet Your Expectations
Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha +1
Ordinary stochastic neural networks mostly rely on the expected values of their weights to make predictions, whereas the induced noise is mostly used to capture the uncertainty, pr…