331 citations · 655 across the 11 of their papers we have counts for
12 papers
Automating Control of Overestimation Bias for Reinforcement Learning
Arsenii Kuznetsov, Alexander Grishin, Artem Tsypin +3
Overestimation bias control techniques are used by the majority of high-performing off-policy reinforcement learning algorithms. However, most of these techniques rely on pre-defin…
Resolution-robust Large Mask Inpainting with Fourier Convolutions
Roman Suvorov, Elizaveta Logacheva, Anton Mashikhin +7
Modern image inpainting systems, despite the significant progress, often struggle with large missing areas, complex geometric structures, and high-resolution images. We find that o…
Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations
Arsenii Ashukha, Andrei Atanov, Dmitry Vetrov
Averaging predictions over a set of models -- an ensemble -- is widely used to improve predictive performance and uncertainty estimation of deep learning models. At the same time,…
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…
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov +1
Uncertainty estimation and ensembling methods go hand-in-hand. Uncertainty estimation is one of the main benchmarks for assessment of ensembling performance. At the same time, deep…
Semi-Conditional Normalizing Flows for Semi-Supervised Learning
Andrei Atanov, Alexandra Volokhova, Arsenii Ashukha +2
This paper proposes a semi-conditional normalizing flow model for semi-supervised learning. The model uses both labelled and unlabeled data to learn an explicit model of joint dist…
The Deep Weight Prior
Andrei Atanov, Arsenii Ashukha, Kirill Struminsky +2
Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior dist…
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…
Structured Bayesian Pruning via Log-Normal Multiplicative Noise
Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha +1
Dropout-based regularization methods can be regarded as injecting random noise with pre-defined magnitude to different parts of the neural network during training. It was recently…
Variational Dropout Sparsifies Deep Neural Networks
Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov
We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout. We extend Variational Dropout to the case when dr…