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Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation
Test-time data augmentation—averaging the predictions of a machine learn...
02/21/2020 ∙ by Dmitry Molchanov, et al. ∙
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Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
Uncertainty estimation and ensembling methods go hand-in-hand. Uncertain...
02/15/2020 ∙ by Arsenii Ashukha, et al. ∙
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Semi-Conditional Normalizing Flows for Semi-Supervised Learning
This paper proposes a semi-conditional normalizing flow model for semi-s...
05/01/2019 ∙ by Andrei Atanov, et al. ∙
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The Deep Weight Prior. Modeling a prior distribution for CNNs using generative models
Bayesian inference is known to provide a general framework for incorpora...
10/16/2018 ∙ by Andrei Atanov, et al. ∙
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Variance Networks: When Expectation Does Not Meet Your Expectations
In this paper, we propose variance networks, a new model that stores the...
03/10/2018 ∙ by Kirill Neklyudov, et al. ∙
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Bayesian Incremental Learning for Deep Neural Networks
In industrial machine learning pipelines, data often arrive in parts. Pa...
02/20/2018 ∙ by Max Kochurov, et al. ∙
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Uncertainty Estimation via Stochastic Batch Normalization
In this work, we investigate Batch Normalization technique and propose i...
02/13/2018 ∙ by Andrei Atanov, et al. ∙
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Structured Bayesian Pruning via Log-Normal Multiplicative Noise
Dropout-based regularization methods can be regarded as injecting random...
05/20/2017 ∙ by Kirill Neklyudov, et al. ∙
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Variational Dropout Sparsifies Deep Neural Networks
We explore a recently proposed Variational Dropout technique that provid...
01/19/2017 ∙ by Dmitry Molchanov, et al. ∙
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