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02/10/2023
Star-Shaped Denoising Diffusion Probabilistic Models
Methods based on Denoising Diffusion Probabilistic Models (DDPM) became ...
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06/11/2020
Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks
Training neural networks with binary weights and activations is a challe...
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02/21/2020
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation
Test-time data augmentation—averaging the predictions of a machine learn...
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02/15/2020
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
Uncertainty estimation and ensembling methods go hand-in-hand. Uncertain...
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11/01/2018
Variational Dropout via Empirical Bayes
We study the Automatic Relevance Determination procedure applied to deep...
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10/05/2018
Doubly Semi-Implicit Variational Inference
We extend the existing framework of semi-implicit variational inference ...
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03/10/2018
Variance Networks: When Expectation Does Not Meet Your Expectations
In this paper, we propose variance networks, a new model that stores the...
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02/20/2018
Bayesian Incremental Learning for Deep Neural Networks
In industrial machine learning pipelines, data often arrive in parts. Pa...
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02/13/2018
Uncertainty Estimation via Stochastic Batch Normalization
In this work, we investigate Batch Normalization technique and propose i...
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05/20/2017
Structured Bayesian Pruning via Log-Normal Multiplicative Noise
Dropout-based regularization methods can be regarded as injecting random...
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01/19/2017