Search Results for author:
Found 12 papers, 9 papers with code
Automating Control of Overestimation Bias for Reinforcement Learning
Overestimation bias control techniques are used by the majority of high-performing off-policy reinforcement learning algorithms.
Resolution-robust Large Mask Inpainting with Fourier Convolutions
We find that one of the main reasons for that is the lack of an effective receptive field in both the inpainting network and the loss function.
Ranked #3 on
Seeing Beyond the Visible
on KITTI360-EX
Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations
Averaging predictions over a set of models -- an ensemble -- is widely used to improve predictive performance and uncertainty estimation of deep learning models.
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation
Test-time data augmentation$-$averaging the predictions of a machine learning model across multiple augmented samples of data$-$is a widely used technique that improves the predictive performance.
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
Uncertainty estimation and ensembling methods go hand-in-hand.
Semi-Conditional Normalizing Flows for Semi-Supervised Learning
This paper proposes a semi-conditional normalizing flow model for semi-supervised learning.
The Deep Weight Prior
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 distribution.
Variance Networks: When Expectation Does Not Meet Your Expectations
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, prevent overfitting and slightly boost the performance through test-time averaging.
Bayesian Incremental Learning for Deep Neural Networks
In industrial machine learning pipelines, data often arrive in parts.
Uncertainty Estimation via Stochastic Batch Normalization
In this work, we investigate Batch Normalization technique and propose its probabilistic interpretation.
Structured Bayesian Pruning via Log-Normal Multiplicative Noise
In the paper, we propose a new Bayesian model that takes into account the computational structure of neural networks and provides structured sparsity, e. g. removes neurons and/or convolutional channels in CNNs.
Variational Dropout Sparsifies Deep Neural Networks
We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout.
