4 papers
An accurate detection is not all you need to combat label noise in web-noisy datasets
Paul Albert, Jack Valmadre, Eric Arazo +3
Training a classifier on web-crawled data demands learning algorithms that are robust to annotation errors and irrelevant examples. This paper builds upon the recent empirical obse…
Dataset Clustering for Improved Offline Policy Learning
Qiang Wang, Yixin Deng, Francisco Roldan Sanchez +4
Offline policy learning aims to discover decision-making policies from previously-collected datasets without additional online interactions with the environment. As the training da…
Learning Saliency From Fixations
Yasser Abdelaziz Dahou Djilali, Kevin McGuiness, Noel O'Connor
We present a novel approach for saliency prediction in images, leveraging parallel decoding in transformers to learn saliency solely from fixation maps. Models typically rely on co…
Learning and reusing primitive behaviours to improve Hindsight Experience Replay sample efficiency
Francisco Roldan Sanchez, Qiang Wang, David Cordova Bulens +3
Hindsight Experience Replay (HER) is a technique used in reinforcement learning (RL) that has proven to be very efficient for training off-policy RL-based agents to solve goal-base…