19 citations · 23 across the 3 of their papers we have counts for
4 papers
Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation
Yannick Hogewind, Thiago D. Simao, Tal Kachman +1
We address the problem of safe reinforcement learning from pixel observations. Inherent challenges in such settings are (1) a trade-off between reward optimization and adhering to…
Gotta Go Fast When Generating Data with Score-Based Models
Alexia Jolicoeur-Martineau, Ke Li, Rémi Piché-Taillefer +2
Score-based (denoising diffusion) generative models have recently gained a lot of success in generating realistic and diverse data. These approaches define a forward diffusion proc…
Novel Uncertainty Framework for Deep Learning Ensembles
Tal Kachman, Michal Moshkovitz, Michal Rosen-Zvi
Deep neural networks have become the default choice for many of the machine learning tasks such as classification and regression. Dropout, a method commonly used to improve the con…
Learning multiple non-mutually-exclusive tasks for improved classification of inherently ordered labels
Vadim Ratner, Yoel Shoshan, Tal Kachman
Medical image classification involves thresholding of labels that represent malignancy risk levels. Usually, a task defines a single threshold, and when developing computer-aided d…