14 citations · 37 across the 8 of their papers we have counts for
10 papers
Contrastively Disentangled Sequential Variational Autoencoder
Junwen Bai, Weiran Wang, Carla Gomes
Self-supervised disentangled representation learning is a critical task in sequence modeling. The learnt representations contribute to better model interpretability as well as the…
Sparse Bayesian Learning via Stepwise Regression
Sebastian Ament, Carla Gomes
Sparse Bayesian Learning (SBL) is a powerful framework for attaining sparsity in probabilistic models. Herein, we propose a coordinate ascent algorithm for SBL termed Relevance Mat…
On the Optimality of Backward Regression: Sparse Recovery and Subset Selection
Sebatian Ament, Carla Gomes
Sparse recovery and subset selection are fundamental problems in varied communities, including signal processing, statistics and machine learning. Herein, we focus on an important…
HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders
Wenting Zhao, Shufeng Kong, Junwen Bai +2
Understanding how environmental characteristics affect bio-diversity patterns, from individual species to communities of species, is critical for mitigating effects of global chang…
Evaluating Multi-label Classifiers with Noisy Labels
Wenting Zhao, Carla Gomes
Multi-label classification (MLC) is a generalization of standard classification where multiple labels may be assigned to a given sample. In the real world, it is more common to dea…
Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision
Johan Bjorck, Xiangyu Chen, Christopher De Sa +2
Low-precision training has become a popular approach to reduce compute requirements, memory footprint, and energy consumption in supervised learning. In contrast, this promising ap…