35 citations · 91 across the 22 of their papers we have counts for
5 papers · 1 filter
Using Cooperative Game Theory to Prune Neural Networks
Mauricio Diaz-Ortiz, Benjamin Kempinski, Daphne Cornelisse +2
We show how solution concepts from cooperative game theory can be used to tackle the problem of pruning neural networks. The ever-growing size of deep neural networks (DNNs) increa…
Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees
Alexia Jolicoeur-Martineau, Kilian Fatras, Tal Kachman
Tabular data is hard to acquire and is subject to missing values. This paper introduces a novel approach for generating and imputing mixed-type (continuous and categorical) tabular…
Explainability Techniques for Chemical Language Models
Stefan Hödl, William Robinson, Yoram Bachrach +2
Explainability techniques are crucial in gaining insights into the reasons behind the predictions of deep learning models, which have not yet been applied to chemical language mode…
Charting the Topography of the Neural Network Landscape with Thermal-Like Noise
Theo Jules, Gal Brener, Tal Kachman +2
The training of neural networks is a complex, high-dimensional, non-convex and noisy optimization problem whose theoretical understanding is interesting both from an applicative pe…
Diffusion models with location-scale noise
Alexia Jolicoeur-Martineau, Kilian Fatras, Ke Li +1
Diffusion Models (DMs) are powerful generative models that add Gaussian noise to the data and learn to remove it. We wanted to determine which noise distribution (Gaussian or non-G…