54 citations · 108 across the 20 of their papers we have counts for
4 papers · 1 filter
Predicting Deep Neural Network Generalization with Perturbation Response Curves
Yair Schiff, Brian Quanz, Payel Das +1
The field of Deep Learning is rich with empirical evidence of human-like performance on a variety of prediction tasks. However, despite these successes, the recent Predicting Gener…
Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations
Yair Schiff, Vijil Chenthamarakshan, Samuel Hoffman +2
Deep generative models have emerged as a powerful tool for learning useful molecular representations and designing novel molecules with desired properties, with applications in dru…
Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks
David Alvarez-Melis, Yair Schiff, Youssef Mroueh
Gradient flows are a powerful tool for optimizing functionals in general metric spaces, including the space of probabilities endowed with the Wasserstein metric. A typical approach…
Gi and Pal Scores: Deep Neural Network Generalization Statistics
Yair Schiff, Brian Quanz, Payel Das +1
The field of Deep Learning is rich with empirical evidence of human-like performance on a variety of regression, classification, and control tasks. However, despite these successes…