Computer vision-based recognition of liquid surfaces and phase boundaries in transparent vessels, with emphasis on chemistry applications. - Sagi Eppel; Tal Kachman (CoRR, 2014)

Numerical implementation of the multiscale and averaging methods for quasi periodic systems. - Tal Kachman; Shmuel Fishman; Avy Soffer (Comput. Phys. Commun., 2017)

Learning multiple non-mutually-exclusive tasks for improved classification of inherently ordered labels. - Vadim Ratner; Yoel Shoshan; Tal Kachman (CoRR, 2018)

Novel Uncertainty Framework for Deep Learning Ensembles. - Tal Kachman; Michal Moshkovitz; Michal Rosen-Zvi (CoRR, 2019)

Gotta Go Fast When Generating Data with Score-Based Models. - Alexia Jolicoeur-Martineau; Ke Li 011; Rémi Piché-Taillefer; Tal Kachman; Ioannis Mitliagkas (CoRR, 2021)

Gradients are Not All You Need. - Luke Metz; C. Daniel Freeman; Samuel S. Schoenholz; Tal Kachman (CoRR, 2021)

Lyapunov Exponents for Diversity in Differentiable Games. - Jonathan Lorraine; Paul Vicol; Jack Parker-Holder; Tal Kachman; Luke Metz; Jakob N. Foerster (AAMAS, 2022)

Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members. - Daphne Cornelisse; Thomas Rood; Yoram Bachrach; Mateusz Malinowski; Tal Kachman (NeurIPS, 2022)

Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation. - Yannick Hogewind; Thiago D. Simão; Tal Kachman; Nils Jansen 001 (ICLR, 2023)

Charting the Topography of the Neural Network Landscape with Thermal-Like Noise. - Théo Jules; Gal Brener; Tal Kachman; Noam Levi; Yohai Bar-Sinai (CoRR, 2023)

Diffusion models with location-scale noise. - Alexia Jolicoeur-Martineau; Kilian Fatras; Ke Li 011; Tal Kachman (CoRR, 2023)

Explainability Techniques for Chemical Language Models. - Stefan Hödl; William E. Robinson; Yoram Bachrach; Wilhelm T. S. Huck; Tal Kachman (CoRR, 2023)

Using Cooperative Game Theory to Prune Neural Networks. - Mauricio Diaz-Ortiz Jr.; Benjamin Kempinski; Daphne Cornelisse; Yoram Bachrach; Tal Kachman (CoRR, 2023)

Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees. - Alexia Jolicoeur-Martineau; Kilian Fatras; Tal Kachman (AISTATS, 2024)

Pruning Neural Networks Using Cooperative Game Theory. - Mauricio Diaz-Ortiz Jr.; Benjamin Kempinski; Daphne Cornelisse; Yoram Bachrach; Tal Kachman (AAMAS, 2024)

Game of Thoughts: Iterative Reasoning in Game-Theoretic Domains with Large Language Models. - Benjamin Kempinski; Ian Gemp; Kate Larson; Marc Lanctot; Yoram Bachrach; Tal Kachman (AAMAS, 2025)

Modeling Chemical Reaction Networks Using Neural Ordinary Differential Equations. - Anna C. M. Thöni; William E. Robinson; Yoram Bachrach; Wilhelm T. S. Huck; Tal Kachman (J. Chem. Inf. Model., 2025)

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations. - Anna C. M. Thöni; William E. Robinson; Yoram Bachrach; Wilhelm T. S. Huck; Tal Kachman (CoRR, 2025)

InfluenceNet: AI Models for Banzhaf and Shapley Value Prediction. - Benjamin Kempinski; Tal Kachman (CoRR, 2025)

Modelling Mean-Field Games with Neural Ordinary Differential Equations. - Anna C. M. Thöni; Yoram Bachrach; Tal Kachman (CoRR, 2025)