Michal Klein

According to our database, Michal Klein authored at least 11 papers between 2022 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Controlling Language and Diffusion Models by Transporting Activations.

Pau Rodríguez López, Arno Blaas, Michal Klein, Luca Zappella, Nicholas Apostoloff, Marco Cuturi, Xavier Suau

CoRR, 2024

Simple ReFlow: Improved Techniques for Fast Flow Models.

Beomsu Kim, Yu-Guan Hsieh, Michal Klein, Marco Cuturi, Jong Chul Ye, Bahjat Kawar, James Thornton

CoRR, 2024

Progressive Entropic Optimal Transport Solvers.

Parnian Kassraie, Aram-Alexandre Pooladian, Michal Klein, James Thornton, Jonathan Niles-Weed, Marco Cuturi

CoRR, 2024

Contrasting Multiple Representations with the Multi-Marginal Matching Gap.

Zoe Piran, Michal Klein, James Thornton, Marco Cuturi

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Careful with that Scalpel: Improving Gradient Surgery with an EMA.

Yu-Guan Hsieh, James Thornton, Eugène Ndiaye, Michal Klein, Marco Cuturi, Pierre Ablin

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Scalable Pre-training of Large Autoregressive Image Models.

Alaaeldin El-Nouby, Michal Klein, Shuangfei Zhai, Miguel Ángel Bautista, Vaishaal Shankar, Alexander T. Toshev, Joshua M. Susskind, Armand Joulin

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Learning Costs for Structured Monge Displacements.

Michal Klein, Aram-Alexandre Pooladian, Pierre Ablin, Eugène Ndiaye, Jonathan Niles-Weed, Marco Cuturi

CoRR, 2023

Unbalanced Low-rank Optimal Transport Solvers.

Meyer Scetbon, Michal Klein, Giovanni Palla, Marco Cuturi

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps.

Marco Cuturi, Michal Klein, Pierre Ablin

Proceedings of the International Conference on Machine Learning, 2023

Predicting Ordinary Differential Equations with Transformers.

Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus

Proceedings of the International Conference on Machine Learning, 2023

2022

Discovering ordinary differential equations that govern time-series.

Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus

CoRR, 2022