Kyle Matoba

According to our database, Kyle Matoba authored at least 9 papers between 2014 and 2024.

Collaborative distances:

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

2024

Inference from Real-World Sparse Measurements.

Arnaud Pannatier, Kyle Matoba, François Fleuret

Trans. Mach. Learn. Res., 2024

2023

Benefits of Max Pooling in Neural Networks: Theoretical and Experimental Evidence.

Kyle Matoba, Nikolaos Dimitriadis, François Fleuret

Trans. Mach. Learn. Res., 2023

MEDITRON-70B: Scaling Medical Pretraining for Large Language Models.

Zeming Chen, Alejandro Hernández-Cano, Angelika Romanou, Antoine Bonnet, Kyle Matoba, Francesco Salvi, Matteo Pagliardini, Simin Fan, Andreas Köpf, Amirkeivan Mohtashami, Alexandre Sallinen, Alireza Sakhaeirad, Vinitra Swamy, Igor Krawczuk, Deniz Bayazit, Axel Marmet, Syrielle Montariol, Mary-Anne Hartley, Martin Jaggi, Antoine Bosselut

CoRR, 2023

2022

Accurate Extrinsic Prediction of Physical Systems Using Transformers.

Arnaud Pannatier, Kyle Matoba, François Fleuret

CoRR, 2022

Flatten the Curve: Efficiently Training Low-Curvature Neural Networks.

Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, François Fleuret

CoRR, 2022

The Theoretical Expressiveness of Maxpooling.

Kyle Matoba, Nikolaos Dimitriadis, François Fleuret

CoRR, 2022

Efficient Training of Low-Curvature Neural Networks.

Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, François Fleuret

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Challenges for Using Impact Regularizers to Avoid Negative Side Effects.

David Lindner, Kyle Matoba, Alexander Meulemans

Proceedings of the Workshop on Artificial Intelligence Safety 2021 (SafeAI 2021) co-located with the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021), 2021

2014

A computable figure of merit for quasi-Monte Carlo point sets.

Makoto Matsumoto, Mutsuo Saito, Kyle Matoba

Math. Comput., 2014