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2020 – today
- 2024
[j3]Arnaud Pannatier, Kyle Matoba, François Fleuret:
Inference from Real-World Sparse Measurements. Trans. Mach. Learn. Res. 2024 (2024)- 2023
- [j2]Kyle Matoba, Nikolaos Dimitriadis, François Fleuret:
Benefits of Max Pooling in Neural Networks: Theoretical and Experimental Evidence. Trans. Mach. Learn. Res. 2023 (2023) - [i5]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:
MEDITRON-70B: Scaling Medical Pretraining for Large Language Models. CoRR abs/2311.16079 (2023) - 2022
- [c2]Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, François Fleuret:
Efficient Training of Low-Curvature Neural Networks. NeurIPS 2022 - [i4]Kyle Matoba, Nikolaos Dimitriadis, François Fleuret:
The Theoretical Expressiveness of Maxpooling. CoRR abs/2203.01016 (2022) - [i3]Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, François Fleuret:
Flatten the Curve: Efficiently Training Low-Curvature Neural Networks. CoRR abs/2206.07144 (2022) - [i2]Arnaud Pannatier, Kyle Matoba, François Fleuret:
Accurate Extrinsic Prediction of Physical Systems Using Transformers. CoRR abs/2210.11269 (2022) - 2021
- [c1]David Lindner, Kyle Matoba, Alexander Meulemans:
Challenges for Using Impact Regularizers to Avoid Negative Side Effects. SafeAI@AAAI 2021 - [i1]David Lindner, Kyle Matoba, Alexander Meulemans:
Challenges for Using Impact Regularizers to Avoid Negative Side Effects. CoRR abs/2101.12509 (2021)
2010 – 2019
- 2014
- [j1]Makoto Matsumoto, Mutsuo Saito, Kyle Matoba:
A computable figure of merit for quasi-Monte Carlo point sets. Math. Comput. 83(287): 1233-1250 (2014)
Coauthor Index
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