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Kyle Matoba
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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)
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last updated on 2024-08-10 01:23 CEST by the dblp team
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