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Atish Agarwala
Atish Agarwala
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Cited by
Cited by
Year
Development of a comprehensive genotype-to-fitness map of adaptation-driving mutations in yeast
S Venkataram, B Dunn, Y Li, A Agarwala, J Chang, ER Ebel, ...
Cell 166 (6), 1585-1596. e22, 2016
2092016
Stabilization of extensive fine-scale diversity by ecologically driven spatiotemporal chaos
MT Pearce, A Agarwala, DS Fisher
Proceedings of the National Academy of Sciences 117 (25), 14572-14583, 2020
912020
Hidden complexity of yeast adaptation under simple evolutionary conditions
Y Li, S Venkataram, A Agarwala, B Dunn, DA Petrov, G Sherlock, ...
Current Biology 28 (4), 515-525. e6, 2018
602018
Learning Dota 2 team compositions
A Agarwala, M Pearce
Sl: sn, 2014
412014
Adaptive walks on high-dimensional fitness landscapes and seascapes with distance-dependent statistics
A Agarwala, DS Fisher
Theoretical population biology 130, 13-49, 2019
352019
Temperature check: theory and practice for training models with softmax-cross-entropy losses
A Agarwala, J Pennington, Y Dauphin, S Schoenholz
arXiv preprint arXiv:2010.07344, 2020
222020
Second-order regression models exhibit progressive sharpening to the edge of stability
A Agarwala, F Pedregosa, J Pennington
arXiv preprint arXiv:2210.04860, 2022
182022
SAM operates far from home: eigenvalue regularization as a dynamical phenomenon
A Agarwala, Y Dauphin
International Conference on Machine Learning, 152-168, 2023
142023
One network fits all? modular versus monolithic task formulations in neural networks
A Agarwala, A Das, B Juba, R Panigrahy, V Sharan, X Wang, Q Zhang
arXiv preprint arXiv:2103.15261, 2021
132021
Hé rissant L, Blundell JR, Levy SF, Fisher DS, Sherlock G, Petrov DA. 2016. Development of a comprehensive genotype-to-fitness map of adaptation-driving mutations in yeast
S Venkataram, B Dunn, Y Li, A Agarwala, J Chang, ER Ebel, ...
Cell 166, 1585-1596, 0
7
Tuned Fitness Landscapes for Benchmarking Model-Guided Protein Design
N Thomas, A Agarwala, D Belanger, YS Song, LJ Colwell
bioRxiv, 2022.10. 28.514293, 2022
42022
Deep equilibrium networks are sensitive to initialization statistics
A Agarwala, SS Schoenholz
International Conference on Machine Learning, 136-160, 2022
42022
Evolution of haploid and diploid populations reveals common, strong, and variable pleiotropic effects in non-home environments
V Chen, MS Johnson, L Hérissant, PT Humphrey, DC Yuan, Y Li, ...
Elife 12, 2023
22023
Gradient descent induces alignment between weights and the empirical NTK for deep non-linear networks
D Beaglehole, I Mitliagkas, A Agarwala
arXiv preprint arXiv:2402.05271, 2024
12024
Neglected Hessian component explains mysteries in Sharpness regularization
YN Dauphin, A Agarwala, H Mobahi
arXiv preprint arXiv:2401.10809, 2024
12024
On the Interplay Between Stepsize Tuning and Progressive Sharpening
V Roulet, A Agarwala, F Pedregosa
OPT 2023: Optimization for Machine Learning, 2023
12023
Far From Home: Evolution of haploid and diploid populations reveals common, strong, and variable pleiotropic effects in non-home environments
VK Chen, MS Johnson, L Hérissant, PT Humphrey, DC Yuan, Y Li, ...
bioRxiv, 2023.02. 28.530341, 2023
12023
Learning the gravitational force law and other analytic functions
A Agarwala, A Das, R Panigrahy, Q Zhang
arXiv preprint arXiv:2005.07724, 2020
12020
High dimensional analysis reveals conservative sharpening and a stochastic edge of stability
A Agarwala, J Pennington
arXiv preprint arXiv:2404.19261, 2024
2024
How Hessian structure explains mysteries in sharpness regularization
Y Dauphin, A Agarwala, H Mobahi
2023
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