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2020 – today
- 2023
[i4]Neha S. Wadia, Yatin Dandi, Michael I. Jordan:
A Gentle Introduction to Gradient-Based Optimization and Variational Inequalities for Machine Learning. CoRR abs/2309.04877 (2023)- 2022
- [b1]Neha S. Wadia:
Optimization: Stochastic thermodynamics, machine learning, and numerical algorithms. University of California, Berkeley, USA, 2022 - 2021
- [j1]Charles G. Frye, James Simon, Neha S. Wadia, Andrew Ligeralde, Michael Robert DeWeese, Kristofer E. Bouchard:
Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses. Neural Comput. 33(6): 1469-1497 (2021) - [c1]Neha S. Wadia, Daniel Duckworth, Samuel S. Schoenholz, Ethan Dyer, Jascha Sohl-Dickstein:
Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization. ICML 2021: 10617-10629 - 2020
- [i3]Charles G. Frye, James Simon, Neha S. Wadia, Andrew Ligeralde
, Michael Robert DeWeese, Kristofer E. Bouchard:
Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses. CoRR abs/2003.10397 (2020) - [i2]Neha S. Wadia, Daniel Duckworth, Samuel S. Schoenholz, Ethan Dyer, Jascha Sohl-Dickstein:
Whitening and second order optimization both destroy information about the dataset, and can make generalization impossible. CoRR abs/2008.07545 (2020)
2010 – 2019
- 2019
- [i1]Charles G. Frye, Neha S. Wadia, Michael Robert DeWeese, Kristofer E. Bouchard:
Numerically Recovering the Critical Points of a Deep Linear Autoencoder. CoRR abs/1901.10603 (2019)
Coauthor Index
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