Preetum Nakkiran
Apple (Israel)(IL)Apple (United States)(US)
Publications by Year
Research Areas
Machine Learning and Algorithms, Adversarial Robustness in Machine Learning, Machine Learning and Data Classification, Neural Networks and Applications, Stochastic Gradient Optimization Techniques
Most-Cited Works
- → Deep double descent: where bigger models and more data hurt*(2021)149 cited
- → Having your cake and eating it too: jointly optimal erasure codes for I/O, storage and network-bandwidth(2015)123 cited
- → Automatic gain control and multi-style training for robust small-footprint keyword spotting with deep neural networks(2015)96 cited
- → Adversarial Robustness May Be at Odds With Simplicity(2019)75 cited
- → Compressing deep neural networks using a rank-constrained topology(2015)74 cited
- SGD on Neural Networks Learns Functions of Increasing Complexity(2019)
- → Optimal Regularization Can Mitigate Double Descent(2020)48 cited
- → A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features'(2019)43 cited
- → More Data Can Hurt for Linear Regression: Sample-wise Double Descent(2019)42 cited