96 citations · 140 across the 2 of their papers we have counts for
11 papers
Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers
Piotr Teterwak, Chiyuan Zhang, Dilip Krishnan +1
A discriminatively trained neural net classifier can fit the training data perfectly if all information about its input other than class membership has been discarded prior to the…
Deep Learning with Label Differential Privacy
Badih Ghazi, Noah Golowich, Ravi Kumar +2
The Randomized Response (RR) algorithm is a classical technique to improve robustness in survey aggregation, and has been widely adopted in applications with differential privacy g…
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation
Vitaly Feldman, Chiyuan Zhang
Deep learning algorithms are well-known to have a propensity for fitting the training data very well and often fit even outliers and mislabeled data points. Such fitting requires m…
What is being transferred in transfer learning?
Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang
One desired capability for machines is the ability to transfer their knowledge of one domain to another where data is (usually) scarce. Despite ample adaptation of transfer learnin…
Transfusion: Understanding Transfer Learning for Medical Imaging
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg +1
Transfer learning from natural image datasets, particularly ImageNet, using standard large models and corresponding pretrained weights has become a de-facto method for deep learnin…
Identity Crisis: Memorization and Generalization under Extreme Overparameterization
Chiyuan Zhang, Samy Bengio, Moritz Hardt +2
We study the interplay between memorization and generalization of overparameterized networks in the extreme case of a single training example and an identity-mapping task. We exami…