39 citations · 135 across the 14 of their papers we have counts for
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cs.LG2021
Balancing Robustness and Sensitivity using Feature Contrastive Learning
Seungyeon Kim, Daniel Glasner, Srikumar Ramalingam +3
It is generally believed that robust training of extremely large networks is critical to their success in real-world applications. However, when taken to the extreme, methods that…
cs.LG2021★ 17 cited
On the Reproducibility of Neural Network Predictions
Srinadh Bhojanapalli, Kimberly Wilber, Andreas Veit +4
Standard training techniques for neural networks involve multiple sources of randomness, e.g., initialization, mini-batch ordering and in some cases data augmentation. Given that n…