3 citations · 4 across the 5 of their papers we have counts for
5 papers
mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization
Kayhan Behdin, Qingquan Song, Aman Gupta +7
Modern deep learning models are over-parameterized, where different optima can result in widely varying generalization performance. The Sharpness-Aware Minimization (SAM) technique…
Improved Deep Neural Network Generalization Using m-Sharpness-Aware Minimization
Kayhan Behdin, Qingquan Song, Aman Gupta +4
Modern deep learning models are over-parameterized, where the optimization setup strongly affects the generalization performance. A key element of reliable optimization for these s…
Heterogeneous Calibration: A post-hoc model-agnostic framework for improved generalization
David Durfee, Aman Gupta, Kinjal Basu
We introduce the notion of heterogeneous calibration that applies a post-hoc model-agnostic transformation to model outputs for improving AUC performance on binary classification t…
Logit Attenuating Weight Normalization
Aman Gupta, Rohan Ramanath, Jun Shi +4
Over-parameterized deep networks trained using gradient-based optimizers are a popular choice for solving classification and ranking problems. Without appropriately tuned …
Smoothed Gaussian Mixture Models for Video Classification and Recommendation
Sirjan Kafle, Aman Gupta, Xue Xia +4
Cluster-and-aggregate techniques such as Vector of Locally Aggregated Descriptors (VLAD), and their end-to-end discriminatively trained equivalents like NetVLAD have recently been…