2 citations · 3 across the 2 of their papers we have counts for
3 papers
Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision Post-Training Quantization
Clemens JS Schaefer, Navid Lambert-Shirzad, Xiaofan Zhang +7
Efficiently serving neural network models with low latency is becoming more challenging due to increasing model complexity and parameter count. Model quantization offers a solution…
Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression
Yihao Xue, Siddharth Joshi, Eric Gan +2
Contrastive learning (CL) has emerged as a powerful technique for representation learning, with or without label supervision. However, supervised CL is prone to collapsing represen…
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least
Siddharth Joshi, Baharan Mirzasoleiman
Self-supervised learning (SSL) learns high-quality representations from large pools of unlabeled training data. As datasets grow larger, it becomes crucial to identify the examples…