157 citations · 227 across the 19 of their papers we have counts for
3 papers · 1 filter
OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization
Xiang Meng, Shibal Ibrahim, Kayhan Behdin +3
Structured pruning is a promising approach for reducing the inference costs of large vision and language models. By removing carefully chosen structures, e.g., neurons or attention…
DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation
Yunjuan Wang, Hussein Hazimeh, Natalia Ponomareva +3
Distribution shifts and adversarial examples are two major challenges for deploying machine learning models. While these challenges have been studied individually, their combinatio…
Scaling Laws for Downstream Task Performance of Large Language Models
Berivan Isik, Natalia Ponomareva, Hussein Hazimeh +3
Scaling laws provide important insights that can guide the design of large language models (LLMs). Existing work has primarily focused on studying scaling laws for pretraining (ups…