3 citations · 9 across the 6 of their papers we have counts for
6 papers
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…
Explaining and Adapting Graph Conditional Shift
Qi Zhu, Yizhu Jiao, Natalia Ponomareva +2
Graph Neural Networks (GNNs) have shown remarkable performance on graph-structured data. However, recent empirical studies suggest that GNNs are very susceptible to distribution sh…
COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search
Shibal Ibrahim, Wenyu Chen, Hussein Hazimeh +3
The sparse Mixture-of-Experts (Sparse-MoE) framework efficiently scales up model capacity in various domains, such as natural language processing and vision. Sparse-MoEs select a s…
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization
Riade Benbaki, Wenyu Chen, Xiang Meng +4
The sheer size of modern neural networks makes model serving a serious computational challenge. A popular class of compression techniques overcomes this challenge by pruning or spa…
Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets
Hussein Hazimeh, Natalia Ponomareva
Adversarial nets have proved to be powerful in various domains including generative modeling (GANs), transfer learning, and fairness. However, successfully training adversarial net…