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Zezhou Wang

2 accepted papers

2026

V-Pruner: A Fast and Globally-informed Token Pruning Framework for Vision Transformer

AAAI 2026technical

Vision Transformer (ViT) has become one of the cornerstones of the computer vision field, demonstrating exceptional performance. However, its inherent high computational complexity and inference latency still pose significant obstacles for deployment in resource-constrained environments. Token pruni

Cited by 0SourcePDFScholar
2025

Optimizing Cross-Client Domain Coverage for Federated Instruction Tuning of Large Language Models

EMNLP 2025

Federated domain-specific instruction tuning (FedDIT) for large language models (LLMs) aims to enhance performance in specialized domains using distributed private and limited data, yet identifying key performance drivers and optimal augmentation strategies remains challenging. We empirically establ

Cited by 0SourcePDFScholar