4 papers
Prox: Training-Free FFN Activation Sparsity via Approximate Intermediate-Channel Salience in LLMs
Jinyi Liu, Wei Chen, Pengyu Chen +4
The paper introduces Prox, a training-free framework that sparsifies feed‑forward network activations in large language models by approximating intermediate‑channel salience, enabl…
Demystifying Deep Learning Compiler Frontend Bugs: An LLM-Aided Empirical Study
Xinyi Yuan, Wei Chen, Jinyi Liu +5
Deep learning compilers (DLCs) are designed to translate deep learning programs into optimized, hardware-specific code. Typically, DLC frontends translate programs into graph-based…
Deep Reinforcement Learning for Automated Web GUI Testing
Zhiyu Gu, Chenxu Liu, Guoquan Wu +5
Automated GUI testing of web applications has always been considered a challenging task considering their large state space and complex interaction logic. Deep Reinforcement Learni…
Testing the Fault-Tolerance of Multi-Sensor Fusion Perception in Autonomous Driving Systems
Haoxiang Tian, Wenqiang Ding, Xingshuo Han +5
High-level Autonomous Driving Systems (ADSs), such as Google Waymo and Baidu Apollo, typically rely on multi-sensor fusion (MSF) based approaches to perceive their surroundings. Th…