60 citations · 118 across the 6 of their papers we have counts for
10 papers
Greener yet Powerful: Taming Large Code Generation Models with Quantization
Xiaokai Wei, Sujan Gonugondla, Wasi Ahmad +13
ML-powered code generation aims to assist developers to write code in a more productive manner, by intelligently generating code blocks based on natural language prompts. Recently,…
Adaptive Verifiable Training Using Pairwise Class Similarity
Shiqi Wang, Kevin Eykholt, Taesung Lee +2
Verifiable training has shown success in creating neural networks that are provably robust to a given amount of noise. However, despite only enforcing a single robustness criterion…
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers
Kaidi Xu, Huan Zhang, Shiqi Wang +4
Formal verification of neural networks (NNs) is a challenging and important problem. Existing efficient complete solvers typically require the branch-and-bound (BaB) process, which…
Towards Understanding Fast Adversarial Training
Bai Li, Shiqi Wang, Suman Jana +1
Current neural-network-based classifiers are susceptible to adversarial examples. The most empirically successful approach to defending against such adversarial examples is adversa…
Towards Practical Lottery Ticket Hypothesis for Adversarial Training
Bai Li, Shiqi Wang, Yunhan Jia +4
Recent research has proposed the lottery ticket hypothesis, suggesting that for a deep neural network, there exist trainable sub-networks performing equally or better than the orig…
Towards Compact and Robust Deep Neural Networks
Vikash Sehwag, Shiqi Wang, Prateek Mittal +1
Deep neural networks have achieved impressive performance in many applications but their large number of parameters lead to significant computational and storage overheads. Several…