3 citations · 6 across the 7 of their papers we have counts for
4 papers · 1 filter
Uncovering the Limits of Proof Sharing for Neural Networks
Kanak Das, Shubham Ugare, Bor-Yuh Evan Chang +3
Robustness verification of neural networks is increasingly important, due to their use in many critical domains. In certain scenarios, proof sharing has been shown to accelerate in…
DINGO: Constrained Inference for Diffusion LLMs
Tarun Suresh, Debangshu Banerjee, Shubham Ugare +2
Diffusion LLMs have emerged as a promising alternative to conventional autoregressive LLMs, offering significant potential for improved runtime efficiency. However, existing diffus…
ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs
Yuchen Yang, Yifan Zhao, Shubham Ugare +2
Mixed precision quantization has become an important technique for optimizing the execution of deep neural networks (DNNs). Certified robustness, which provides provable guarantees…
SynCode: LLM Generation with Grammar Augmentation
Shubham Ugare, Tarun Suresh, Hangoo Kang +2
LLMs are widely used in complex AI applications. These applications underscore the need for LLM outputs to adhere to a specific format, for their integration with other components…