12 citations · 32 across the 20 of their papers we have counts for
5 papers · 1 filter
End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver
Shaocong Ma, James Diffenderfer, Bhavya Kailkhura +1
Deep learning has been widely applied to solve partial differential equations (PDEs) in computational fluid dynamics. Recent research proposed a PDE correction framework that lever…
SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning
Jinghan Jia, Yihua Zhang, Yimeng Zhang +5
Large Language Models (LLMs) have highlighted the necessity of effective unlearning mechanisms to comply with data regulations and ethical AI practices. LLM unlearning aims at remo…
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies
Brian R. Bartoldson, James Diffenderfer, Konstantinos Parasyris +1
This paper revisits the simple, long-studied, yet still unsolved problem of making image classifiers robust to imperceptible perturbations. Taking CIFAR10 as an example, SOTA clean…
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression
Junyuan Hong, Jinhao Duan, Chenhui Zhang +12
Compressing high-capability Large Language Models (LLMs) has emerged as a favored strategy for resource-efficient inferences. While state-of-the-art (SoTA) compression methods boas…
GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations
Jinhao Duan, Renming Zhang, James Diffenderfer +6
As Large Language Models (LLMs) are integrated into critical real-world applications, their strategic and logical reasoning abilities are increasingly crucial. This paper evaluates…