4 papers · 1 filter
Learning Generalizable Neural Operators for Inverse Problems
Adam J. Thorpe, Stepan Tretiakov, Dibakar Roy Sarkar +2
Inverse problems challenge existing neural operator architectures because ill-posed inverse maps violate continuity, uniqueness, and stability assumptions. We introduce B2B${}^{-1}…
Stability of Transformers under Layer Normalization
Kelvin Kan, Xingjian Li, Benjamin J. Zhang +4
Despite their widespread use, training deep Transformers can be unstable. Layer normalization, a standard component, improves training stability, but its placement has often been a…
AdversariaL attacK sAfety aLIgnment(ALKALI): Safeguarding LLMs through GRACE: Geometric Representation-Aware Contrastive Enhancement- Introducing Adversarial Vulnerability Quality Index (AVQI)
Danush Khanna, Gurucharan Marthi Krishna Kumar, Basab Ghosh +5
Adversarial threats against LLMs are escalating faster than current defenses can adapt. We expose a critical geometric blind spot in alignment: adversarial prompts exploit latent c…
MLPs and KANs for data-driven learning in physical problems: A performance comparison
Raghav Pant, Sikan Li, Xingjian Li +2
There is increasing interest in solving partial differential equations (PDEs) by casting them as machine learning problems. Recently, there has been a spike in exploring Kolmogorov…