4 citations · 7 across the 7 of their papers we have counts for
8 papers
Maestro: Joint Graph & Config Optimization for Reliable AI Agents
Wenxiao Wang, Priyatham Kattakinda, Soheil Feizi
Building reliable LLM agents requires decisions at two levels: the graph (which modules exist and how information flows) and the configuration of each node (models, prompts, tools,…
On Mechanistic Knowledge Localization in Text-to-Image Generative Models
Samyadeep Basu, Keivan Rezaei, Priyatham Kattakinda +5
Identifying layers within text-to-image models which control visual attributes can facilitate efficient model editing through closed-form updates. Recent work, leveraging causal tr…
Understanding the Effect of using Semantically Meaningful Tokens for Visual Representation Learning
Neha Kalibhat, Priyatham Kattakinda, Sumit Nawathe +5
Vision transformers have established a precedent of patchifying images into uniformly-sized chunks before processing. We hypothesize that this design choice may limit models in lea…
Rethinking Artistic Copyright Infringements in the Era of Text-to-Image Generative Models
Mazda Moayeri, Samyadeep Basu, Sriram Balasubramanian +4
Recent text-to-image generative models such as Stable Diffusion are extremely adept at mimicking and generating copyrighted content, raising concerns amongst artists that their uni…
Fast Adversarial Attacks on Language Models In One GPU Minute
Vinu Sankar Sadasivan, Shoumik Saha, Gaurang Sriramanan +3
In this paper, we introduce a novel class of fast, beam search-based adversarial attack (BEAST) for Language Models (LMs). BEAST employs interpretable parameters, enabling attacker…
Invariant Learning via Diffusion Dreamed Distribution Shifts
Priyatham Kattakinda, Alexander Levine, Soheil Feizi
Though the background is an important signal for image classification, over reliance on it can lead to incorrect predictions when spurious correlations between foreground and backg…