21 citations · 53 across the 27 of their papers we have counts for
28 papers · 1 filter
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
ExecTune: Effective Steering of Black-Box LLMs with Guide Models
Vijay Lingam, Aditya Golatkar, Anwesan Pal +6
For large language models deployed through black-box APIs, recurring inference costs often exceed one-time training costs. This motivates composed agentic systems that amortize exp…
Compositional Structures in Neural Embedding and Interaction Decompositions
Matthew Trager, Alessandro Achille, Pramuditha Perera +2
We describe a basic correspondence between linear algebraic structures within vector embeddings in artificial neural networks and conditional independence constraints on the probab…
B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory
Luca Zancato, Arjun Seshadri, Yonatan Dukler +6
We describe a family of architectures to support transductive inference by allowing memory to grow to a finite but a-priori unknown bound while making efficient use of finite resou…
Critical Learning Periods Emerge Even in Deep Linear Networks
Michael Kleinman, Alessandro Achille, Stefano Soatto
Critical learning periods are periods early in development where temporary sensory deficits can have a permanent effect on behavior and learned representations. Despite the radical…
Training Data Protection with Compositional Diffusion Models
Aditya Golatkar, Alessandro Achille, Ashwin Swaminathan +1
We introduce Compartmentalized Diffusion Models (CDM), a method to train different diffusion models (or prompts) on distinct data sources and arbitrarily compose them at inference…