215 citations · 453 across the 21 of their papers we have counts for
8 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…
lo-fi: distributed fine-tuning without communication
Mitchell Wortsman, Suchin Gururangan, Shen Li +4
When fine-tuning large neural networks, it is common to use multiple nodes and to communicate gradients at each optimization step. By contrast, we investigate completely local fine…
LCS: Learning Compressible Subspaces for Adaptive Network Compression at Inference Time
Elvis Nunez, Maxwell Horton, Anish Prabhu +3
When deploying deep learning models to a device, it is traditionally assumed that available computational resources (compute, memory, and power) remain static. However, real-world…
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes
Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan +6
Learning binary representations of instances and classes is a classical problem with several high potential applications. In modern settings, the compression of high-dimensional ne…
Learning Neural Network Subspaces
Mitchell Wortsman, Maxwell Horton, Carlos Guestrin +2
Recent observations have advanced our understanding of the neural network optimization landscape, revealing the existence of (1) paths of high accuracy containing diverse solutions…
Supermasks in Superposition
Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu +4
We present the Supermasks in Superposition (SupSup) model, capable of sequentially learning thousands of tasks without catastrophic forgetting. Our approach uses a randomly initial…