310 citations · 539 across the 55 of their papers we have counts for
13 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…
Evaluation of ML Resource Utilization Requires Model Life Cycle Assessment
Jared Fernandez, Clara Na, Yonatan Bisk +2
Proper accounting of the energy requirements and environmental impact of artificial intelligence (AI) systems is necessary for researchers, developers, policy makers, and users to…
Kinetics: Rethinking Test-Time Scaling Laws
Ranajoy Sadhukhan, Zhuoming Chen, Haizhong Zheng +3
We rethink test-time scaling laws from a practical efficiency perspective, revealing that the effectiveness of smaller models is significantly overestimated. Prior work, grounded i…
Expert Routing with Synthetic Data for Continual Learning
Yewon Byun, Sanket Vaibhav Mehta, Saurabh Garg +4
In many real-world settings, regulations and economic incentives permit the sharing of models but not data across institutional boundaries. In such scenarios, practitioners might h…
Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training
Jared Fernandez, Luca Wehrstedt, Leonid Shamis +5
Dramatic increases in the capabilities of neural network models in recent years are driven by scaling model size, training data, and corresponding computational resources. To devel…
What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions
Sang Keun Choe, Hwijeen Ahn, Juhan Bae +11
Large language models (LLMs) are trained on a vast amount of human-written data, but data providers often remain uncredited. In response to this issue, data valuation (or data attr…