1 citations · 1 across the 5 of their papers we have counts for
6 papers
Goal Force: Teaching Video Models To Accomplish Physics-Conditioned Goals
Nate Gillman, Yinghua Zhou, Zitian Tang +6
Recent advancements in video generation have enabled the development of ``world models'' capable of simulating potential futures for robotics and planning. However, specifying prec…
Assessing Factual Music Comprehension in Large Audio Language Models
Daniel Chenyu Lin, Michael Freeman, John Thickstun
Large audio language models (LALMs) leverage multimodal representations to generate open-ended answers to natural language queries about audio. In this paper, we (1) provide empiri…
Force Prompting: Video Generation Models Can Learn and Generalize Physics-based Control Signals
Nate Gillman, Charles Herrmann, Michael Freeman +4
Recent advances in video generation models have sparked interest in world models capable of simulating realistic environments. While navigation has been well-explored, physically m…
Fourier Head: Helping Large Language Models Learn Complex Probability Distributions
Nate Gillman, Daksh Aggarwal, Michael Freeman +2
As the quality of large language models has improved, there has been increased interest in using them to model non-linguistic tokens. For example, the Decision Transformer recasts…
Do Music Generation Models Encode Music Theory?
Megan Wei, Michael Freeman, Chris Donahue +1
Music foundation models possess impressive music generation capabilities. When people compose music, they may infuse their understanding of music into their work, by using notes an…
Self-Correcting Self-Consuming Loops for Generative Model Training
Nate Gillman, Michael Freeman, Daksh Aggarwal +4
As synthetic data becomes higher quality and proliferates on the internet, machine learning models are increasingly trained on a mix of human- and machine-generated data. Despite t…