Nate Gillman

According to our database, Nate Gillman authored at least 7 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Goal Force: Teaching Video Models To Accomplish Physics-Conditioned Goals.

Nate Gillman, Yinghua Zhou, Zitian Tang, Evan Luo, Arjan Chakravarthy, Daksh Aggarwal, Michael Freeman, Charles Herrmann, Chen Sun

CoRR, January, 2026

2025

Force Prompting: Video Generation Models Can Learn And Generalize Physics-based Control Signals.

Nate Gillman, Charles Herrmann, Michael Freeman, Daksh Aggarwal, Evan Luo, Deqing Sun, Chen Sun

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Fourier Head: Helping Large Language Models Learn Complex Probability Distributions.

Nate Gillman, Daksh Aggarwal, Michael Freeman, Chen Sun

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Fourier Head: Helping Large Language Models Learn Complex Probability Distributions.

Nate Gillman, Daksh Aggarwal, Michael Freeman, Saurabh Singh, Chen Sun

CoRR, 2024

Self-Correcting Self-Consuming Loops for Generative Model Training.

Nate Gillman, Michael Freeman, Daksh Aggarwal, Chia-Hong Hsu, Calvin Luo, Yonglong Tian, Chen Sun

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2022

IsoScore: Measuring the Uniformity of Embedding Space Utilization.

William Rudman, Nate Gillman, Taylor Rayne, Carsten Eickhoff

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2022, 2022

2021

IsoScore: Measuring the Uniformity of Vector Space Utilization.

William Rudman, Nate Gillman, Taylor Rayne, Carsten Eickhoff

CoRR, 2021