Aaron Lou

According to our database, Aaron Lou authored at least 15 papers between 2018 and 2024.

Collaborative distances:

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

2024

Equivariant Graph Neural Operator for Modeling 3D Dynamics.

Minkai Xu, Jiaqi Han, Aaron Lou, Jean Kossaifi, Arvind Ramanathan, Kamyar Azizzadenesheli, Jure Leskovec, Stefano Ermon, Anima Anandkumar

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

Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution.

Aaron Lou, Chenlin Meng, Stefano Ermon

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

Denoising Diffusion Bridge Models.

Linqi Zhou, Aaron Lou, Samar Khanna, Stefano Ermon

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Diffusion Model Alignment Using Direct Preference Optimization.

Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, Nikhil Naik

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

Scaling Riemannian Diffusion Models.

Aaron Lou, Minkai Xu, Stefano Ermon

CoRR, 2023

Discrete Diffusion Language Modeling by Estimating the Ratios of the Data Distribution.

Aaron Lou, Chenlin Meng, Stefano Ermon

CoRR, 2023

Riemannian Residual Neural Networks.

Isay Katsman, Eric Ming Chen, Sidhanth Holalkere, Anna Asch, Aaron Lou, Ser-Nam Lim, Christopher De Sa

CoRR, 2023

Scaling Riemannian Diffusion Models.

Aaron Lou, Minkai Xu, Adam Farris, Stefano Ermon

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Riemannian Residual Neural Networks.

Isay Katsman, Eric Chen, Sidhanth Holalkere, Anna Asch, Aaron Lou, Ser Nam Lim, Christopher De Sa

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Reflected Diffusion Models.

Aaron Lou, Stefano Ermon

Proceedings of the International Conference on Machine Learning, 2023

2021

Equivariant Manifold Flows.

Isay Katsman, Aaron Lou, Derek Lim, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks.

Tolga Birdal, Aaron Lou, Leonidas J. Guibas, Umut Simsekli

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Neural Manifold Ordinary Differential Equations.

Aaron Lou, Derek Lim, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Differentiating through the Fréchet Mean.

Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge J. Belongie, Ser-Nam Lim, Christopher De Sa

Proceedings of the 37th International Conference on Machine Learning, 2020

2018

Adversarial Example Decomposition.

Horace He, Aaron Lou, Qingxuan Jiang, Isay Katsman, Pian Pawakapan, Serge J. Belongie, Ser-Nam Lim

CoRR, 2018