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2020 – today
- 2024
[c12]Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, Nikhil Naik:
Diffusion Model Alignment Using Direct Preference Optimization. CVPR 2024: 8228-8238- [c11]Linqi Zhou, Aaron Lou, Samar Khanna, Stefano Ermon:
Denoising Diffusion Bridge Models. ICLR 2024 - [c10]Aaron Lou, Chenlin Meng, Stefano Ermon:
Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution. ICML 2024 - [c9]Minkai Xu, Jiaqi Han, Aaron Lou, Jean Kossaifi, Arvind Ramanathan, Kamyar Azizzadenesheli, Jure Leskovec, Stefano Ermon, Anima Anandkumar:
Equivariant Graph Neural Operator for Modeling 3D Dynamics. ICML 2024 - [c8]Jiaqi Han, Minkai Xu, Aaron Lou, Haotian Ye, Stefano Ermon:
Geometric Trajectory Diffusion Models. NeurIPS 2024 - [i13]Minkai Xu, Jiaqi Han, Aaron Lou, Jean Kossaifi, Arvind Ramanathan, Kamyar Azizzadenesheli, Jure Leskovec, Stefano Ermon, Anima Anandkumar:
Equivariant Graph Neural Operator for Modeling 3D Dynamics. CoRR abs/2401.11037 (2024) - [i12]Jiaqi Han, Minkai Xu, Aaron Lou, Haotian Ye, Stefano Ermon:
Geometric Trajectory Diffusion Models. CoRR abs/2410.13027 (2024) - 2023
- [c7]Aaron Lou, Stefano Ermon:
Reflected Diffusion Models. ICML 2023: 22675-22701 - [c6]Isay Katsman, Eric Ming Chen, Sidhanth Holalkere, Anna Asch, Aaron Lou, Ser Nam Lim, Christopher De Sa:
Riemannian Residual Neural Networks. NeurIPS 2023 - [c5]Aaron Lou, Minkai Xu, Adam Farris, Stefano Ermon:
Scaling Riemannian Diffusion Models. NeurIPS 2023 - [i11]Aaron Lou, Stefano Ermon:
Reflected Diffusion Models. CoRR abs/2304.04740 (2023) - [i10]Linqi Zhou, Aaron Lou, Samar Khanna, Stefano Ermon:
Denoising Diffusion Bridge Models. CoRR abs/2309.16948 (2023) - [i9]Isay Katsman, Eric Ming Chen, Sidhanth Holalkere, Anna Asch, Aaron Lou, Ser-Nam Lim, Christopher De Sa:
Riemannian Residual Neural Networks. CoRR abs/2310.10013 (2023) - [i8]Aaron Lou, Chenlin Meng, Stefano Ermon:
Discrete Diffusion Language Modeling by Estimating the Ratios of the Data Distribution. CoRR abs/2310.16834 (2023) - [i7]Aaron Lou, Minkai Xu, Stefano Ermon:
Scaling Riemannian Diffusion Models. CoRR abs/2310.20030 (2023) - [i6]Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, Nikhil Naik:
Diffusion Model Alignment Using Direct Preference Optimization. CoRR abs/2311.12908 (2023) - 2021
- [c4]Tolga Birdal, Aaron Lou, Leonidas J. Guibas, Umut Simsekli:
Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks. NeurIPS 2021: 6776-6789 - [c3]Isay Katsman, Aaron Lou, Derek Lim, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa:
Equivariant Manifold Flows. NeurIPS 2021: 10600-10612 - [i5]Isay Katsman, Aaron Lou, Derek Lim, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa:
Equivariant Manifold Flows. CoRR abs/2107.08596 (2021) - [i4]Tolga Birdal, Aaron Lou, Leonidas J. Guibas, Umut Simsekli:
Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks. CoRR abs/2111.13171 (2021) - 2020
- [c2]Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge J. Belongie, Ser-Nam Lim, Christopher De Sa:
Differentiating through the Fréchet Mean. ICML 2020: 6393-6403 - [c1]Aaron Lou, Derek Lim, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa:
Neural Manifold Ordinary Differential Equations. NeurIPS 2020 - [i3]Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge J. Belongie, Ser-Nam Lim, Christopher De Sa:
Differentiating through the Fréchet Mean. CoRR abs/2003.00335 (2020) - [i2]Aaron Lou, Derek Lim
, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa:
Neural Manifold Ordinary Differential Equations. CoRR abs/2006.10254 (2020)
2010 – 2019
- 2018
- [i1]Horace He, Aaron Lou, Qingxuan Jiang, Isay Katsman, Pian Pawakapan, Serge J. Belongie, Ser-Nam Lim:
Adversarial Example Decomposition. CoRR abs/1812.01198 (2018)
Coauthor Index
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