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ORCIDMitch Hill
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2020 – today
- 2024
[c8]Zhangsihao Yang, Mingyuan Zhou, Mengyi Shan, Bingbing Wen, Ziwei Xuan, Mitch Hill, Junjie Bai, Guo-Jun Qi, Yalin Wang:
OmniMotionGPT: Animal Motion Generation with Limited Data. CVPR 2024: 1249-1259- [c7]Mengyi Shan, Lu Dong, Yutao Han, Yuan Yao, Tao Liu, Ifeoma Nwogu, Guo-Jun Qi, Mitch Hill:
Towards Open Domain Text-Driven Synthesis of Multi-person Motions. ECCV (65) 2024: 67-86 - [i8]Mengyi Shan, Lu Dong, Yutao Han, Yuan Yao, Tao Liu, Ifeoma Nwogu, Guo-Jun Qi, Mitch Hill:
Towards Open Domain Text-Driven Synthesis of Multi-Person Motions. CoRR abs/2405.18483 (2024) - 2023
- [i7]Zhangsihao Yang, Mingyuan Zhou, Mengyi Shan, Bingbing Wen, Ziwei Xuan, Mitch Hill, Junjie Bai, Guo-Jun Qi, Yalin Wang:
OmniMotionGPT: Animal Motion Generation with Limited Data. CoRR abs/2311.18303 (2023) - 2022
- [c6]Navid Kardan, Mubarak Shah, Mitch Hill:
Self-Joint Supervised Learning. ICLR 2022 - [c5]Mitch Hill, Erik Nijkamp, Jonathan Mitchell, Bo Pang, Song-Chun Zhu:
Learning Probabilistic Models from Generator Latent Spaces with Hat EBM. NeurIPS 2022 - [i6]Mitch Hill, Jonathan Mitchell, Chu Chen, Yuan Du, Mubarak Shah
, Song-Chun Zhu:
EBM Life Cycle: MCMC Strategies for Synthesis, Defense, and Density Modeling. CoRR abs/2205.12243 (2022) - [i5]Mitch Hill, Erik Nijkamp, Jonathan Mitchell, Bo Pang, Song-Chun Zhu:
Learning Probabilistic Models from Generator Latent Spaces with Hat EBM. CoRR abs/2210.16486 (2022) - 2021
- [c4]Mitch Hill, Jonathan Craig Mitchell, Song-Chun Zhu:
Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models. ICLR 2021 - 2020
- [b1]Mitch Hill:
Learning and Mapping Energy Functions of High-Dimensional Image Data. University of California, Los Angeles, USA, 2020 - [c3]Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu:
On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models. AAAI 2020: 5272-5280 - [i4]Mitch Hill, Jonathan Mitchell, Song-Chun Zhu:
Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models. CoRR abs/2005.13525 (2020)
2010 – 2019
- 2019
- [c2]Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, Ying Nian Wu:
Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model. CVPR 2019: 8670-8679 - [c1]Erik Nijkamp, Mitch Hill, Song-Chun Zhu, Ying Nian Wu:
Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model. NeurIPS 2019: 5233-5243 - [i3]Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu:
On the Anatomy of MCMC-based Maximum Likelihood Learning of Energy-Based Models. CoRR abs/1903.12370 (2019) - 2018
- [i2]Mitch Hill, Erik Nijkamp, Song-Chun Zhu:
Building a Telescope to Look Into High-Dimensional Image Spaces. CoRR abs/1803.01043 (2018) - [i1]Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, Ying Nian Wu:
Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model. CoRR abs/1812.10907 (2018)
Coauthor Index
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