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2003.03463
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Training Deep Energy-Based Models with f-Divergence Minimization
International Conference on Machine Learning (ICML), 2020
6 March 2020
Lantao Yu
Yang Song
Jiaming Song
Stefano Ermon
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Papers citing
"Training Deep Energy-Based Models with f-Divergence Minimization"
33 / 33 papers shown
A Unified Framework for Diffusion Model Unlearning with f-Divergence
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Sinkhorn Distance Minimization for Knowledge Distillation
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Yulei Qin
Yuting Gao
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Zihan Xu
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Xing Sun
Wen-gang Zhou
Houqiang Li
232
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27 Feb 2024
f
f
f
-Divergence Based Classification: Beyond the Use of Cross-Entropy
International Conference on Machine Learning (ICML), 2024
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Andrea M. Tonello
409
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0
02 Jan 2024
Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach
Neural Information Processing Systems (NeurIPS), 2023
Sangwoong Yoon
Young-Uk Jin
Yung-Kyun Noh
Frank C. Park
298
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28 Oct 2023
STANLEY: Stochastic Gradient Anisotropic Langevin Dynamics for Learning Energy-Based Models
Belhal Karimi
Jianwen Xie
Ping Li
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298
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19 Oct 2023
Learning Unnormalized Statistical Models via Compositional Optimization
International Conference on Machine Learning (ICML), 2023
Wei Jiang
Jiayu Qin
Lingyu Wu
Changyou Chen
Tianbao Yang
Lijun Zhang
414
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13 Jun 2023
On Feature Diversity in Energy-based Models
Firas Laakom
Jenni Raitoharju
Alexandros Iosifidis
Moncef Gabbouj
205
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02 Jun 2023
Persistently Trained, Diffusion-assisted Energy-based Models
Xinwei Zhang
Z. Tan
Zhijian Ou
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196
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21 Apr 2023
M-EBM: Towards Understanding the Manifolds of Energy-Based Models
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2023
Xiulong Yang
Shihao Ji
208
6
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08 Mar 2023
Guiding Energy-based Models via Contrastive Latent Variables
International Conference on Learning Representations (ICLR), 2023
Hankook Lee
Jongheon Jeong
Sejun Park
Jinwoo Shin
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279
19
0
06 Mar 2023
Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence
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Lingwei Zhu
Zheng Chen
Takamitsu Matsubara
Martha White
277
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0
27 Jan 2023
Improved Stein Variational Gradient Descent with Importance Weights
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Peter Richtárik
353
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02 Oct 2022
Entropy-driven Unsupervised Keypoint Representation Learning in Videos
International Conference on Machine Learning (ICML), 2022
A. Younes
Simone Schaub-Meyer
Georgia Chalvatzaki
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374
1
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30 Sep 2022
A General Recipe for Likelihood-free Bayesian Optimization
International Conference on Machine Learning (ICML), 2022
Jiaming Song
Lantao Yu
Willie Neiswanger
Stefano Ermon
320
29
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27 Jun 2022
EBM Life Cycle: MCMC Strategies for Synthesis, Defense, and Density Modeling
Mitch Hill
Jonathan Mitchell
Chu Chen
Yuan Du
M. Shah
Song-Chun Zhu
216
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24 May 2022
Bi-level Doubly Variational Learning for Energy-based Latent Variable Models
Computer Vision and Pattern Recognition (CVPR), 2022
Ge Kan
Jinhu Lu
Tian Wang
Baochang Zhang
Aichun Zhu
Lei Huang
Guodong Guo
H. Snoussi
255
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0
24 Mar 2022
Generative Flow Networks for Discrete Probabilistic Modeling
International Conference on Machine Learning (ICML), 2022
Dinghuai Zhang
Nikolay Malkin
Ziqiang Liu
Alexandra Volokhova
Aaron Courville
Yoshua Bengio
417
131
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03 Feb 2022
Pseudo-Spherical Contrastive Divergence
Neural Information Processing Systems (NeurIPS), 2021
Lantao Yu
Jiaming Song
Yang Song
Stefano Ermon
296
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0
01 Nov 2021
Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation
International Conference on Learning Representations (ICLR), 2021
Bingbin Liu
Elan Rosenfeld
Pradeep Ravikumar
Andrej Risteski
257
19
0
21 Oct 2021
Learning High-Dimensional Distributions with Latent Neural Fokker-Planck Kernels
Jiuxiang Gu
Changyou Chen
Jinhui Xu
238
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0
10 May 2021
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
Sam Bond-Taylor
Adam Leach
Yang Long
Chris G. Willcocks
VLM
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650
0
08 Mar 2021
EBMs Trained with Maximum Likelihood are Generator Models Trained with a Self-adverserial Loss
Zhisheng Xiao
Qing Yan
Y. Amit
282
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23 Feb 2021
How to Train Your Energy-Based Models
Yang Song
Diederik P. Kingma
DiffM
517
321
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09 Jan 2021
Improved Contrastive Divergence Training of Energy Based Models
International Conference on Machine Learning (ICML), 2020
Yilun Du
Shuang Li
J. Tenenbaum
Igor Mordatch
736
170
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02 Dec 2020
Unpaired Image-to-Image Translation via Latent Energy Transport
Computer Vision and Pattern Recognition (CVPR), 2020
Yang Zhao
Changyou Chen
396
29
0
01 Dec 2020
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration
H. Dai
Rishabh Singh
Bo Dai
Charles Sutton
Dale Schuurmans
299
32
0
10 Nov 2020
Autoregressive Score Matching
Neural Information Processing Systems (NeurIPS), 2020
Chenlin Meng
Lantao Yu
Yang Song
Jiaming Song
Stefano Ermon
DiffM
519
16
0
24 Oct 2020
Imitation with Neural Density Models
Neural Information Processing Systems (NeurIPS), 2020
Kuno Kim
Akshat Jindal
Yang Song
Jiaming Song
Yanan Sui
Stefano Ermon
254
14
0
19 Oct 2020
A Neural Network MCMC sampler that maximizes Proposal Entropy
Zengyi Li
Yubei Chen
Friedrich T. Sommer
302
15
0
07 Oct 2020
f
f
f
-GAIL: Learning
f
f
f
-Divergence for Generative Adversarial Imitation Learning
Neural Information Processing Systems (NeurIPS), 2020
Xin Zhang
Jun Luo
Ziming Zhang
Zhi-Li Zhang
252
38
0
02 Oct 2020
VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models
International Conference on Learning Representations (ICLR), 2020
Zhisheng Xiao
Karsten Kreis
Jan Kautz
Arash Vahdat
515
138
0
01 Oct 2020
Telescoping Density-Ratio Estimation
Benjamin Rhodes
Kai Xu
Michael U. Gutmann
433
132
0
22 Jun 2020
Generalized Energy Based Models
International Conference on Learning Representations (ICLR), 2020
Michael Arbel
Liang Zhou
Arthur Gretton
DRL
604
93
0
10 Mar 2020
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