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Your Classifier is Secretly an Energy Based Model and You Should Treat
  it Like One
v1v2v3 (latest)

Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One

International Conference on Learning Representations (ICLR), 2019
6 December 2019
Will Grathwohl
Kuan-Chieh Wang
J. Jacobsen
David Duvenaud
Mohammad Norouzi
Kevin Swersky
    VLM
ArXiv (abs)PDFHTML

Papers citing "Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One"

50 / 390 papers shown
Active Learning for Domain Adaptation: An Energy-Based Approach
Active Learning for Domain Adaptation: An Energy-Based Approach
Binhui Xie
Longhui Yuan
Shuang Li
Chi Harold Liu
Xinjing Cheng
Guoren Wang
255
140
0
02 Dec 2021
Particle Dynamics for Learning EBMs
Particle Dynamics for Learning EBMs
Kirill Neklyudov
P. Jaini
Max Welling
DiffM
130
1
0
26 Nov 2021
Consensus Synergizes with Memory: A Simple Approach for Anomaly
  Segmentation in Urban Scenes
Consensus Synergizes with Memory: A Simple Approach for Anomaly Segmentation in Urban Scenes
Jiazhong Cen
Zekun Jiang
Lingxi Xie
Qi Tian
Dongsheng Jiang
Wei Shen
223
6
0
24 Nov 2021
Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on
  Complex Urban Driving Scenes
Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes
Yu Tian
Yuyuan Liu
Guansong Pang
Fengbei Liu
Yuanhong Chen
G. Carneiro
480
110
0
24 Nov 2021
Learning to Compose Visual Relations
Learning to Compose Visual Relations
Nan Liu
Shuang Li
Yilun Du
J. Tenenbaum
Antonio Torralba
CoGeOCL
236
92
0
17 Nov 2021
Unsupervised Learning of Compositional Energy Concepts
Unsupervised Learning of Compositional Energy ConceptsNeural Information Processing Systems (NeurIPS), 2021
Yilun Du
Shuang Li
Yash Sharma
J. Tenenbaum
Igor Mordatch
CoGeOCL
292
84
0
04 Nov 2021
Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling
Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid SamplingNeural Information Processing Systems (NeurIPS), 2021
Greg Ver Steeg
Aram Galstyan
301
15
0
03 Nov 2021
A Unified View of cGANs with and without Classifiers
A Unified View of cGANs with and without ClassifiersNeural Information Processing Systems (NeurIPS), 2021
Si-An Chen
Chun-Liang Li
Hsuan-Tien Lin
GAN
175
12
0
01 Nov 2021
Pseudo-Spherical Contrastive Divergence
Pseudo-Spherical Contrastive DivergenceNeural Information Processing Systems (NeurIPS), 2021
Lantao Yu
Jiaming Song
Yang Song
Stefano Ermon
222
7
0
01 Nov 2021
Learning Deep Representation with Energy-Based Self-Expressiveness for Subspace Clustering
Yanming Li
Changsheng Li
Shiye Wang
Ye Yuan
Guoren Wang
SSL
217
0
0
28 Oct 2021
Graph Posterior Network: Bayesian Predictive Uncertainty for Node
  Classification
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
Maximilian Stadler
Bertrand Charpentier
Simon Geisler
Daniel Zügner
Stephan Günnemann
UQCVBDL
325
104
0
26 Oct 2021
Learning Proposals for Practical Energy-Based Regression
Learning Proposals for Practical Energy-Based RegressionInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2021
L. Kumar
Martin Danelljan
Thomas B. Schon
172
4
0
22 Oct 2021
Analyzing and Improving the Optimization Landscape of Noise-Contrastive
  Estimation
Analyzing and Improving the Optimization Landscape of Noise-Contrastive EstimationInternational Conference on Learning Representations (ICLR), 2021
Bingbin Liu
Elan Rosenfeld
Pradeep Ravikumar
Andrej Risteski
225
17
0
21 Oct 2021
Controllable and Compositional Generation with Latent-Space Energy-Based
  Models
Controllable and Compositional Generation with Latent-Space Energy-Based ModelsNeural Information Processing Systems (NeurIPS), 2021
Weili Nie
Arash Vahdat
Anima Anandkumar
264
84
0
21 Oct 2021
Adversarial Purification through Representation Disentanglement
Adversarial Purification through Representation Disentanglement
Tao Bai
Jun Zhao
Lanqing Guo
Bihan Wen
AAML
114
1
0
15 Oct 2021
Well-classified Examples are Underestimated in Classification with Deep
  Neural Networks
Well-classified Examples are Underestimated in Classification with Deep Neural Networks
Guangxiang Zhao
Wenkai Yang
Xuancheng Ren
Lei Li
Hao Sun
Xu Sun
249
15
0
13 Oct 2021
Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic
  Uncertainty
Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic UncertaintyInternational Conference on Learning Representations (ICLR), 2021
Jeffrey Willette
Haebeom Lee
Juho Lee
Sung Ju Hwang
OODDOOD
227
2
0
12 Oct 2021
Prior and Posterior Networks: A Survey on Evidential Deep Learning
  Methods For Uncertainty Estimation
Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation
Dennis Ulmer
Christian Hardmeier
J. Frellsen
BDLUQCVUDEDLPER
335
77
0
06 Oct 2021
Score-Based Generative Classifiers
Score-Based Generative Classifiers
Roland S. Zimmermann
Lukas Schott
Yang Song
Benjamin A. Dunn
David A. Klindt
DiffM
256
74
0
01 Oct 2021
JEM++: Improved Techniques for Training JEM
JEM++: Improved Techniques for Training JEM
Xiulong Yang
Shihao Ji
AAMLVLM
252
33
0
19 Sep 2021
The Functional Correspondence Problem
The Functional Correspondence Problem
Zihang Lai
Senthil Purushwalkam
Abhinav Gupta
302
22
0
02 Sep 2021
Implicit Behavioral Cloning
Implicit Behavioral CloningConference on Robot Learning (CoRL), 2021
Peter R. Florence
Corey Lynch
Andy Zeng
Oscar Ramirez
Ayzaan Wahid
Laura Downs
Adrian S. Wong
Johnny Lee
Igor Mordatch
Jonathan Tompson
OffRL
485
532
0
01 Sep 2021
Learning Energy-Based Approximate Inference Networks for Structured
  Applications in NLP
Learning Energy-Based Approximate Inference Networks for Structured Applications in NLP
Lifu Tu
BDL
141
0
0
27 Aug 2021
Anomaly Detection of Defect using Energy of Point Pattern Features
  within Random Finite Set Framework
Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set FrameworkEngineering applications of artificial intelligence (EAAI), 2021
Ammar Mansoor Kamoona
A. Gostar
A. Bab-Hadiashar
R. Hoseinnezhad
124
19
0
27 Aug 2021
Latent Space Energy-Based Model of Symbol-Vector Coupling for Text
  Generation and Classification
Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and ClassificationInternational Conference on Machine Learning (ICML), 2021
Bo Pang
Ying Nian Wu
165
19
0
26 Aug 2021
Revealing the Distributional Vulnerability of Discriminators by Implicit
  Generators
Revealing the Distributional Vulnerability of Discriminators by Implicit GeneratorsIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
Zhilin Zhao
LongBing Cao
Kun-Yu Lin
440
17
0
23 Aug 2021
Towards Understanding the Generative Capability of Adversarially Robust
  Classifiers
Towards Understanding the Generative Capability of Adversarially Robust Classifiers
Yao Zhu
Jiacheng Ma
Jiacheng Sun
Zewei Chen
Rongxin Jiang
Zhenguo Li
AAML
222
28
0
20 Aug 2021
Energy-Based Open-World Uncertainty Modeling for Confidence Calibration
Energy-Based Open-World Uncertainty Modeling for Confidence CalibrationIEEE International Conference on Computer Vision (ICCV), 2021
Yezhen Wang
Yue Liu
Tong Che
Kaiyang Zhou
Ziwei Liu
Dongsheng Li
UQCV
287
66
0
27 Jul 2021
Energy-based Unknown Intent Detection with Data Manipulation
Energy-based Unknown Intent Detection with Data ManipulationFindings (Findings), 2021
Yawen Ouyang
Jiasheng Ye
Yu Chen
Xinyu Dai
Shujian Huang
Jiajun Chen
112
24
0
27 Jul 2021
Directly Training Joint Energy-Based Models for Conditional Synthesis
  and Calibrated Prediction of Multi-Attribute Data
Directly Training Joint Energy-Based Models for Conditional Synthesis and Calibrated Prediction of Multi-Attribute Data
Jacob Kelly
R. Zemel
Will Grathwohl
183
2
0
19 Jul 2021
On the Importance of Regularisation & Auxiliary Information in OOD
  Detection
On the Importance of Regularisation & Auxiliary Information in OOD DetectionInternational Conference on Neural Information Processing (ICONIP), 2021
John Mitros
Brian Mac Namee
256
2
0
15 Jul 2021
Fast approximations of the Jeffreys divergence between univariate
  Gaussian mixture models via exponential polynomial densities
Fast approximations of the Jeffreys divergence between univariate Gaussian mixture models via exponential polynomial densities
Frank Nielsen
395
13
0
13 Jul 2021
InfoNCE is variational inference in a recognition parameterised model
InfoNCE is variational inference in a recognition parameterised model
Laurence Aitchison
Stoil Ganev
BDL
389
12
0
06 Jul 2021
On Out-of-distribution Detection with Energy-based Models
On Out-of-distribution Detection with Energy-based Models
Sven Elflein
Bertrand Charpentier
Daniel Zügner
Stephan Günnemann
OODD
165
22
0
03 Jul 2021
Conjugate Energy-Based Models
Conjugate Energy-Based ModelsInternational Conference on Machine Learning (ICML), 2021
Hao Wu
Babak Esmaeili
Michael L. Wick
Jean-Baptiste Tristan
Jan-Willem van de Meent
215
2
0
25 Jun 2021
Energy-Based Generative Cooperative Saliency Prediction
Energy-Based Generative Cooperative Saliency Prediction
Jing Zhang
Jianwen Xie
Zilong Zheng
Nick Barnes
293
13
0
25 Jun 2021
Learning Equivariant Energy Based Models with Equivariant Stein
  Variational Gradient Descent
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient DescentNeural Information Processing Systems (NeurIPS), 2021
P. Jaini
Lars Holdijk
Max Welling
325
13
0
15 Jun 2021
Adversarial purification with Score-based generative models
Adversarial purification with Score-based generative modelsInternational Conference on Machine Learning (ICML), 2021
Jongmin Yoon
Sung Ju Hwang
Juho Lee
DiffM
240
183
0
11 Jun 2021
Hybrid Generative-Contrastive Representation Learning
Hybrid Generative-Contrastive Representation Learning
Saehoon Kim
Sungwoong Kim
Juho Lee
SSL
142
11
0
11 Jun 2021
Taxonomy of Machine Learning Safety: A Survey and Primer
Taxonomy of Machine Learning Safety: A Survey and PrimerACM Computing Surveys (CSUR), 2021
Sina Mohseni
Haotao Wang
Zhiding Yu
Chaowei Xiao
Zinan Lin
J. Yadawa
313
45
0
09 Jun 2021
Energy Aligning for Biased Models
Energy Aligning for Biased Models
Bowen Zhao
Chen Chen
Qi Ju
Shutao Xia
101
1
0
07 Jun 2021
Energy-Based Learning for Cooperative Games, with Applications to
  Valuation Problems in Machine Learning
Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine LearningInternational Conference on Learning Representations (ICLR), 2021
Yatao Bian
Yu Rong
Qifeng Bai
Jiaxiang Wu
Andreas Krause
Junzhou Huang
436
17
0
05 Jun 2021
Bridging the Gap Between Explainable AI and Uncertainty Quantification
  to Enhance Trustability
Bridging the Gap Between Explainable AI and Uncertainty Quantification to Enhance Trustability
Dominik Seuss
149
20
0
25 May 2021
Autoencoding Under Normalization Constraints
Autoencoding Under Normalization ConstraintsInternational Conference on Machine Learning (ICML), 2021
Sangwoong Yoon
Yung-Kyun Noh
Frank C. Park
OODDUQCV
303
42
0
12 May 2021
Diffusion Models Beat GANs on Image Synthesis
Diffusion Models Beat GANs on Image SynthesisNeural Information Processing Systems (NeurIPS), 2021
Prafulla Dhariwal
Alex Nichol
3.0K
10,306
0
11 May 2021
Natural Posterior Network: Deep Bayesian Uncertainty for Exponential
  Family Distributions
Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family Distributions
Bertrand Charpentier
Oliver Borchert
Daniel Zügner
Simon Geisler
Stephan Günnemann
UQCVBDL
263
19
0
10 May 2021
Energy-Based Anomaly Detection and Localization
Energy-Based Anomaly Detection and Localization
Ergin Utku Genc
Nilesh A. Ahuja
I. Ndiour
Omesh Tickoo
113
6
0
07 May 2021
MOOD: Multi-level Out-of-distribution Detection
MOOD: Multi-level Out-of-distribution DetectionComputer Vision and Pattern Recognition (CVPR), 2021
Ziqian Lin
Sreya . Dutta Roy
Shouqing Yang
OODD
185
126
0
30 Apr 2021
An Energy-Based View of Graph Neural Networks
An Energy-Based View of Graph Neural Networks
John Y. Shin
Prathamesh Dharangutte
GNN
197
1
0
27 Apr 2021
See through Gradients: Image Batch Recovery via GradInversion
See through Gradients: Image Batch Recovery via GradInversionComputer Vision and Pattern Recognition (CVPR), 2021
Hongxu Yin
Arun Mallya
Arash Vahdat
J. Álvarez
Jan Kautz
Pavlo Molchanov
FedML
336
551
0
15 Apr 2021
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