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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
Persistently Trained, Diffusion-assisted Energy-based Models
Persistently Trained, Diffusion-assisted Energy-based Models
Xinwei Zhang
Z. Tan
Zhijian Ou
DiffM
167
3
0
21 Apr 2023
Binary Latent Diffusion
Binary Latent DiffusionComputer Vision and Pattern Recognition (CVPR), 2023
Ze Wang
Jiang Wang
Zicheng Liu
Qiang Qiu
223
18
0
10 Apr 2023
Exploring the Connection between Robust and Generative Models
Exploring the Connection between Robust and Generative Models
Senad Beadini
I. Masi
AAML
339
2
0
08 Apr 2023
EGC: Image Generation and Classification via a Diffusion Energy-Based
  Model
EGC: Image Generation and Classification via a Diffusion Energy-Based ModelIEEE International Conference on Computer Vision (ICCV), 2023
Qiushan Guo
Chuofan Ma
Yi Jiang
Zehuan Yuan
Yizhou Yu
Ping Luo
DiffM
353
13
0
04 Apr 2023
Non-Generative Energy Based Models
Non-Generative Energy Based ModelsIEEE International Joint Conference on Neural Network (IJCNN), 2023
Jacob Piland
Christopher Sweet
Priscila Saboia
Charles Vardeman
A. Czajka
181
1
0
03 Apr 2023
Invertible Convolution with Symmetric Paddings
Invertible Convolution with Symmetric Paddings
Yangqiu Song
93
0
0
30 Mar 2023
Your Diffusion Model is Secretly a Zero-Shot Classifier
Your Diffusion Model is Secretly a Zero-Shot ClassifierIEEE International Conference on Computer Vision (ICCV), 2023
Alexander C. Li
Mihir Prabhudesai
Shivam Duggal
Ellis L Brown
Deepak Pathak
DiffMVLM
679
307
0
28 Mar 2023
Mind the Label Shift of Augmentation-based Graph OOD Generalization
Mind the Label Shift of Augmentation-based Graph OOD GeneralizationComputer Vision and Pattern Recognition (CVPR), 2023
Junchi Yu
Jian Liang
Ran He
180
44
0
27 Mar 2023
Enhancing Multiple Reliability Measures via Nuisance-extended
  Information Bottleneck
Enhancing Multiple Reliability Measures via Nuisance-extended Information BottleneckComputer Vision and Pattern Recognition (CVPR), 2023
Jongheon Jeong
Sihyun Yu
Hankook Lee
Jinwoo Shin
AAML
176
1
0
24 Mar 2023
InPL: Pseudo-labeling the Inliers First for Imbalanced Semi-supervised
  Learning
InPL: Pseudo-labeling the Inliers First for Imbalanced Semi-supervised LearningInternational Conference on Learning Representations (ICLR), 2023
Xiaohua Xie
Yin Li
Yong Jae Lee
181
17
0
13 Mar 2023
Pixel-wise Gradient Uncertainty for Convolutional Neural Networks
  applied to Out-of-Distribution Segmentation
Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation
Kira Maag
Tobias Riedlinger
UQCV
227
10
0
13 Mar 2023
M-EBM: Towards Understanding the Manifolds of Energy-Based Models
M-EBM: Towards Understanding the Manifolds of Energy-Based ModelsPacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2023
Xiulong Yang
Shihao Ji
173
6
0
08 Mar 2023
Stabilized training of joint energy-based models and their practical
  applications
Stabilized training of joint energy-based models and their practical applications
Martin Sustek
Samik Sadhu
L. Burget
H. Hermansky
Jesus Villalba
Laureano Moro-Velazquez
Najim Dehak
AAMLVLM
113
1
0
07 Mar 2023
Guiding Energy-based Models via Contrastive Latent Variables
Guiding Energy-based Models via Contrastive Latent VariablesInternational Conference on Learning Representations (ICLR), 2023
Hankook Lee
Jongheon Jeong
Sejun Park
Jinwoo Shin
BDL
236
18
0
06 Mar 2023
How to Construct Energy for Images? Denoising Autoencoder Can Be Energy
  Based Model
How to Construct Energy for Images? Denoising Autoencoder Can Be Energy Based Model
W. Zeng
DiffM
192
1
0
05 Mar 2023
Randomness in ML Defenses Helps Persistent Attackers and Hinders
  Evaluators
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators
Keane Lucas
Matthew Jagielski
Florian Tramèr
Lujo Bauer
Nicholas Carlini
AAML
203
10
0
27 Feb 2023
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based
  Diffusion Models and MCMC
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMCInternational Conference on Machine Learning (ICML), 2023
Yilun Du
Conor Durkan
Robin Strudel
J. Tenenbaum
Sander Dieleman
Rob Fergus
Jascha Narain Sohl-Dickstein
Arnaud Doucet
Will Grathwohl
DiffM
448
197
0
22 Feb 2023
Energy-Based Test Sample Adaptation for Domain Generalization
Energy-Based Test Sample Adaptation for Domain GeneralizationInternational Conference on Learning Representations (ICLR), 2023
Zehao Xiao
Xiantong Zhen
Tianran Ouyang
Cees G. M. Snoek
TTA
237
22
0
22 Feb 2023
Piecewise Deterministic Markov Processes for Bayesian Neural Networks
Piecewise Deterministic Markov Processes for Bayesian Neural NetworksConference on Uncertainty in Artificial Intelligence (UAI), 2023
Ethan Goan
Dimitri Perrin
Kerrie Mengersen
Clinton Fookes
175
0
0
17 Feb 2023
Energy-based Out-of-Distribution Detection for Graph Neural Networks
Energy-based Out-of-Distribution Detection for Graph Neural NetworksInternational Conference on Learning Representations (ICLR), 2023
Qitian Wu
Yiting Chen
Chenxiao Yang
Junchi Yan
OODD
497
87
0
06 Feb 2023
Energy-Inspired Self-Supervised Pretraining for Vision Models
Energy-Inspired Self-Supervised Pretraining for Vision ModelsInternational Conference on Learning Representations (ICLR), 2023
Ze Wang
Jiang Wang
Zicheng Liu
Qiang Qiu
245
10
0
02 Feb 2023
Versatile Energy-Based Probabilistic Models for High Energy Physics
Versatile Energy-Based Probabilistic Models for High Energy PhysicsNeural Information Processing Systems (NeurIPS), 2023
Taoli Cheng
Aaron Courville
DiffM
360
1
0
01 Feb 2023
Generating High Fidelity Synthetic Data via Coreset selection and
  Entropic Regularization
Generating High Fidelity Synthetic Data via Coreset selection and Entropic Regularization
Omead Brandon Pooladzandi
Pasha Khosravi
Erik Nijkamp
Baharan Mirzasoleiman
SyDa
79
3
0
31 Jan 2023
Learning Data Representations with Joint Diffusion Models
Learning Data Representations with Joint Diffusion Models
Kamil Deja
Tomasz Trzciñski
Jakub M. Tomczak
DiffM
267
24
0
31 Jan 2023
Master's Thesis: Out-of-distribution Detection with Energy-based Models
Master's Thesis: Out-of-distribution Detection with Energy-based Models
Sven Elflein
OODD
183
2
0
28 Jan 2023
A Deep Learning Method for Comparing Bayesian Hierarchical Models
A Deep Learning Method for Comparing Bayesian Hierarchical ModelsPsychological methods (Psychol Methods), 2023
Lasse Elsemüller
Martin Schnuerch
Paul-Christian Bürkner
Stefan T. Radev
BDL
276
17
0
27 Jan 2023
Hybrid Open-set Segmentation with Synthetic Negative Data
Hybrid Open-set Segmentation with Synthetic Negative DataIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023
Matej Grcić
Sinivsa vSegvić
284
11
0
19 Jan 2023
Rationalizing Predictions by Adversarial Information Calibration
Rationalizing Predictions by Adversarial Information CalibrationArtificial Intelligence (AI), 2022
Lei Sha
Oana-Maria Camburu
Thomas Lukasiewicz
185
9
0
15 Jan 2023
A survey and taxonomy of loss functions in machine learning
A survey and taxonomy of loss functions in machine learning
Lorenzo Ciampiconi
A. Elwood
Marco Leonardi
A. Mohamed
A. Rozza
MUFaML
182
48
0
13 Jan 2023
GEDI: GEnerative and DIscriminative Training for Self-Supervised
  Learning
GEDI: GEnerative and DIscriminative Training for Self-Supervised Learning
Emanuele Sansone
Robin Manhaeve
SSL
426
9
0
27 Dec 2022
The Forward-Forward Algorithm: Some Preliminary Investigations
The Forward-Forward Algorithm: Some Preliminary Investigations
Geoffrey E. Hinton
238
359
0
27 Dec 2022
Key Feature Replacement of In-Distribution Samples for
  Out-of-Distribution Detection
Key Feature Replacement of In-Distribution Samples for Out-of-Distribution DetectionAAAI Conference on Artificial Intelligence (AAAI), 2022
Jaeyoung Kim
Seo Taek Kong
Dongbin Na
Kyu-Hwan Jung
OODD
137
6
0
26 Dec 2022
Detecting Objects with Context-Likelihood Graphs and Graph Refinement
Detecting Objects with Context-Likelihood Graphs and Graph RefinementIEEE International Conference on Computer Vision (ICCV), 2022
Aritra Bhowmik
Yu Wang
N. Baka
Martin R. Oswald
Cees G. M. Snoek
305
2
0
23 Dec 2022
Robust Graph Representation Learning via Predictive Coding
Robust Graph Representation Learning via Predictive Coding
Billy Byiringiro
Tommaso Salvatori
Thomas Lukasiewicz
OOD
200
7
0
09 Dec 2022
PROB: Probabilistic Objectness for Open World Object Detection
PROB: Probabilistic Objectness for Open World Object DetectionComputer Vision and Pattern Recognition (CVPR), 2022
O. Zohar
Kuan-Chieh Wang
Serena Yeung
277
93
0
02 Dec 2022
Isolation and Impartial Aggregation: A Paradigm of Incremental Learning
  without Interference
Isolation and Impartial Aggregation: A Paradigm of Incremental Learning without InterferenceAAAI Conference on Artificial Intelligence (AAAI), 2022
Yabin Wang
Zhiheng Ma
Zhiwu Huang
Yaowei Wang
Zhou Su
Xiaopeng Hong
191
59
0
29 Nov 2022
Traditional Classification Neural Networks are Good Generators: They are
  Competitive with DDPMs and GANs
Traditional Classification Neural Networks are Good Generators: They are Competitive with DDPMs and GANs
Guangrun Wang
Juil Sock
279
11
0
27 Nov 2022
End-to-End Stochastic Optimization with Energy-Based Model
End-to-End Stochastic Optimization with Energy-Based ModelNeural Information Processing Systems (NeurIPS), 2022
Lingkai Kong
Jiaming Cui
Yuchen Zhuang
Rui Feng
B. Prakash
Chao Zhang
118
22
0
25 Nov 2022
Normalizing Flow with Variational Latent Representation
Normalizing Flow with Variational Latent Representation
Hanze Dong
Shizhe Diao
Weizhong Zhang
Tong Zhang
BDLOODDRL
154
1
0
21 Nov 2022
A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive
  Coding Networks
A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding NetworksInternational Conference on Learning Representations (ICLR), 2022
Tommaso Salvatori
Yuhang Song
Yordan Yordanov
Beren Millidge
Zheng R. Xu
Lei Sha
Cornelius Emde
Rafal Bogacz
Thomas Lukasiewicz
322
17
0
16 Nov 2022
Far Away in the Deep Space: Dense Nearest-Neighbor-Based
  Out-of-Distribution Detection
Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection
Silvio Galesso
Max Argus
Thomas Brox
UQCV
267
15
0
12 Nov 2022
Self-Adapting Noise-Contrastive Estimation for Energy-Based Models
Self-Adapting Noise-Contrastive Estimation for Energy-Based Models
Na Xu
122
2
0
03 Nov 2022
DensePure: Understanding Diffusion Models towards Adversarial Robustness
DensePure: Understanding Diffusion Models towards Adversarial Robustness
Chaowei Xiao
Zhongzhu Chen
Kun Jin
Zhenghao Hu
Weili Nie
Mingyan D. Liu
Anima Anandkumar
Yue Liu
Basel Alomair
DiffM
289
48
0
01 Nov 2022
Learning Probabilistic Models from Generator Latent Spaces with Hat EBM
Learning Probabilistic Models from Generator Latent Spaces with Hat EBMNeural Information Processing Systems (NeurIPS), 2022
Mitch Hill
Erik Nijkamp
Jonathan Mitchell
Bo Pang
Song-Chun Zhu
802
13
0
29 Oct 2022
Maximum Likelihood Learning of Unnormalized Models for Simulation-Based
  Inference
Maximum Likelihood Learning of Unnormalized Models for Simulation-Based Inference
Pierre Glaser
Michael Arbel
Samo Hromadka
Arnaud Doucet
Arthur Gretton
331
4
0
26 Oct 2022
Adversarial Purification with the Manifold Hypothesis
Adversarial Purification with the Manifold HypothesisAAAI Conference on Artificial Intelligence (AAAI), 2022
Zhaoyuan Yang
Zhiwei Xu
Jing Zhang
Leonid Sigal
Peter Tu
AAML
382
9
0
26 Oct 2022
Disentangling Confidence Score Distribution for Out-of-Domain Intent
  Detection with Energy-Based Learning
Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based Learning
Yanan Wu
Zhiyuan Zeng
Keqing He
Yutao Mou
Pei Wang
Yuanmeng Yan
Weiran Xu
OODD
155
4
0
17 Oct 2022
Maximum entropy exploration in contextual bandits with neural networks
  and energy based models
Maximum entropy exploration in contextual bandits with neural networks and energy based models
A. Elwood
Marco Leonardi
A. Mohamed
A. Rozza
198
2
0
12 Oct 2022
Gradient-Guided Importance Sampling for Learning Binary Energy-Based
  Models
Gradient-Guided Importance Sampling for Learning Binary Energy-Based ModelsInternational Conference on Learning Representations (ICLR), 2022
Meng Liu
Haoran Liu
Shuiwang Ji
217
6
0
11 Oct 2022
Boosting Out-of-distribution Detection with Typical Features
Boosting Out-of-distribution Detection with Typical FeaturesNeural Information Processing Systems (NeurIPS), 2022
Yao Zhu
YueFeng Chen
Chuanlong Xie
Xiaodan Li
Rong Zhang
Hui Xue
Xiang Tian
Bolun Zheng
Yao-wu Chen
OODD
277
67
0
09 Oct 2022
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