Cited by 1,011Neural Networks and ApplicationsTopic ModelingImage and Signal Denoising Methods

The DOI matches its Crossref record; 27 of the 29 references it cites were found in a registry or an index.

A Connection Between Score Matching and Denoising Autoencoders

Vincent, Pascal·2011·Neural Computation

Open this paper in CoChat — free account

Abstract

Denoising autoencoders have been previously shown to be competitive alternatives to restricted Boltzmann machines for unsupervised pretraining of each layer of a deep architecture. We show that a simple denoising autoencoder training criterion is equivalent to matching the score (with respect to the data) of a specific energy-based model to that of a nonparametric Parzen density estimator of the data. This yields several useful insights. It defines a proper probabilistic model for the denoising autoencoder technique, which makes it in principle possible to sample from them or rank examples by their energy. It suggests a different way to apply score matching that is related to learning to denoise and does not require computing second derivatives. It justifies the use of tied weights between the encoder and decoder and suggests ways to extend the success of denoising autoencoders to a larger family of energy-based models.

Integrity signals

Registry

Crossref · 10.1162/neco_a_00142

Cites

27 of 27 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,093
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
Self-citation
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436
Self-citation
Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 3×
“1Specifically SM (Hyvärinen, 2005), minimum velocity learning (Movellan, 2008), and certain forms of contrastive divergence (Hinton, 2002; Welling & Hinton, 2002) are all recast as minimizing the Kullback-Leibler divergence between the data distribution and the distribution obtained after running, for infinitesimal ti…”
BackgroundMethod
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
Hybrid Monte Carlo
Duane, S., Kennedy, A. D., Pendleton, B. J., & Roweth, D. (1987) · Physics Letters B · doi:10.1016/0370-2693(87)91197-x · Cited by 4,081
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051
Self-citation
Why Does Unsupervised Pre-training Help Deep Learning?
Erhan, D., Bengio, Y., Courville, A. C., Manzagol, P.-A., Vincent, P., & Samy Bengio, A. (2010) · Cited by 2,180
Self-citation
Advances in Neural Information Processing Systems 18
Murray, I., Mackay, D., Zoubin Ghahramani, A., & Skilling, J. (2006) · Cited by 1,927

Flagged first, then by citations

Show all 29 references

2 of the 29 references couldn’t be matched to a known paper.

Research works printed but not linked: 4

Erhan D., 2010, Journal of Machine Learning Research, 11, 625
Gallinari P., 1987, Proceedings of COGNITIVA 87
Hyvärinen A., 2005, Journal of Machine Learning Research, 6, 695
Vincent P., 2010, Journal of Machine Learning Research, 11, 3371

Sorted by citations

Items 801–900 of 1,003

Unnormalized Variational BayesPreprint
Saremi, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.15130 · Cited by 1
Semantics-Guided Exploration of Latent Spaces for Shape Synthesis
Jahan, T. (2020) · doi:10.22215/etd/2020-13898
Background and Related Work
Mou, L., & Jin, Z. G. (2018) · SpringerBriefs in computer science · doi:10.1007/978-981-13-1870-2_2
The Sparse Manifold Transform and Unsupervised Learning for Signal Representation
Chen, Y. (2020) · eScholarship (California Digital Library)
Bijective-Contrastive Estimation
Lim, J. H., Huang, C.-W., Courville, A. C., & Pal, C. (2020)
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal EffectsPreprint
Zhang, Y., Berrevoets, J., & van der Schaar, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.03039
Adversarial Stein Training for Graph Energy ModelsPreprint
Shankar, S. S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.12982
ITÔN: End-to-end audio generation with Itô stochastic differential equations
Shi, Z., & Wu, S. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103781
A Tale of Three Families: Discriminative, Descriptive, and Generative Models
Zhu, S. C., & Wu, Y. N. (2023) · Springer eBooks · doi:10.1007/978-3-030-96530-3_12
Deep Image Models
Zhu, S. C., & Wu, Y. N. (2023) · Springer eBooks · doi:10.1007/978-3-030-96530-3_11
Score-based Image-to-Image Regression with Synchronized Diffusion
Xin, H., & Zhu, M. Y. (2022) · International Conference on Machine Learning and Applications · doi:10.1109/icmla55696.2022.00056 · Cited by 1
SchröWave: Realistic voice generation by solving two-stage conditional Schrödinger bridge problems
Shi, Z., & Wu, S. (2023) · Digital Signal Processing · doi:10.1016/j.dsp.2023.104175
Versatile Molecular Editing via Multimodal and Group-optimized Generative LearningPreprint
Lin, X., Xia, Y., Huang, Y., Liu, S., Chen, M., Ni, N., Wang, Z., Gao, Y. Q., & Zhang, J. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-j2n6l
Complex priors and flexible inference in recurrent circuits with dendritic nonlinearitiesPreprint
Lyo, B. S. H., & Savin, C. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.03.560701
A mortized inference for structured deep generative models
Wu, H. (2023) · Digital Repository Service (Northeastern University) · doi:10.17760/d20560797
Frederic Schoenberg and Weng Kee Wong’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’
Schoenberg, F. P., & Wong, W. K. (2023) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1093/jrsssb/qkad158
Daniela Cialfi’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’
Cialfi, D. (2023) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1093/jrsssb/qkad147
Yingnian Wu and Weng Kee Wong’s contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’
Wu, Y., & Wong, W. K. (2023) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1093/jrsssb/qkad163
Report on 2303.05376v1
(2023) · SciPost Physics · doi:10.21468/scipost.report.7781
Report on 2303.05376v1
(2023) · SciPost Physics · doi:10.21468/scipost.report.7640
Contour wavelet diffusion – a fast and high-quality facial expression generation model
Xu, C., & Zou, Y. (2024) · Connection Science · doi:10.1080/09540091.2024.2316023
Subclass Feature Evaluation of Latent Diffusion Model
Foo, C. B., & Christopher, L. (2023) · IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr60534.2023.10440695
Single Image Reflection Removal with Diffusion Model
Mao, X., Zhan, Y., & Gao, H. (2023) · International Conference on Computing and Pattern Recognition (ICCPR) · doi:10.1145/3633637.3633700
Report on 2306.03933v3
Mikuni, V. M., & Nachman, B. (2023) · SciPost Physics · doi:10.21468/scipost.report.8173
Report on 2306.03933v3
Mikuni, V. M., & Nachman, B. P. (2023) · SciPost Physics · doi:10.21468/scipost.report.8056
Comment on egusphere-2023-2261
Finn, T. S., Disson, L., Farchi, A., et al. (2024) · doi:10.5194/egusphere-2023-2261-rc1
Fast image recovery using plug-and-play multi-scale energy (MuSE) framework
Chand, J. R., & Jacob, M. (2023) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf59524.2023.10476808
A Score-Based Particle Method for Homogeneous Landau EquationPreprint
Huang, Y., & Wang, L. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4856369
Versatile Molecular Editing via Multimodal and Group-optimized Generative LearningPreprint
Lin, X., Xia, Y., Huang, Y., Liu, S., Zhang, J., & Gao, Y. Q. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2023-j2n6l-v2
Synthetic Data Generation for Accurate, Fair, and Private Recommender Systems
Lilienthal, D. (2024) · San José State University ScholarWorks (San Jose State University) · doi:10.31979/etd.ggt3-adtq
Error Bound Based Noise Schedule Design in Diffusion Models
Liu, L., & Yuan, C. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650926
The oneirogen hypothesis: modeling the hallucinatory effects of classical psychedelics in terms of replay-dependent plasticity mechanismsPreprint
Bredenberg, C., Normandin, F., Richards, B. A., & Lajoie, G. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.27.615483
Learning Quantized Adaptive Conditions for Diffusion Models
Liang, Y., Tian, Y., Yu, L., Tang, H., Hu, J., Fang, X., & Chen, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73004-7_21
Fundamentals and Applications of Deep Generative Models
Kaneko, T. (2024) · Denshi Jouhou Tsuushin Gakkai Tsuushin Sosaieti magajin/B-plus/Denshi Jouhou Tsuushin Gakkai Tsuushin Sosaeti magajin · doi:10.1587/bplus.18.251
Direct Diffusion Bridge for High MB-count Image Generation in Ultrasound Localization Microscopy
Zhang, S., Lan, H., Wang, R., Wei, X., Chen, Z., Liu, Y., He, Z., & Luo, J. (2024) · IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS) · doi:10.1109/uffc-js60046.2024.10793898
Score Matching with Deep Neural Networks: A Non-Asymptotic Analysis
Crafts, E. S., Zhang, X., & Zhao, B. (2024) · IEEE Information Theory Workshop (ITW) · doi:10.1109/itw61385.2024.10807035
AnoGrad: Time Series Anomaly Detection with Score-Based Generative Model
Mi, J., Zhu, X., Meng, Z., & Zheng, H. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831310
Mitigating Exposure Bias in Score-Based Generation of Molecular Conformations
Wang, S., Wang, C., Zhao, Z., Zhang, J., & Cai, W. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831165
Three-Dimensional Diffusion-Weighted Multi-Slab MRI with Slice Profile Compensation Using Deep Energy Model
Ghorbani, R., Chand, J. R., Lee, C., Jacob, M., & Mani, M. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10981200
Non-invasive blood glucose detection by using multiwavelength optical imaging
Cao, F., Kong, L., Liu, H., Liu, H., Wang, Y., Liu, Z., Wang, H., Li, J. K., Dong, L., Liu, M., et al. (2025) · Sixth Symposium on Novel Optoelectronic Detection Technology and Applications · doi:10.1117/12.3057096
Deep Unfolding Using Score-based Generative Networks for Automotive Radar Interference Mitigation
Wei, X., Youn, J., Overdevest, J., Li, J., Ravindran, S., & van Sloun, R. J. G. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889785 · Cited by 1
Monte Carlo Score Matching for Image Generation
Shetty, N., & Seelamantula, C. S. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889041 · Cited by 2
MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model
Chand, J. R., & Jacob, M. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889960
A Synthetic Data-enhanced Differential Privacy Reinforcement Learning Policy Optimization Method
Liang, Y., Yuan, L., Yin, L., Sun, Z., Luo, W., Li, C., He, Y., & Chao, J. (2024) · IEEE Smart World Congress (SWC) · doi:10.1109/swc62898.2024.00348
MR Spatiospectral Reconstruction Integrating Subspace Modeling and Self-Supervised Spatiotemporal Denoising
Zhao, R., Wang, Z., Anderson, A. T., Huesmann, G., & Lam, F. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3555928
The diffusion model and its applications in bioinformatics
Wu, Z., Fu, Y., Chen, J., Ma, J., Wu, W., & Deng, M. (2025) · Scientia Sinica Mathematica · doi:10.1360/ssm-2024-0316
Subspace diffusion posterior sampling for travel-time tomography
Cao, X., & Zhang, X. (2025) · Inverse Problems · doi:10.1088/1361-6420/adcae2
Partial Fourier Reconstruction Using Diffusion Models
王, 志. (2025) · Advances in Applied Mathematics · doi:10.12677/aam.2025.145235
Exploring Bidirectional Bounds for Minimax-Training of Energy-Based Models
Geng, C., Wang, J., Chen, L., Gao, Z., Frellsen, J., & Hauberg, S. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02460-0 · Cited by 1
Diffusion models
Heckel, R. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780198947172.003.0010
Learning to solve inverse problems end-to-end
Heckel, R. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780198947172.003.0006 · Cited by 1
Robustness to perturbations
Heckel, R. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780198947172.003.0013
Plug-and-play methods
Heckel, R. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780198947172.003.0005
Introduction
Heckel, R. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780198947172.003.0001
Deep Learning for Computational Imaging
Heckel, R. (2025) · doi:10.1093/oso/9780198947172.001.0001
List of Figures
Heckel, R. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780198947172.002.0006
Unrolled neural networks
Heckel, R. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780198947172.003.0007
Mathematical background
Heckel, R. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780198947172.005.0001
Solving inverse problems with optimization tasks
Heckel, R. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780198947172.003.0002
Copyright Page
Heckel, R. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780198947172.002.0003
Datasets and evaluation of image reconstruction methods
Heckel, R. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780198947172.003.0014
Coordinate-based multi-layer perceptrons
Heckel, R. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780198947172.003.0012
Signal reconstruction with un-trained neural networks
Heckel, R. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780198947172.003.0011
Advanced reconstruction problems
Heckel, R. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780198947172.003.0015
Sparse modelling and sparse signal reconstruction
Heckel, R. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780198947172.003.0004
Signal reconstruction by imposing generative priors
Heckel, R. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780198947172.003.0009
Solving optimization problems
Heckel, R. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780198947172.003.0003
Theoretical analysis of the denoising autoencoder using Tweedie’s formula
Aishima, Y., & Ikeda, K. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.109287
The oneirogen hypothesis: modeling the hallucinatory effects of classical psychedelics in terms of replay-dependent plasticity mechanismsPreprint
Bredenberg, C., Normandin, F., Richards, B. A., & Lajoie, G. (2025) · eLife · doi:10.7554/elife.105968.1
An Introduction to the Langevin Monte Carlo Method and Its Applications
Majima, K. (2025) · The Brain & Neural Networks · doi:10.3902/jnns.32.99
Extraction of beat features for piano teaching performance based on improved autoencoder
Yang, Y. (2025) · International Journal of Information and Communication Technology · doi:10.1504/ijict.2025.10072282
Enhancing Adversarial Robustness With Energy-Based Models: a Novel Training Approach for Input Purification
Tabriz, R. H., Amini, S., & Kazemi, R. (2025) · Iran Workshop on Communication and Information Theory (IWCIT) · doi:10.1109/iwcit67595.2025.11073010
Model Free Prediction With Uncertainty Assessment
Jiao, Y., Kang, L., Liu, J., Peng, H., & Zuo, H. (2025) · IEEE Transactions on Information Theory · doi:10.1109/tit.2025.3588335 · Cited by 2
Nonlinear denoising score matching for enhanced learning of structured distributions
Birrell, J., Katsoulakis, M. A., Rey-Bellet, L., Zhang, B. J., & Zhu, W. (2025) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2025.118226 · Cited by 2
Flexible Geometric Guidance for Probabilistic Human Pose Estimation with Diffusion Models
Snelgar, F., Xu, M., Gould, S. J., Zheng, L., & Asthana, A. (2025) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg61629.2025.11099197
EVPGS: Enhanced View Prior Guidance for Splatting-based Extrapolated View Synthesis
Li, J., Wang, F., Qu, X., Wu, C., Liu, L., & Liu, T. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01529
Collaborative Diffusion with Child Net for Personalized Facial Editing: Integrating Genetic Information
Priyadharshini, H. R. S., Laxmanan, S., Kumar, S. V. M., Karthik, R. R. M., Sruthi, C. J., & Padmavathi, S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-4139-0_24
PAINT: Prior-Aided Alternate Iterative NeTwork for Ultra-Low-Dose CT Imaging Using Diffusion Model-Restored Sinogram
Chen, K., Zhang, W., Deng, Z., Zhou, Y., & Zhao, J. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3599508
Diffusion Models in a Nutshell: A Tutorial
Ali, A. M., Koubâa, A., & Benjdira, B. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-90573-5_5
DiffNO: Neural Operator Learning Using Physically Structured Constrained Diffusion Model
Feng, Z., & Zhang, X. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-99857-7_15
Neural correction operator: A reliable and fast approach for electrical impedance tomography
Bhat, A., Chen, K., & Wang, C. (2026) · Journal of Computational Physics · doi:10.1016/j.jcp.2026.115039 · Cited by 2
Diffusion Meets Options: Hierarchical Generative Skill Composition for Temporally-Extended Tasks
Feng, Z., Luan, H., Yuchen, K., & Soh, H. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127641
Memory-Efficient Image Reconstruction using Diffusion Models for Accelerated 3D Non-Cartesian UTE imaging
Petersen, J., Grodzki, D. M., Küstner, T., & Sommer, S. (2025) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-026-01403-1
Energy-based models for inverse imaging problems
Habring, A., Holler, M., Pock, T., & Zach, M. (2025) · Handbook of numerical analysis · doi:10.1016/bs.hna.2025.09.001
Research on soil spotted degradation prediction in qinling tea-producing area of China based on deep learning
Zhang, L., Zhang, T., & Guo, L. (2025) · Frontiers in Environmental Science · doi:10.3389/fenvs.2025.1649528
Research progress of diffusion model in crowd activity analysis
Wu, F., & Zhang, G. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-026-03209-y
S-NODE: Explicit and Reversible Image Translation Encoding With Neural ODEs
Wang, X., Pang, D., You, Z., Guan, X., & Le, X. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3619457
Generalized Score Matching: Bridging $f$-Divergence and Statistical Estimation Under Correlated Noise
Shen, Y., Gan, L., & Ling, C. (2025) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit63088.2025.11195353
Score-Based Manifold Projection for Diffusion-Based Inverse Problems
Hamidi, S. M., & Yang, E. (2025) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit63088.2025.11195578
Toward a Unified Framework for Consistency Generative Modeling
Dou, H., Lu, J., Du, J., Fu, C., Yao, W., Li, H., & Deng, Y. C. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3624330
Wavelet-Driven GAN with Attention Mechanism for Improved Raindrop Removal
Balamurugan, M., K., S., & Gopi, V. P. (2026) · IEEE Latin America Transactions · doi:10.21203/rs.3.rs-7767719/v1
Efficient Diffusion Bridge with Initial-Value Correction Strategy for Super-Resolution
Cai, J., Yue, L., Tai, W., He, X., Zhong, T., Chen, J., & Zhou, F. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11208927
“Set it up”: Functional object arrangement with compositional generative models
Xu, Y., Mao, J., Li, L., Du, Y., Lozano‐Pérez, T., Kaelbling, L. P., & Hsu, D. (2025) · The International Journal of Robotics Research · doi:10.1177/02783649251378198
Robust One-step Speech Enhancement via Consistency Distillation
Xu, L. J., Yan, L., & Kleijn, W. B. (2025) · IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) · doi:10.1109/waspaa66052.2025.11230988
Enhancing Massive MIMO Symbol Detection in Unknown Noise Environments: A Generative Modeling Approach
Olutayo, T., & Champagne, B. (2025) · IEEE Transactions on Communications · doi:10.1109/tcomm.2025.3637109
Posterior Sampling with Latent Diffusion for Microwave Brain Imaging
Guo, R., Kvich, Y., Zhang, Y., Huang, T., Li, M., & Eldar, Y. C. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11254903
Conditional Score-based Diffusion Models for Lung CT Scans Generation
Cardoso, A. F., Sousa, P., Oliveira, H. P., & Pereira, T. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11254813
Machine learning techniques for crop identification and classification
Arun, P. V., Hari, J., & Zhao, D. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-34113-7.00016-x
Efficient Kilometer‐Scale Precipitation Downscaling With Conditional Wavelet Diffusion
Yi, C., Yu, M., Qian, W., Wen, Y., & Yang, H. (2026) · Journal of Geophysical Research Machine Learning and Computation · doi:10.1029/2025jh000941 · Cited by 6
Score-based physics-informed learning framework for stochastic dynamics
Kou, H., Liu, F., Wu, F., & Zhang, X. (2025) · Journal of Computational Physics · doi:10.1016/j.jcp.2025.114585

Authors: Vincent, P.
Affiliations: Université de Montréal (CA)
Corresponding author: Vincent, Pascal
Venue: Neural Computation
Publisher: MIT Press
Published: 2011-07
Volume: 23
Issue: 7
Pages: 1661-1674
ISSN: 0899-7667 (ISSN-L)
1530-888X
DOI: 10.1162/neco_a_00142
OpenAlex: W2013035813
Cited by: 1,011
References: 29
Citations by year: 2026: 131 · 2025: 254 · 2024: 198 · 2023: 126 · 2022: 46 · 2021: 59 · 2020: 52 · 2019: 29 · 2018: 20 · 2017: 22 · 2016: 20 · 2015: 14 · 2014: 9 · 2013: 14 · 2012: 9
FWCI: 11.49
Type: journal article
Language: en
Open access: closed
Locations: Neural Computation (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
Keywords: score matching, denoising autoencoder, energy-based models, unsupervised pre-training, restricted Boltzmann machine, probabilistic models, deep learning architectures
MeSH terms: Computer Simulation, Models, Statistical

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

A Connection Between Score Matching and Denoising Autoencoders

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks