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Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score Matching
[article]
2019
arXiv
pre-print
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However, demonstrations of such models in high quality sample synthesis of high dimensional data were lacking. ...
Energy-Based Models (EBMs) assign unnormalized log-probability to data samples. ...
LEARNING ENERGY-BASED MODEL WITH MULTISCALE DENOISING SCORE MATCHING
MULTISCALE DENOISING SCORE MATCHING Motivated by the analysis in section 2, we strive to develop an EBM based on denoising score matching ...
arXiv:1910.07762v2
fatcat:ogwmqrxyxnetrd3frhpyh7df4i
Learning Energy-Based Models in High-Dimensional Spaces with Multiscale Denoising-Score Matching
2023
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However, demonstrations of such models in the high-quality sample synthesis of high-dimensional data were lacking. ...
Energy-based models (EBMs) assign an unnormalized log probability to data samples. ...
Learning Energy-Based Model with Multiscale Denoising-Score Matching
Multiscale Denoising-Score Matching Motivated by the analysis in Section 2, we strive to develop an EBM based on denoising-score matching ...
doi:10.3390/e25101367
pmid:37895489
pmcid:PMC10606347
fatcat:63a3yk2psbcvrhpsqge56js5oy
Autoregressive Score Matching
[article]
2020
arXiv
pre-print
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Compared to previous score matching algorithms, our method is more scalable to high dimensional data and more stable to optimize. ...
To increase flexibility, we propose autoregressive conditional score models (AR-CSM) where we parameterize the joint distribution in terms of the derivatives of univariate log-conditionals (scores), which ...
DSM Denoising score matching (DSM) [28] is perhaps the most scalable score matching alternative available, and has been applied to high dimensional score matching problems [24] . ...
arXiv:2010.12810v1
fatcat:axo4u3wxyrcanggv35ews7apwq
Moment Matching Denoising Gibbs Sampling
[article]
2024
arXiv
pre-print
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The widely-used Denoising Score Matching (DSM) method for scalable EBM training suffers from inconsistency issues, causing the energy model to learn a 'noisy' data distribution. ...
Energy-Based Models (EBMs) offer a versatile framework for modeling complex data distributions. However, training and sampling from EBMs continue to pose significant challenges. ...
Energy-Based Models Energy-Based Models (EBMs) have attracted a lot of attention in the generative model literature [25, 46, 7, 35] . ...
arXiv:2305.11650v6
fatcat:mgjpdopjybehbc7bgpgrgrln7u
Improving Generative Adversarial Networks with Denoising Feature Matching
2017
International Conference on Learning Representations
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We estimate and track the distribution of these features, as computed from data, with a denoising auto-encoder, and use it to propose high-level targets for the generator. ...
the original and evaluate the hybrid criterion on the task of unsupervised image synthesis from datasets comprising a diverse set of visual categories, noting a qualitative and quantitative improvement in ...
We thank Vincent Dumoulin and Ishmael Belghazi for making available code and model parameters used in comparison to ALI, as well as Alec Radford for making available the code and model parameters for his ...
dblp:conf/iclr/Warde-FarleyB17
fatcat:2b3rrigydjgzxg3uip4gb7k63q
Concrete Score Matching: Generalized Score Matching for Discrete Data
[article]
2023
arXiv
pre-print
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2022 pre-print arXiv:2211.00802v1 fatcat:q4vs4qajqvc7jfcey45u3uu5j4
Finally, we introduce a new framework to learn such scores from samples called Concrete Score Matching (CSM), and propose an efficient training objective to scale our approach to high dimensions. ...
Empirically, we demonstrate the efficacy of CSM on density estimation tasks on a mixture of synthetic, tabular, and high-dimensional image datasets, and demonstrate that it performs favorably relative ...
Despite their key role in successfully scaling score-based generative models to high-dimensional datasets, we note two critical limitations of existing score matching techniques: (1) X must be continuous ...
arXiv:2211.00802v2
fatcat:qs2quqbb5vd3zc26pes7pxkgka
Probabilistic Mapping of Dark Matter by Neural Score Matching
[article]
2020
arXiv
pre-print
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on Neural Score Matching. ...
In this work, we present a novel methodology for addressing such inverse problems by combining elements of Bayesian statistics, analytic physical theory, and a recent class of Deep Generative Models based ...
In practice to learn this score efficiently, we adopt the noise-conditional Denoising Score Matching (DSM) technique proposed by (Lim et al., 2020) , and train a model to minimize the loss: L DSM = E ...
arXiv:2011.08271v1
fatcat:gpqiobtx2naxzifni37qqwndo4
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
[article]
2024
arXiv
pre-print
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In this paper, we propose Iterated Denoising Energy Matching (iDEM), an iterative algorithm that uses a novel stochastic score matching objective leveraging solely the energy function and its gradient ...
Specifically, iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our stochastic matching objective to further improve the ...
In addition, the authors thank Julius Berner for sharing their code for PIS and DDS. A.J.B. is supported through an NSERC Postdoctoral fellowship. ...
arXiv:2402.06121v1
fatcat:hwfdu5fccbfzzdehmyuxb6r5jm
Learning Gradually Non-convex Image Priors Using Score Matching
[article]
2023
arXiv
pre-print
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In this paper, we propose a unified framework of denoising score-based models in the context of graduated non-convex energy minimization. ...
Consequently, denoising score-based models essentially follow a graduated non-convexity heuristic. ...
Introduction Score matching (SM, Hyvärinen, 2005) has recently seen a renewed interest in computer vision and machine learning as it allows to fit a high-dimensional parametric distribution to a given ...
arXiv:2302.10502v1
fatcat:3amd4qdxgrhghbd3wnqixwlam4
Regularized estimation of image statistics by Score Matching
2010
Neural Information Processing Systems
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Score Matching is a recently-proposed criterion for training high-dimensional density models for which maximum likelihood training is intractable. ...
In addition, we introduce a regularization term for the Score Matching loss that enables its use for a broader range of problem by suppressing instabilities that occur with finite training sample sizes ...
(o) Denoised with model learned with CD-1 [21], (p) Basis Rotation [23], (q) and Score Matching with (near) optimal regularization. ...
dblp:conf/nips/KingmaL10
fatcat:zdjpi3c5mzcl3bzypou5qdfw6q
Heavy-tailed denoising score matching
[article]
2022
arXiv
pre-print
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Score-based model research in the last few years has produced state of the art generative models by employing Gaussian denoising score-matching (DSM). ...
For noise vector norm distributions, we demonstrate favourable concentration of measure in the high-dimensional spaces prevalent in deep learning. ...
By starting with the energy based model formulation p θ (x) = e −f θ (x) /Z θ , (2) for parameters θ ∈ R m , with m 1 for deep learning models, it is clear that s θ (x) = ∇ x log p θ (x) = −∇ x f θ (x) ...
arXiv:2112.09788v2
fatcat:og6ri3xlordfnj7ld6f5z6s5n4
Bi-level Score Matching for Learning Energy-based Latent Variable Models
[article]
2020
arXiv
pre-print
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Score matching (SM) provides a compelling approach to learn energy-based models (EBMs) by avoiding the calculation of partition function. ...
However, it remains largely open to learn energy-based latent variable models (EBLVMs), except some special cases. ...
Conclusion and Discussion We consider to extend score matching (SM) to learn energy-based latent variable models with a minimal model assumption. ...
arXiv:2010.07856v2
fatcat:imut4jzaybcbxbhfqksc2x6l4e
Sliced Score Matching: A Scalable Approach to Density and Score Estimation
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1905.07088v1 fatcat:el3d5zfvjngljc5wgencvrvpy4
In our experiments, we show sliced score matching can learn deep energy-based models effectively, and can produce accurate score estimates for applications such as variational inference with implicit distributions ...
Therefore, sliced score matching is amenable to more complex models and higher dimensional data compared to score matching. ...
RELATED WORK
SCALABLE SCORE MATCHING To the best of our knowledge, there are three existing methods that are able to scale up score matching to learning deep models on high dimensional data. ...
arXiv:1905.07088v2
fatcat:w4pbdtlf6jef7igz6sk3slaoii
A Variational Perspective on Diffusion-Based Generative Models and Score Matching
[article]
2021
arXiv
pre-print
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Discrete-time diffusion-based generative models and score matching methods have shown promising results in modeling high-dimensional image data. ...
Under this framework, we show that minimizing the score-matching loss is equivalent to maximizing a lower bound of the likelihood of the plug-in reverse SDE proposed by Song et al. (2021), bridging the ...
Score matching for energy-based models Besides the connection to diffusion models, score matching is also often used as a method for learning energy based models (EBM)-see for a comprehensive review on ...
arXiv:2106.02808v2
fatcat:fvscuw5qvzfddklwglowwgk744
Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs
2013
Neural Information Processing Systems
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Sparse high-dimensional data vectors are common in many application domains where a very large number of rarely non-zero features can be devised. ...
Unfortunately, this creates a computational bottleneck for unsupervised feature learning algorithms such as those based on auto-encoders and RBMs, because they involve a reconstruction step where the whole ...
In this form, we can see the similarity between score matching and ratio matching. ...
dblp:conf/nips/DauphinB13
fatcat:wsema3ystrgl3pimjv7pxoeizm
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