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Learning Energy-Based Models by Diffusion Recovery Likelihood [article]

Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma
2020
Inspired by recent progress on diffusion probabilistic models, we present a diffusion recovery likelihood method to tractably learn and sample from a sequence of EBMs trained on increasingly noisy versions  ...  While energy-based models (EBMs) exhibit a number of desirable properties, training and sampling on high-dimensional datasets remains challenging.  ...  B EXPERIMENTAL DETAILS Model architecture. Our network structure is based on Wide ResNet (Zagoruyko & Komodakis, 2016) . Table 5 lists the detailed network structures of various resolutions.  ... 
doi:10.48550/arxiv.2012.08125 fatcat:q346wymmnnektfm4dhugblfehi

EBM Life Cycle: MCMC Strategies for Synthesis, Defense, and Density Modeling [article]

Mitch Hill, Jonathan Mitchell, Chu Chen, Yuan Du, Mubarak Shah, Song-Chun Zhu
2022 arXiv   pre-print
This work presents strategies to learn an Energy-Based Model (EBM) according to the desired length of its MCMC sampling trajectories.  ...  To achieve these outcomes, we introduce three novel methods of MCMC initialization for negative samples used in Maximum Likelihood (ML) learning.  ...  This approach is taken by Energy-Based Models (EBMs) [1] , normalizing flows [2] , score-based models [3, 4] , and auto-regressive models [5] , as well as by Variational Auto-encoders (VAEs) [6]  ... 
arXiv:2205.12243v1 fatcat:sbntc3otc5gi5dvjan4z24mt2m

A New Search for Neutrino Point Sources with IceCube [article]

Chiara Bellenghi, Theo Glauch, Christian Haack, Tomas Kontrimas, Hans Niederhausen, Rene Reimann, Martin Wolf
2021 arXiv   pre-print
We replaced the usual Gaussian approximations of IceCube's point spread function with precise numerical representations, obtained from simulations, and combined them with new machine learning-based estimates  ...  While eight years have passed since IceCube discovered a diffuse flux of high-energy astrophysical neutrinos, the sources of the vast majority of these neutrinos remain unknown.  ...  and truncated energy, a traditional likelihood-based algorithm (right).  ... 
arXiv:2107.08700v2 fatcat:bsfi3s7mjvgilfoujtwjrwje4u

Energy-Based Generative Cooperative Saliency Prediction [article]

Jing Zhang and Jianwen Xie and Zilong Zheng and Nick Barnes
2022 arXiv   pre-print
Specifically, we propose a generative cooperative saliency prediction framework, where a conditional latent variable model (LVM) and a conditional energy-based model (EBM) are jointly trained to predict  ...  In this paper, to model the uncertainty of visual saliency, we study the saliency prediction problem from the perspective of generative models by learning a conditional probability distribution over the  ...  The maximum likelihood learning of the energy-based model typically requires iterative MCMC sampling, which is computationally challenging.  ... 
arXiv:2106.13389v2 fatcat:lwvah5fvrzbxxfyfdphujxjjai

Learning Energy-based Spatial-Temporal Generative ConvNets for Dynamic Patterns [article]

Jianwen Xie, Song-Chun Zhu, Ying Nian Wu
2019 arXiv   pre-print
We show that an energy-based spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns.  ...  The model can be learned from the training video sequences by an "analysis by synthesis" learning algorithm that iterates the following two steps.  ...  The work is supported by ONR MURI N00014-16-1-2007, and DARPA ARO W911NF-16-1-0579.  ... 
arXiv:1909.11975v1 fatcat:celbwe5qfjabjhyoiagr464fxi

Guest Editorial: Special Issue in Memory of Mila Nikolova

Raymond H. Chan, Albert Cohen, Jalal Fadili, Alfred Hero, Gabriele Steidl
2020 Journal of Mathematical Imaging and Vision  
The authors of Stable backward diffusion models that minimise convex energies (L. Bergerhoff, M. Cárdenas, J. Weickert, M.  ...  The challenging problem of fine structure detection with applications to bituminous surfacing crack recovery is examined in A nonlocal Laplacian-based model for bituminous surfacing crack recovery and  ... 
doi:10.1007/s10851-020-00981-6 fatcat:c5xojwqquvce5lyhujgcsd5u3e

Recovery of relative depth from a single observation using an uncalibrated (real-aperture) camera

Vinay P. Namboodiri, Subhasis Chaudhuri
2008 2008 IEEE Conference on Computer Vision and Pattern Recognition  
Because of ill-posedness we propose a graph-cuts based method for inferring the depth in the scene using the amount of diffusion as a data likelihood and a smoothness condition on the depth in the scene  ...  We stabilize the reverse heat equation by considering the gradient degeneration as an effective stopping criterion. The amount of (inverse) diffusion is actually a measure of relative depth.  ...  A successful approach for single image based structure recovery has been that by Criminisi et al. [5] .  ... 
doi:10.1109/cvpr.2008.4587779 dblp:conf/cvpr/NamboodiriC08 fatcat:jtripwnefnbsxd6l3wzastx36q

Latent Diffusion Energy-Based Model for Interpretable Text Modeling [article]

Peiyu Yu, Sirui Xie, Xiaojian Ma, Baoxiong Jia, Bo Pang, Ruiqi Gao, Yixin Zhu, Song-Chun Zhu, Ying Nian Wu
2022 arXiv   pre-print
Inspired by the recent efforts that leverage diffusion recovery likelihood learning as a cure for the sampling issue, we introduce a novel symbiosis between the diffusion models and latent space EBMs in  ...  a variational learning framework, coined as the latent diffusion energy-based model.  ...  Wu was supported by NSF DMS-2015577. We would like to thank the anonymous reviewers for their constructive comments.  ... 
arXiv:2206.05895v3 fatcat:kktenwa5qbevtbpuzcik6rrx4a

Neural network analysis of X-ray polarimeter data [article]

Abel L. Peirson
2022 arXiv   pre-print
and model uncertainties.  ...  Deep neural network based classifiers can be used to select against these events to improve energy resolution and polarization sensitivity.  ...  Photon energy. Energy recovery can be adversely affected by tail events.  ... 
arXiv:2206.10537v1 fatcat:egtxzwoklvesjgy5u7va2pj2s4

Introducing Dynamic Prior Knowledge to Partially-Blurred Image Restoration [chapter]

Hongwei Zheng, Olaf Hellwich
2006 Lecture Notes in Computer Science  
To estimate the point spread function (PSF), a parametric model space is introduced to reduce the searching uncertainty for PSF model selection.  ...  Maximum a posteriori estimation of parameters in Bayesian regularization is equal to minimizing energy of a dataset for a given number of classes.  ...  Based on the model space, an unsupervised self-initializing PSF learning term can learn a PSF parametric model according to the following energy functional, F (ĥ|f , g) = 1 2 Ω (g −ĥ * f ) 2 dA + β Ω |  ... 
doi:10.1007/11861898_12 fatcat:lyb7ptyzdbbvvdm2b7h2xtxgqq

2021 Index IEEE Transactions on Signal Processing Vol. 69

2021 IEEE Transactions on Signal Processing  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  ., +, TSP 2021 5830-5845 Location based services Diffusion SLAM: Localizing Diffusion Sources From Samples Taken by Location-Unaware Mobile Sensors.  ...  Imtiaz, H., +, TSP 2021 6355-6370 Diffusion Diffusion SLAM: Localizing Diffusion Sources From Samples Taken by Location-Unaware Mobile Sensors.  ... 
doi:10.1109/tsp.2022.3162899 fatcat:kcubj566gzb4zkj7xb5r5we3ri

Minimum Probability Flow Learning [article]

Jascha Sohl-Dickstein, Peter Battaglino, Michael R. DeWeese
2011 arXiv   pre-print
In the Ising model case, current state of the art techniques are outperformed by at least an order of magnitude in learning time, with lower error in recovered coupling parameters.  ...  Score matching, minimum velocity learning, and certain forms of contrastive divergence are shown to be special cases of this learning technique.  ...  Schapire and William Bialek for use of their Ising model coupling parameters; Jonathon Shlens for useful discussion and ground truth for his Ising model convergence times; Bruno Olshausen, Anthony Bell  ... 
arXiv:0906.4779v4 fatcat:vbhj2juwcfgmvldujod7sklzu4

Table of Contents

2021 IEEE Transactions on Signal Processing  
Blu Diffusion SLAM: Localizing Diffusion Sources From Samples Taken by Location-Unaware Mobile Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Trung Marginal Likelihood Maximization Based Fast Array Manifold Matrix Learning for Direction of Arrival Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tsp.2021.3136800 fatcat:zhf46mb3rbdlnnh3u2xizgxof4

Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations [article]

Mateusz Wilinski, Andrey Y. Lokhov
2021 arXiv   pre-print
We address the problem of learning of a spreading model such that the predictions generated from this model are accurate and could be subsequently used for the optimization, and control of diffusion dynamics  ...  We introduce a computationally efficient algorithm, based on a scalable dynamic message-passing approach, which is able to learn parameters of the effective spreading model given only limited information  ...  In the recent years, the problem of diffusion model recovery from full observations has been extensively addressed through a series of works focusing on heuristic and exact algorithms, largely based on  ... 
arXiv:2007.06557v3 fatcat:g3xamqwhujcotg4yihsiugkvx4

Learning Energy-Based Models With Adversarial Training [article]

Xuwang Yin, Shiying Li, Gustavo K. Rohde
2022 arXiv   pre-print
We study a new approach to learning energy-based models (EBMs) based on adversarial training (AT).  ...  We show that (binary) AT learns a special kind of energy function that models the support of the data distribution, and the learning process is closely related to MCMC-based maximum likelihood learning  ...  .: Learning latent space energy-based prior model.  ... 
arXiv:2012.06568v3 fatcat:ba4ya6kxsjd2ti2bw4borauc34
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