ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2007.09240
  4. Cited By
A new method for parameter estimation in probabilistic models: Minimum
  probability flow

A new method for parameter estimation in probabilistic models: Minimum probability flow

17 July 2020
Jascha Narain Sohl-Dickstein
P. Battaglino
M. DeWeese
ArXiv (abs)PDFHTML

Papers citing "A new method for parameter estimation in probabilistic models: Minimum probability flow"

13 / 13 papers shown
Redundancy Maximization as a Principle of Associative Memory Learning
Redundancy Maximization as a Principle of Associative Memory Learning
Mark Blümel
Andreas C. Schneider
Valentin Neuhaus
David A. Ehrlich
Marcel Graetz
Michael Wibral
Abdullah Makkeh
V. Priesemann
245
0
0
04 Nov 2025
Self-Supervised Discovery of Neural Circuits in Spatially Patterned Neural Responses with Graph Neural Networks
Self-Supervised Discovery of Neural Circuits in Spatially Patterned Neural Responses with Graph Neural Networks
Kijung Yoon
GNN
188
0
0
21 Sep 2025
From Denoising Diffusions to Denoising Markov Models
From Denoising Diffusions to Denoising Markov Models
Joe Benton
Yuyang Shi
Valentin De Bortoli
George Deligiannidis
Arnaud Doucet
DiffM
402
58
0
07 Nov 2022
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating
  and Auditing Generative Models
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative ModelsInternational Conference on Machine Learning (ICML), 2021
Ahmed Alaa
B. V. Breugel
Evgeny S. Saveliev
M. Schaar
428
278
0
17 Feb 2021
Score Matched Neural Exponential Families for Likelihood-Free Inference
Score Matched Neural Exponential Families for Likelihood-Free InferenceJournal of machine learning research (JMLR), 2020
Lorenzo Pacchiardi
Ritabrata Dutta
667
30
0
20 Dec 2020
A Generalization of Spatial Monte Carlo Integration
A Generalization of Spatial Monte Carlo IntegrationNeural Computation (Neural Comput.), 2020
Muneki Yasuda
Kei Uchizawa
186
8
0
04 Sep 2020
Efficient Learning of Generative Models via Finite-Difference Score
  Matching
Efficient Learning of Generative Models via Finite-Difference Score Matching
Tianyu Pang
Kun Xu
Chongxuan Li
Yang Song
Stefano Ermon
Jun Zhu
DiffM
350
63
0
07 Jul 2020
Your GAN is Secretly an Energy-based Model and You Should use
  Discriminator Driven Latent Sampling
Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent SamplingNeural Information Processing Systems (NeurIPS), 2020
Tong Che
Ruixiang Zhang
Jascha Narain Sohl-Dickstein
Hugo Larochelle
Liam Paull
Yuan Cao
Yoshua Bengio
DiffMDRL
457
126
0
12 Mar 2020
Inverse Ising inference from high-temperature re-weighting of
  observations
Inverse Ising inference from high-temperature re-weighting of observationsPhysical Review E (PRE), 2019
Junghyo Jo
Danh-Tai Hoang
V. Periwal
134
0
0
10 Sep 2019
Empirical Bayes Method for Boltzmann Machines
Empirical Bayes Method for Boltzmann Machines
Muneki Yasuda
T. Obuchi
129
2
0
14 Jun 2019
Deep Energy Estimator Networks
Deep Energy Estimator Networks
Saeed Saremi
Arash Mehrjou
Bernhard Schölkopf
Aapo Hyvarinen
285
81
0
21 May 2018
Variational Probability Flow for Biologically Plausible Training of Deep
  Neural Networks
Variational Probability Flow for Biologically Plausible Training of Deep Neural Networks
Zuozhu Liu
Tony Q.S. Quek
Shaowei Lin
150
3
0
21 Nov 2017
Optimal structure and parameter learning of Ising models
Optimal structure and parameter learning of Ising modelsScience Advances (Sci Adv), 2016
A. Lokhov
Marc Vuffray
Sidhant Misra
Michael Chertkov
524
90
0
15 Dec 2016
1
Page 1 of 1