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1205.1925
Cited By
Hamiltonian Annealed Importance Sampling for partition function estimation
9 May 2012
Jascha Narain Sohl-Dickstein
B. J. Culpepper
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Papers citing
"Hamiltonian Annealed Importance Sampling for partition function estimation"
18 / 18 papers shown
Flow Annealed Importance Sampling Bootstrap
International Conference on Learning Representations (ICLR), 2022
Laurence Illing Midgley
Vincent Stimper
G. Simm
Bernhard Schölkopf
José Miguel Hernández-Lobato
470
124
0
03 Aug 2022
Variational Inference with Locally Enhanced Bounds for Hierarchical Models
International Conference on Machine Learning (ICML), 2022
Tomas Geffner
Justin Domke
275
6
0
08 Mar 2022
Surrogate Likelihoods for Variational Annealed Importance Sampling
International Conference on Machine Learning (ICML), 2021
M. Jankowiak
Du Phan
BDL
294
14
0
22 Dec 2021
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise
Neural Information Processing Systems (NeurIPS), 2021
Guodong Zhang
Kyle Hsu
Jianing Li
Chelsea Finn
Roger C. Grosse
318
44
0
21 Jul 2021
MCMC Variational Inference via Uncorrected Hamiltonian Annealing
Neural Information Processing Systems (NeurIPS), 2021
Tomas Geffner
Justin Domke
366
43
0
08 Jul 2021
Accelerated Jarzynski Estimator with Deterministic Virtual Trajectories
Physical Review E (PRE), 2021
Nobumasa Ishida
Yoshihiko Hasegawa
66
0
0
28 Feb 2021
Scalable Approximate Inference and Some Applications
Jun Han
BDL
182
1
0
07 Mar 2020
Co-Generation with GANs using AIS based HMC
Neural Information Processing Systems (NeurIPS), 2019
Tiantian Fang
Alex Schwing
188
2
0
31 Oct 2019
Ergodic Inference: Accelerate Convergence by Optimisation
Yichuan Zhang
José Miguel Hernández-Lobato
BDL
314
9
0
25 May 2018
Gaussian Process Behaviour in Wide Deep Neural Networks
A. G. Matthews
Mark Rowland
Jiri Hron
Richard Turner
Zoubin Ghahramani
BDL
609
606
0
30 Apr 2018
Generalizing Hamiltonian Monte Carlo with Neural Networks
Daniel Levy
Matthew D. Hoffman
Jascha Narain Sohl-Dickstein
BDL
307
131
0
25 Nov 2017
Modified Hamiltonian Monte Carlo for Bayesian inference
Statistics and computing (Stat. Comput.), 2017
Tijana Radivojević
E. Akhmatskaya
455
35
0
13 Jun 2017
Stein Variational Adaptive Importance Sampling
J. Han
Qiang Liu
522
29
0
18 Apr 2017
Beyond Brightness Constancy: Learning Noise Models for Optical Flow
Dan Rosenbaum
Yair Weiss
56
2
0
11 Apr 2016
A Markov Jump Process for More Efficient Hamiltonian Monte Carlo
A. Berger
M. Mudigonda
M. DeWeese
Jascha Narain Sohl-Dickstein
290
1
0
13 Sep 2015
Variational Optimization of Annealing Schedules
Taichi Kiwaki
192
5
0
18 Feb 2015
Hamiltonian Monte Carlo Without Detailed Balance
International Conference on Machine Learning (ICML), 2014
Jascha Narain Sohl-Dickstein
M. Mudigonda
M. DeWeese
313
66
0
18 Sep 2014
Efficient Methods for Unsupervised Learning of Probabilistic Models
Jascha Narain Sohl-Dickstein
TPM
225
0
0
19 May 2012
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