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Neural Importance Sampling

11 August 2018

Markus Gross

Papers citing "Neural Importance Sampling"

35 / 185 papers shown

Title
Longitudinal Variational Autoencoder S. Ramchandran Gleb Tikhonov Kalle Kujanpää Miika Koskinen Harri Lähdesmäki DRL CML BDL 84 37 0 17 Jun 2020
Learning Neural Light Transport Paul Sanzenbacher L. Mescheder Andreas Geiger 29 7 0 05 Jun 2020
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities Jonas Köhler Leon Klein Frank Noé DRL 140 279 0 03 Jun 2020
Neural Control Variates Thomas Müller Fabrice Rousselle Jan Novák A. Keller BDL 107 56 0 02 Jun 2020
Fully probabilistic quasar continua predictions near Lyman-α with conditional neural spline flows D. Reiman John Tamanas J. Prochaska Dominika Ďurovčíková 57 6 0 31 May 2020
Exhaustive Neural Importance Sampling applied to Monte Carlo event generation Sebastian Pina-Otey F. Sánchez Thorsten Lux Vicens Gaitán 39 10 0 26 May 2020
Survey: Machine Learning in Production Rendering Shilin Zhu 3DV AI4CE 16 1 0 26 May 2020
Online path sampling control with progressive spatio-temporal filtering J. Pantaleoni 42 14 0 15 May 2020
Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space Nikolas Nusken Lorenz Richter AI4CE 88 112 0 11 May 2020
Efficient sampling generation from explicit densities via Normalizing Flows Sebastian Pina-Otey Thorsten Lux F. Sánchez Vicens Gaitán 10 0 0 23 Mar 2020
VegasFlow: accelerating Monte Carlo simulation across multiple hardware platforms Stefano Carrazza J. Cruz-Martinez 65 26 0 28 Feb 2020
Woodbury Transformations for Deep Generative Flows You Lu Bert Huang 73 16 0 27 Feb 2020
VFlow: More Expressive Generative Flows with Variational Data Augmentation Jianfei Chen Cheng Lu Biqi Chenli Jun Zhu Tian Tian DRL 81 63 0 22 Feb 2020
Likelihood-free inference of experimental Neutrino Oscillations using Neural Spline Flows Sebastian Pina-Otey F. Sánchez Vicens Gaitán Thorsten Lux 15 3 0 21 Feb 2020
Stochastic Normalizing Flows Hao Wu Jonas Köhler Frank Noé 152 186 0 16 Feb 2020
Targeted free energy estimation via learned mappings Peter Wirnsberger A. J. Ballard George Papamakarios Stuart Abercrombie S. Racanière Alexander Pritzel Danilo Jimenez Rezende Charles Blundell 88 93 0 12 Feb 2020
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow Didrik Nielsen Ole Winther MQ 235 13 0 06 Feb 2020
Normalizing Flows on Tori and Spheres Danilo Jimenez Rezende George Papamakarios S. Racanière M. S. Albergo G. Kanwar P. Shanahan Kyle Cranmer TPM 93 157 0 06 Feb 2020
i-flow: High-dimensional Integration and Sampling with Normalizing Flows Christina Gao J. Isaacson Claudius Krause AI4CE 83 111 0 15 Jan 2020
Invertible Generative Modeling using Linear Rational Splines H. M. Dolatabadi S. Erfani C. Leckie 110 65 0 15 Jan 2020
Normalizing Flows for Probabilistic Modeling and Inference George Papamakarios Eric T. Nalisnick Danilo Jimenez Rezende S. Mohamed Balaji Lakshminarayanan TPM AI4CE 219 1,720 0 05 Dec 2019
Neural Density Estimation and Likelihood-free Inference George Papamakarios BDL DRL 100 47 0 29 Oct 2019
Distilling Importance Sampling for Likelihood Free Inference D. Prangle Cecilia Viscardi 81 3 0 08 Oct 2019
Identifying through Flows for Recovering Latent Representations Shen Li Bryan Hooi Gim Hee Lee DRL OOD 92 14 0 27 Sep 2019
Normalizing Flows: An Introduction and Review of Current Methods I. Kobyzev S. Prince Marcus A. Brubaker TPM MedIm 100 58 0 25 Aug 2019
Stochastic Neural Network with Kronecker Flow Chin-Wei Huang Ahmed Touati Pascal Vincent Gintare Karolina Dziugaite Alexandre Lacoste Aaron Courville BDL 67 8 0 10 Jun 2019
Neural Spline Flows Conor Durkan Artur Bekasov Iain Murray George Papamakarios DRL 228 778 0 10 Jun 2019
Cubic-Spline Flows Conor Durkan Artur Bekasov Iain Murray George Papamakarios TPM 104 58 0 05 Jun 2019
Hierarchical Importance Weighted Autoencoders Chin-Wei Huang Kris Sankaran Eeshan Gunesh Dhekane Alexandre Lacoste Aaron Courville BDL 61 15 0 13 May 2019
Autoregressive Energy Machines C. Nash Conor Durkan 73 55 0 11 Apr 2019
A RAD approach to deep mixture models Laurent Dinh Jascha Narain Sohl-Dickstein Hugo Larochelle Razvan Pascanu 93 46 0 18 Mar 2019
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design Jonathan Ho Xi Chen A. Srinivas Yan Duan Pieter Abbeel DRL 102 451 0 01 Feb 2019
Deep-learning the Latent Space of Light Transport Pedro Hermosilla S. Maisch Tobias Ritschel Timo Ropinski 3DPC 50 23 0 12 Nov 2018
Learning to Importance Sample in Primary Sample Space Q. Zheng Matthias Zwicker 52 66 0 23 Aug 2018
Integral Equations and Machine Learning A. Keller Ken Dahm 63 12 0 17 Dec 2017