Inference Compilation and Universal Probabilistic Programming

31 October 2016

Papers citing "Inference Compilation and Universal Probabilistic Programming"

29 / 79 papers shown

Title
Learning to Infer Program Sketches Maxwell Nye Luke B. Hewitt J. Tenenbaum Armando Solar-Lezama NAI 13 113 0 17 Feb 2019
Meta-Amortized Variational Inference and Learning Mike Wu Kristy Choi Noah D. Goodman Stefano Ermon OOD VLM BDL DRL 38 35 0 05 Feb 2019
ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language W. Neiswanger Kirthevasan Kandasamy Barnabás Póczós J. Schneider Eric P. Xing 31 17 0 31 Jan 2019
Neural Clustering Processes Ari Pakman Yueqi Wang Catalin Mitelut JinHyung Lee Liam Paninski BDL 14 5 0 28 Dec 2018
Amortized Bayesian inference for clustering models Ari Pakman Liam Paninski 12 6 0 24 Nov 2018
Inference Over Programs That Make Predictions Yura N. Perov 13 2 0 02 Oct 2018
Deep sequential models for sampling-based planning Yen-Ling Kuo Andrei Barbu Boris Katz BDL 13 26 0 01 Oct 2018
An Introduction to Probabilistic Programming Jan-Willem van de Meent Brooks Paige Hongseok Yang Frank D. Wood GP 15 196 0 27 Sep 2018
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model A. G. Baydin Lukas Heinrich W. Bhimji Lei Shao Saeid Naderiparizi ... Philip Torr Victor W. Lee P. Prabhat Kyle Cranmer Frank D. Wood 26 31 0 20 Jul 2018
Genetic algorithms with DNN-based trainable crossover as an example of partial specialization of general search A. Potapov S. Rodionov 13 5 0 18 Jul 2018
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms Yi Wu Siddharth Srivastava N. Hay S. Du Stuart J. Russell 31 25 0 06 Jun 2018
Mining gold from implicit models to improve likelihood-free inference Johann Brehmer Gilles Louppe J. Pavez Kyle Cranmer AI4CE TPM 38 180 0 30 May 2018
Forward Amortized Inference for Likelihood-Free Variational Marginalization L. Ambrogioni Umut Güçlü Julia Berezutskaya Eva W. P. van den Borne Yağmur Güçlütürk Max Hinne E. Maris Marcel van Gerven BDL VLM 14 17 0 29 May 2018
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow T. Le Adam R. Kosiorek N. Siddharth Yee Whye Teh Frank D. Wood BDL 9 23 0 26 May 2018
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows George Papamakarios D. Sterratt Iain Murray BDL 60 358 0 18 May 2018
High Throughput Synchronous Distributed Stochastic Gradient Descent Michael Teng Frank D. Wood 15 2 0 12 Mar 2018
Tighter Variational Bounds are Not Necessarily Better Tom Rainforth Adam R. Kosiorek T. Le Chris J. Maddison Maximilian Igl Frank D. Wood Yee Whye Teh DRL 13 196 0 13 Feb 2018
Using probabilistic programs as proposals Marco F. Cusumano-Towner Vikash K. Mansinghka BDL 16 11 0 11 Jan 2018
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators Mario Lezcano Casado A. G. Baydin David Martínez-Rubio T. Le Frank D. Wood ... Gilles Louppe Kyle Cranmer Karen Ng W. Bhimji P. Prabhat 19 9 0 21 Dec 2017
Faithful Inversion of Generative Models for Effective Amortized Inference Stefan Webb Adam Goliñski R. Zinkov Siddharth Narayanaswamy Tom Rainforth Yee Whye Teh Frank D. Wood TPM 26 46 0 01 Dec 2017
Meta-Learning MCMC Proposals Tongzhou Wang Yi Wu David A. Moore Stuart J. Russell BDL 21 2 0 21 Aug 2017
Learning Disentangled Representations with Semi-Supervised Deep Generative Models Siddharth Narayanaswamy Brooks Paige Jan-Willem van de Meent Alban Desmaison Noah D. Goodman Pushmeet Kohli Frank D. Wood Philip Torr DRL CoGe 14 359 0 01 Jun 2017
Auto-Encoding Sequential Monte Carlo T. Le Maximilian Igl Tom Rainforth Tom Jin Frank D. Wood BDL DRL 16 151 0 29 May 2017
Masked Autoregressive Flow for Density Estimation George Papamakarios Theo Pavlakou Iain Murray 38 1,327 0 19 May 2017
Learning Probabilistic Programs Using Backpropagation Avi Pfeffer BDL 19 0 0 15 May 2017
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning T. Le A. G. Baydin R. Zinkov Frank D. Wood SyDa OOD 25 89 0 02 Mar 2017
Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks Daniel E. Ritchie Anna T. Thomas Pat Hanrahan Noah D. Goodman TPM 28 11 0 19 Mar 2016
Automatic differentiation in machine learning: a survey A. G. Baydin Barak A. Pearlmutter Alexey Radul J. Siskind PINN AI4CE ODL 54 2,746 0 20 Feb 2015
MatConvNet - Convolutional Neural Networks for MATLAB Andrea Vedaldi Karel Lenc 183 2,946 0 15 Dec 2014