Pyro: Deep Universal Probabilistic Programming

18 October 2018

Papers citing "Pyro: Deep Universal Probabilistic Programming"

50 / 436 papers shown

Title
Efficient Computation Reduction in Bayesian Neural Networks Through Feature Decomposition and Memorization Xiaotao Jia Jianlei Yang Runze Liu Xueyan Wang S. Cotofana Weisheng Zhao 13 26 0 08 May 2020
Orbit: Probabilistic Forecast with Exponential Smoothing Edwin Ng Zhishi Wang Huigang Chen Steve Yang Slawek Smyl AI4TS 8 10 0 18 Apr 2020
Scaling Bayesian inference of mixed multinomial logit models to very large datasets Filipe Rodrigues BDL 18 3 0 11 Apr 2020
Capsule Networks -- A Probabilistic Perspective Lewis Smith Lisa Schut Y. Gal Mark van der Wilk OCL 11 5 0 07 Apr 2020
Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited Wesley J. Maddox Gregory W. Benton A. Wilson 20 61 0 04 Mar 2020
Stochastically Differentiable Probabilistic Programs David Tolpin Yuanshuo Zhou Hongseok Yang BDL 11 0 0 02 Mar 2020
MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference Achille Thin Nikita Kotelevskii Jean-Stanislas Denain Léo Grinsztajn Alain Durmus Maxim Panov Eric Moulines BDL 6 17 0 27 Feb 2020
Lipschitz standardization for multivariate learning Adrián Javaloy Isabel Valera 8 0 0 26 Feb 2020
Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example Serena Booth Yilun Zhou Ankit J. Shah J. Shah BDL 20 2 0 19 Feb 2020
Gravitational-wave parameter estimation with autoregressive neural network flows Stephen R. Green C. Simpson J. Gair BDL 83 87 0 18 Feb 2020
The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence G. Marcus VLM 32 353 0 14 Feb 2020
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence S. Raschka Joshua Patterson Corey J. Nolet AI4CE 24 483 0 12 Feb 2020
Black-Box Optimization with Local Generative Surrogates S. Shirobokov V. Belavin Michael Kagan Andrey Ustyuzhanin A. G. Baydin 8 3 0 11 Feb 2020
DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models Mohamed Tarek Kai Xu Martin Trapp Hong Ge Zoubin Ghahramani 18 7 0 07 Feb 2020
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware Junpeng Lao Christopher Suter I. Langmore C. Chimisov A. Saxena Pavel Sountsov Dave Moore Rif A. Saurous Matthew D. Hoffman Joshua V. Dillon 25 30 0 04 Feb 2020
Torch-Struct: Deep Structured Prediction Library Alexander M. Rush 21 63 0 03 Feb 2020
Automatic structured variational inference L. Ambrogioni Kate Lin Emily Fertig Sharad Vikram Max Hinne Dave Moore Marcel van Gerven BDL 23 29 0 03 Feb 2020
Variational Item Response Theory: Fast, Accurate, and Expressive Mike Wu R. Davis B. Domingue Chris Piech Noah D. Goodman OffRL 36 52 0 01 Feb 2020
Joint Distributions for TensorFlow Probability Dan Piponi Dave Moore Joshua V. Dillon GP 27 16 0 22 Jan 2020
Newtonian Monte Carlo: single-site MCMC meets second-order gradient methods Nimar S. Arora N. Tehrani K. Shah Michael Tingley Y. Li Narjes Torabi David Noursi Sepehr Akhavan Masouleh Eric Lippert E. Meijer BDL 8 3 0 15 Jan 2020
FunMC: A functional API for building Markov Chains Pavel Sountsov Alexey Radul Srinivas Vasudevan 29 1 0 14 Jan 2020
Sampling Prediction-Matching Examples in Neural Networks: A Probabilistic Programming Approach Serena Booth Ankit J. Shah Yilun Zhou J. Shah BDL 17 1 0 09 Jan 2020
Probabilistic Reasoning across the Causal Hierarchy D. Ibeling Thomas Icard LRM AI4CE 14 29 0 09 Jan 2020
Lazy object copy as a platform for population-based probabilistic programming Lawrence M. Murray 21 5 0 09 Jan 2020
Bayesian task embedding for few-shot Bayesian optimization Steven Atkinson Sayan Ghosh Natarajan Chennimalai-Kumar Genghis Khan Liping Wang BDL 16 1 0 02 Jan 2020
Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro Du Phan Neeraj Pradhan M. Jankowiak 25 349 0 24 Dec 2019
Blang: Bayesian declarative modelling of general data structures and inference via algorithms based on distribution continua Alexandre Bouchard-Coté Kevin Chern Davor Cubranic Sahand Hosseini Justin Hume Matteo Lepur Zihui Ouyang G. Sgarbi 16 6 0 22 Dec 2019
srlearn: A Python Library for Gradient-Boosted Statistical Relational Models Alexander L. Hayes GP 11 1 0 17 Dec 2019
Probabilistic Software Modeling: A Data-driven Paradigm for Software Analysis Hannes Thaller L. Linsbauer Rudolf Ramler Alexander Egyed 28 3 0 17 Dec 2019
Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems Robert Osazuwa Ness Kaushal Paneri O. Vitek 17 7 0 06 Nov 2019
Amortized Population Gibbs Samplers with Neural Sufficient Statistics Hao Wu Heiko Zimmermann Eli Sennesh T. Le Jan-Willem van de Meent 22 7 0 04 Nov 2019
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments Adam Foster M. Jankowiak M. O'Meara Yee Whye Teh Tom Rainforth BDL 14 58 0 01 Nov 2019
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support Yuanshuo Zhou Hongseok Yang Yee Whye Teh Tom Rainforth TPM 29 19 0 29 Oct 2019
Attention for Inference Compilation William Harvey Andreas Munk A. G. Baydin Alexander Bergholm Frank Wood 22 9 0 25 Oct 2019
Probabilistic Surrogate Networks for Simulators with Unbounded Randomness Andreas Munk Berend Zwartsenberg Adam Scibior A. G. Baydin Andrew Stewart G. Fernlund A. Poursartip Frank Wood TPM 22 4 0 25 Oct 2019
Functional Tensors for Probabilistic Programming F. Obermeyer Eli Bingham M. Jankowiak Du Phan Jonathan P. Chen 19 17 0 23 Oct 2019
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming Yura N. Perov L. Graham Kostis Gourgoulias Jonathan G. Richens Ciarán M. Gilligan-Lee Adam Baker Saurabh Johri LRM 20 17 0 17 Oct 2019
Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs R. Walecki Kostis Gourgoulias Adam Baker Chris Hart Chris Lucas Max Zwiessele A. Buchard Maria Lomeli Yura N. Perov Saurabh Johri UQCV 16 0 0 16 Oct 2019
Parametric Gaussian Process Regressors M. Jankowiak Geoffrey Pleiss Jacob R. Gardner UQCV 25 5 0 16 Oct 2019
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization Maximilian Balandat Brian Karrer Daniel R. Jiang Sam Daulton Benjamin Letham A. Wilson E. Bakshy 32 93 0 14 Oct 2019
Intensity-Free Learning of Temporal Point Processes Oleksandr Shchur Marin Bilos Stephan Günnemann AI4TS 27 167 0 26 Sep 2019
Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables Guangyao Zhou 22 19 0 11 Sep 2019
Static Analysis for Probabilistic Programs Ryan Bernstein TPM 21 20 0 10 Sep 2019
Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds John P. Lalor Hao Wu Hong-ye Yu 11 42 0 29 Aug 2019
InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy Javier Cózar Rafael Cabañas Antonio Salmerón A. Masegosa BDL 19 3 0 29 Aug 2019
Variationally Inferred Sampling Through a Refined Bound for Probabilistic Programs Víctor Gallego D. Insua BDL 12 1 0 26 Aug 2019
Probabilistic Models with Deep Neural Networks A. Masegosa Rafael Cabañas H. Langseth Thomas D. Nielsen Antonio Salmerón BDL 6 12 0 09 Aug 2019
Towards Verified Stochastic Variational Inference for Probabilistic Programs Wonyeol Lee Hangyeol Yu Xavier Rival Hongseok Yang 21 23 0 20 Jul 2019
Distributions.jl: Definition and Modeling of Probability Distributions in the JuliaStats Ecosystem Mathieu Besançon Theodore Papamarkou D. Anthoff Alex Arslan Simon Byrne Dahua Lin John Pearson GP 19 78 0 19 Jul 2019
Subspace Inference for Bayesian Deep Learning Pavel Izmailov Wesley J. Maddox Polina Kirichenko T. Garipov Dmitry Vetrov A. Wilson UQCV BDL 38 142 0 17 Jul 2019