Pyro: Deep Universal Probabilistic Programming

18 October 2018

Papers citing "Pyro: Deep Universal Probabilistic Programming"

50 / 436 papers shown

Title
Locally Differentially Private Bayesian Inference Tejas D. Kulkarni Joonas Jälkö Samuel Kaski Antti Honkela 48 2 0 27 Oct 2021
Physics-guided Deep Markov Models for Learning Nonlinear Dynamical Systems with Uncertainty Wei Liu Zhilu Lai Kiran Bacsa Eleni Chatzi PINN BDL AI4CE 20 33 0 16 Oct 2021
GaussED: A Probabilistic Programming Language for Sequential Experimental Design Matthew A. Fisher Onur Teymur Chris J. Oates 40 1 0 15 Oct 2021
Recommending POIs for Tourists by User Behavior Modeling and Pseudo-Rating Kun Yi Ryujiro Yamagishi Taishan Li Zhengyang Bai Qiang Ma 21 2 0 13 Oct 2021
Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling Gianluigi Silvestri Emily Fertig David A. Moore L. Ambrogioni BDL TPM AI4CE 31 3 0 12 Oct 2021
SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming Arseny Skryagin Wolfgang Stammer Daniel Ochs Devendra Singh Dhami Kristian Kersting NAI 33 6 0 07 Oct 2021
Unifying AI Algorithms with Probabilistic Programming using Implicitly Defined Representations Avi Pfeffer M. Harradon Joseph Campolongo S. Cvijic 24 2 0 05 Oct 2021
Kalman Bayesian Neural Networks for Closed-form Online Learning Philipp Wagner Xinyang Wu Marco F. Huber BDL 22 12 0 03 Oct 2021
TyXe: Pyro-based Bayesian neural nets for Pytorch H. Ritter Theofanis Karaletsos OOD MU BDL 28 6 0 01 Oct 2021
Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration Gavin Kerrigan Padhraic Smyth M. Steyvers 30 46 0 29 Sep 2021
Bayesian Confidence Calibration for Epistemic Uncertainty Modelling Fabian Küppers Jan Kronenberger Jonas Schneider Anselm Haselhoff UQCV BDL 19 8 0 21 Sep 2021
Combining data assimilation and machine learning to estimate parameters of a convective-scale model Stefanie Legler T. Janjić 28 18 0 07 Sep 2021
Learning Disentangled Representations in the Imaging Domain Xiao Liu Pedro Sanchez Spyridon Thermos Alison Q. OÑeil Sotirios A. Tsaftaris OOD DRL 32 71 0 26 Aug 2021
Modeling Item Response Theory with Stochastic Variational Inference Mike Wu R. Davis B. Domingue Chris Piech Noah D. Goodman CML 25 5 0 26 Aug 2021
Pathfinder: Parallel quasi-Newton variational inference Lu Zhang Bob Carpenter A. Gelman Aki Vehtari 43 40 0 09 Aug 2021
Pixyz: a Python library for developing deep generative models Masahiro Suzuki T. Kaneko Y. Matsuo AI4CE 28 2 0 28 Jul 2021
JAGS, NIMBLE, Stan: a detailed comparison among Bayesian MCMC software Mario Beraha Daniel Falco A. Guglielmi 13 8 0 20 Jul 2021
A Survey of Uncertainty in Deep Neural Networks J. Gawlikowski Cedrique Rovile Njieutcheu Tassi Mohsin Ali Jongseo Lee Matthias Humt ... R. Roscher Muhammad Shahzad Wen Yang R. Bamler Xiaoxiang Zhu BDL UQCV OOD 59 1,111 0 07 Jul 2021
ADAVI: Automatic Dual Amortized Variational Inference Applied To Pyramidal Bayesian Models Louis Rouillard Demian Wassermann 36 2 0 23 Jun 2021
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization David Eriksson P. Chuang Sam Daulton Peng Xia Akshat Shrivastava Arun Babu Shicong Zhao Ahmed Aly Ganesh Venkatesh Maximilian Balandat BDL 10 16 0 22 Jun 2021
Local convexity of the TAP free energy and AMP convergence for Z2-synchronization Michael Celentano Z. Fan Song Mei FedML 21 22 0 21 Jun 2021
Schr{ö}dinger-F{ö}llmer Sampler: Sampling without Ergodicity Jian Huang Yuling Jiao Lican Kang Xu Liao Jin Liu Yanyan Liu 35 27 0 21 Jun 2021
Nonparametric Hamiltonian Monte Carlo Carol Mak Fabian Zaiser C.-H. Luke Ong 31 6 0 18 Jun 2021
Harmonization with Flow-based Causal Inference Rongguang Wang Pratik Chaudhari Christos Davatzikos OOD CML 21 28 0 12 Jun 2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective Geoff Pleiss John P. Cunningham 28 24 0 11 Jun 2021
Pulling back information geometry Georgios Arvanitidis Miguel González Duque Alison Pouplin Dimitris Kalatzis Søren Hauberg DRL 9 14 0 09 Jun 2021
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently Tim Reichelt Adam Goliñski C.-H. Luke Ong Tom Rainforth TPM 26 0 0 09 Jun 2021
Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitions Michael Poli Stefano Massaroli Luca Scimeca Seong Joon Oh Sanghyuk Chun Atsushi Yamashita Hajime Asama Jinkyoo Park Animesh Garg AI4TS AI4CE 276 6 0 08 Jun 2021
Comparing Test Sets with Item Response Theory Clara Vania Phu Mon Htut William Huang Dhara Mungra Richard Yuanzhe Pang Jason Phang Haokun Liu Kyunghyun Cho Sam Bowman 19 39 0 01 Jun 2021
Sparse Uncertainty Representation in Deep Learning with Inducing Weights H. Ritter Martin Kukla Chen Zhang Yingzhen Li UQCV BDL 55 17 0 30 May 2021
Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC Jeremie Coullon Leah F. South Christopher Nemeth 27 12 0 27 May 2021
Scalable Cross Validation Losses for Gaussian Process Models M. Jankowiak Geoff Pleiss 18 6 0 24 May 2021
SPUX Framework: a Scalable Package for Bayesian Uncertainty Quantification and Propagation Jonas vSukys Marco Bacci 15 2 0 12 May 2021
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling Simen Eide David S. Leslie A. Frigessi BDL OffRL 30 9 0 30 Apr 2021
Breiman's two cultures: You don't have to choose sides Andrew C. Miller N. Foti E. Fox 33 11 0 25 Apr 2021
Shadow-Mapping for Unsupervised Neural Causal Discovery M. Vowels Necati Cihan Camgöz Richard Bowden CML 16 6 0 16 Apr 2021
Hypothesis Formalization: Empirical Findings, Software Limitations, and Design Implications Eunice Jun M. Birchfield Nicole de Moura Jeffrey Heer René Just 15 15 0 06 Apr 2021
How Powerful are Performance Predictors in Neural Architecture Search? Colin White Arber Zela Binxin Ru Yang Liu Frank Hutter 19 126 0 02 Apr 2021
Storchastic: A Framework for General Stochastic Automatic Differentiation Emile van Krieken Jakub M. Tomczak A. T. Teije ODL OffRL 31 15 0 01 Apr 2021
D3p -- A Python Package for Differentially-Private Probabilistic Programming Lukas Prediger Niki Loppi Samuel Kaski Antti Honkela 25 6 0 22 Mar 2021
NEO: Non Equilibrium Sampling on the Orbit of a Deterministic Transform Achille Thin Yazid Janati Sylvain Le Corff Charles Ollion Arnaud Doucet Alain Durmus Eric Moulines C. Robert 30 7 0 17 Mar 2021
A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots T. Taniguchi Hiroshi Yamakawa Takayuki Nagai Kenji Doya M. Sakagami Masahiro Suzuki Tomoaki Nakamura Akira Taniguchi 28 23 0 15 Mar 2021
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems Martin Mladenov Chih-Wei Hsu Vihan Jain Eugene Ie Christopher Colby Nicolas Mayoraz H. Pham Dustin Tran Ivan Vendrov Craig Boutilier BDL 15 31 0 14 Mar 2021
VDSM: Unsupervised Video Disentanglement with State-Space Modeling and Deep Mixtures of Experts M. Vowels Necati Cihan Camgöz Richard Bowden CoGe 30 8 0 12 Mar 2021
Combining Interventional and Observational Data Using Causal Reductions Maximilian Ilse Patrick Forré Max Welling Joris M. Mooij OOD CML 25 0 0 08 Mar 2021
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data Mohamed Aziz Bhouri P. Perdikaris 28 20 0 04 Mar 2021
A Structural Causal Model for MR Images of Multiple Sclerosis Jacob C. Reinhold A. Carass Jerry L. Prince CML MedIm 8 27 0 04 Mar 2021
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design Adam Foster Desi R. Ivanova Ilyas Malik Tom Rainforth 28 78 0 03 Mar 2021
Meta-Learning an Inference Algorithm for Probabilistic Programs Gwonsoo Che Hongseok Yang TPM 16 1 0 01 Mar 2021
Learning Proposals for Probabilistic Programs with Inference Combinators Sam Stites Heiko Zimmermann Hao Wu Eli Sennesh Jan-Willem van de Meent NAI 15 14 0 01 Mar 2021