TensorFlow Distributions

28 November 2017

Matt Hoffman

Rif A. Saurous

ArXiv PDF HTML

Papers citing "TensorFlow Distributions"

50 / 60 papers shown

Title
JaxSGMC: Modular stochastic gradient MCMC in JAX Stephan Thaler Paul Fuchs Ana Cukarska Julija Zavadlav BDL 30 2 0 16 May 2025
Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networks Jon Vadillo Roberto Santana J. A. Lozano Marta Z. Kwiatkowska BDL AAML 67 0 0 17 Feb 2025
Expert-elicitation method for non-parametric joint priors using normalizing flows F. Bockting Stefan T. Radev Paul-Christian Bürkner BDL 95 1 0 24 Nov 2024
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models Jinlin Lai Justin Domke Daniel Sheldon 38 0 0 31 Oct 2024
Simulation-based inference with the Python Package sbijax Simon Dirmeier S. Ulzega Antonietta Mira Carlo Albert 38 1 0 28 Sep 2024
Function-Space MCMC for Bayesian Wide Neural Networks Lucia Pezzetti Stefano Favaro Stefano Peluchetti BDL 133 0 0 26 Aug 2024
posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms Måns Magnusson Jakob Torgander Paul-Christian Bürkner Lu Zhang Bob Carpenter Aki Vehtari 42 6 0 06 Jul 2024
A Direct Importance Sampling-based Framework for Rare Event Uncertainty Quantification in Non-Gaussian Spaces Elsayed M. Eshra Konstantinos G. Papakonstantinou Hamed Nikbakht 23 0 0 23 May 2024
Uncertainty Quantification in Machine Learning for Joint Speaker Diarization and Identification Simon W. McKnight Aidan O. T. Hogg Vincent W. Neo Patrick A. Naylor 11 1 0 28 Dec 2023
Unveiling the frontiers of deep learning: innovations shaping diverse domains Shams Forruque Ahmed Md. Sakib Bin Alam Maliha Kabir Shaila Afrin Sabiha Jannat Rafa Aanushka Mehjabin Amir H. Gandomi AI4CE 42 2 0 06 Sep 2023
SynJax: Structured Probability Distributions for JAX Miloš Stanojević Laurent Sartran SyDa 13 4 0 07 Aug 2023
Data-Driven Probabilistic Energy Consumption Estimation for Battery Electric Vehicles with Model Uncertainty Ayan Maity Sudeshna Sarkar 20 8 0 02 Jul 2023
Overcoming the Limitations of Localization Uncertainty: Efficient & Exact Non-Linear Post-Processing and Calibration Moussa Kassem Sbeyti Michelle Karg Christian Wirth Azarm Nowzad S. Albayrak 22 3 0 15 Jun 2023
Gibbs Sampling the Posterior of Neural Networks Giovanni Piccioli Emanuele Troiani Lenka Zdeborová 41 2 0 05 Jun 2023
Testing for the Markov Property in Time Series via Deep Conditional Generative Learning Yunzhe Zhou C. Shi Lexin Li Q. Yao AI4TS 35 8 0 30 May 2023
Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions Hongjing Xia Joshua C. Chang S. Nowak Sonya Mahajan R. Mahajan Ted L. Chang Carson C. Chow 35 1 0 19 Apr 2023
Uncertainty-Aware Vehicle Energy Efficiency Prediction using an Ensemble of Neural Networks Jihed Khiari Cristina Olaverri-Monreal 19 1 0 14 Apr 2023
L-HYDRA: Multi-Head Physics-Informed Neural Networks Zongren Zou George Karniadakis AI4CE 18 26 0 05 Jan 2023
Workload Forecasting of a Logistic Node Using Bayesian Neural Networks Emin Cagatay Nakilcioglu Anisa Rizvanolli 21 1 0 09 Nov 2022
Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detection Jack Y. Araz M. Spannowsky 22 13 0 07 Nov 2022
MARS: Meta-Learning as Score Matching in the Function Space Krunoslav Lehman Pavasovic Jonas Rothfuss Andreas Krause BDL 30 4 0 24 Oct 2022
Learning Robust Dynamics through Variational Sparse Gating A. Jain Shivakanth Sujit S. Joshi Vincent Michalski Danijar Hafner Samira Ebrahimi Kahou 27 8 0 21 Oct 2022
Quantifying Uncertainty with Probabilistic Machine Learning Modeling in Wireless Sensing Amit Kachroo Sai Prashanth Chinnapalli 18 0 0 12 Oct 2022
Liesel: A Probabilistic Programming Framework for Developing Semi-Parametric Regression Models and Custom Bayesian Inference Algorithms Hannes Riebl P. Wiemann Thomas Kneib 8 2 0 22 Sep 2022
Borch: A Deep Universal Probabilistic Programming Language Lewis Belcher Johan Gudmundsson Michael Green BDL AI4CE UQCV 28 0 0 13 Sep 2022
Parallel MCMC Algorithms: Theoretical Foundations, Algorithm Design, Case Studies N. Glatt-Holtz Andrew J Holbrook J. Krometis Cecilia F. Mondaini 18 10 0 10 Sep 2022
Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death Joshua C. Chang Ted L. Chang Carson C. Chow R. Mahajan Sonya Mahajan Joe Maisog Shashaank Vattikuti Hongjing Xia FAtt OOD 37 0 0 28 Aug 2022
PAVI: Plate-Amortized Variational Inference Louis Rouillard Thomas Moreau Demian Wassermann 25 1 0 10 Jun 2022
Sparse Graph Learning from Spatiotemporal Time Series Andrea Cini Daniele Zambon Cesare Alippi CML AI4TS 40 18 0 26 May 2022
Uncertainty-Aware Prediction of Battery Energy Consumption for Hybrid Electric Vehicles Jihed Khiari Cristina Olaverri-Monreal 27 2 0 27 Apr 2022
Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport Liyi Zhang David M. Blei C. A. Naesseth 27 6 0 03 Feb 2022
Efficient Automatic Differentiation of Implicit Functions C. Margossian M. Betancourt 22 2 0 28 Dec 2021
Prediction of Energy Consumption for Variable Customer Portfolios Including Aleatoric Uncertainty Estimation Oliver Mey André Schneider Olaf Enge-Rosenblatt Yesnier Bravo P. Stenzel 17 6 0 01 Oct 2021
Supervising the Decoder of Variational Autoencoders to Improve Scientific Utility Liyun Tu Austin Talbot Neil Gallagher David Carlson DRL 32 2 0 09 Sep 2021
Pathfinder: Parallel quasi-Newton variational inference Lu Zhang Bob Carpenter A. Gelman Aki Vehtari 43 40 0 09 Aug 2021
Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors Dhruv V. Patel Deep Ray Assad A. Oberai AI4CE 13 37 0 06 Jul 2021
ADAVI: Automatic Dual Amortized Variational Inference Applied To Pyramidal Bayesian Models Louis Rouillard Demian Wassermann 36 2 0 23 Jun 2021
Principal Component Density Estimation for Scenario Generation Using Normalizing Flows Eike Cramer Alexander Mitsos Raúl Tempone Manuel Dahmen 29 13 0 21 Apr 2021
GPflux: A Library for Deep Gaussian Processes Vincent Dutordoir Hugh Salimbeni Eric Hambro John Mcleod Felix Leibfried A. Artemev Mark van der Wilk J. Hensman M. Deisenroth S. T. John GP 33 23 0 12 Apr 2021
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport Adrien Corenflos James Thornton George Deligiannidis Arnaud Doucet OT 41 66 0 15 Feb 2021
Convolutional LSTM Neural Networks for Modeling Wildland Fire Dynamics J. Burge M. Bonanni M. Ihme Lily Hu 19 19 0 11 Dec 2020
High-Throughput Image-Based Plant Stand Count Estimation Using Convolutional Neural Networks S. Khaki Hieu H. Pham Ye Han W. Kent Lizhi Wang 34 17 0 23 Oct 2020
Review: Deep Learning in Electron Microscopy Jeffrey M. Ede 31 79 0 17 Sep 2020
Toward Reliable Models for Authenticating Multimedia Content: Detecting Resampling Artifacts With Bayesian Neural Networks Anatol Maier Benedikt Lorch Christian Riess AAML 38 17 0 28 Jul 2020
A Generalization of Otsu's Method and Minimum Error Thresholding Jonathan T. Barron 25 26 0 14 Jul 2020
Bayesian Neural Networks: An Introduction and Survey Ethan Goan Clinton Fookes BDL UQCV 35 199 0 22 Jun 2020
Bayesian Neural Networks at Scale: A Performance Analysis and Pruning Study Himanshu Sharma Elise Jennings BDL 27 3 0 23 May 2020
Learning to Fly via Deep Model-Based Reinforcement Learning Philip Becker-Ehmck Maximilian Karl Jan Peters Patrick van der Smagt SSL 35 37 0 19 Mar 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 17 30 0 04 Feb 2020
Lazy object copy as a platform for population-based probabilistic programming Lawrence M. Murray 16 5 0 09 Jan 2020