Epistemic Neural Networks

19 July 2021

Papers citing "Epistemic Neural Networks"

25 / 25 papers shown

Title
Uncertainty modeling for fine-tuned implicit functions A. Susmelj Mael Macuglia Nataša Tagasovska Reto Sutter Sebastiano Caprara Jean-Philippe Thiran E. Konukoglu 70 1 0 17 Jun 2024
Scalable Bayesian Learning with posteriors Samuel Duffield Kaelan Donatella Johnathan Chiu Phoebe Klett Daniel Simpson BDL UQCV 62 1 0 31 May 2024
On the Challenges and Opportunities in Generative AI Laura Manduchi Kushagra Pandey Robert Bamler Ryan Cotterell Sina Daubener ... F. Wenzel Frank Wood Stephan Mandt Vincent Fortuin Vincent Fortuin 56 17 0 28 Feb 2024
Distinguishing the Knowable from the Unknowable with Language Models Gustaf Ahdritz Tian Qin Nikhil Vyas Boaz Barak Benjamin L. Edelman 26 18 0 05 Feb 2024
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent Yingru Li Jiawei Xu Lei Han Zhi-Quan Luo BDL OffRL 26 6 0 05 Feb 2024
Reducing LLM Hallucinations using Epistemic Neural Networks Shreyas Verma Kien Tran Yusuf Ali Guangyu Min 38 7 0 25 Dec 2023
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding Alizée Pace Hugo Yèche Bernhard Schölkopf Gunnar Rätsch Guy Tennenholtz OffRL 16 6 0 01 Jun 2023
Semantic Anomaly Detection with Large Language Models Amine Elhafsi Rohan Sinha Christopher Agia Edward Schmerling I. Nesnas Marco Pavone 34 64 0 18 May 2023
Variational Voxel Pseudo Image Tracking Illia Oleksiienko Paraskevi Nousi Nikolaos Passalis Anastasios Tefas Alexandros Iosifidis 19 1 0 12 Feb 2023
Selective Uncertainty Propagation in Offline RL Sanath Kumar Krishnamurthy Shrey Modi Tanmay Gangwani S. Katariya B. Kveton A. Rangi OffRL 59 0 0 01 Feb 2023
Do Bayesian Neural Networks Need To Be Fully Stochastic? Mrinank Sharma Sebastian Farquhar Eric T. Nalisnick Tom Rainforth BDL 18 52 0 11 Nov 2022
Reinforcement Learning in Non-Markovian Environments Siddharth Chandak Pratik Shah Vivek Borkar Parth Dodhia OOD 20 7 0 03 Nov 2022
Truncation Sampling as Language Model Desmoothing John Hewitt Christopher D. Manning Percy Liang BDL 41 75 0 27 Oct 2022
Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping Vikranth Dwaracherla Zheng Wen Ian Osband Xiuyuan Lu S. Asghari Benjamin Van Roy UQCV 24 17 0 08 Jun 2022
Incorporating Explicit Uncertainty Estimates into Deep Offline Reinforcement Learning David Brandfonbrener Rémi Tachet des Combes Romain Laroche OffRL 29 5 0 02 Jun 2022
Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling Andreas Kirsch Jannik Kossen Y. Gal UQCV BDL 50 3 0 18 May 2022
Evaluating High-Order Predictive Distributions in Deep Learning Ian Osband Zheng Wen S. Asghari Vikranth Dwaracherla Xiuyuan Lu Benjamin Van Roy 10 9 0 28 Feb 2022
Deep Ensembles Work, But Are They Necessary? Taiga Abe E. Kelly Buchanan Geoff Pleiss R. Zemel John P. Cunningham OOD UQCV 38 59 0 14 Feb 2022
Learning to Be Cautious Montaser Mohammedalamen Dustin Morrill Alexander Sieusahai Yash Satsangi Michael H. Bowling 18 3 0 29 Oct 2021
The Neural Testbed: Evaluating Joint Predictions Ian Osband Zheng Wen S. Asghari Vikranth Dwaracherla Botao Hao M. Ibrahimi Dieterich Lawson Xiuyuan Lu Brendan O'Donoghue Benjamin Van Roy UQCV 26 21 0 09 Oct 2021
Deep Exploration for Recommendation Systems Zheqing Zhu Benjamin Van Roy 32 11 0 26 Sep 2021
Correlated Input-Dependent Label Noise in Large-Scale Image Classification Mark Collier Basil Mustafa Efi Kokiopoulou Rodolphe Jenatton Jesse Berent NoLa 178 53 0 19 May 2021
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 231 4,469 0 23 Jan 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,660 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015