A Practical & Unified Notation for Information-Theoretic Quantities in ML

22 June 2021

Papers citing "A Practical & Unified Notation for Information-Theoretic Quantities in ML"

11 / 11 papers shown

Title
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning Andreas Kirsch Clare Lyle Y. Gal 62 16 0 27 Mar 2020
A Theory of Usable Information Under Computational Constraints Yilun Xu Shengjia Zhao Jiaming Song Russell Stewart Stefano Ermon 57 171 0 25 Feb 2020
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 212 10,591 0 17 Feb 2020
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning Andreas Kirsch Joost R. van Amersfoort Y. Gal FedML 51 624 0 19 Jun 2019
Variational Bayesian Optimal Experimental Design Adam Foster M. Jankowiak Eli Bingham Paul Horsfall Yee Whye Teh Tom Rainforth Noah D. Goodman 50 135 0 13 Mar 2019
Deep Bayesian Active Learning with Image Data Y. Gal Riashat Islam Zoubin Ghahramani BDL UQCV 56 1,725 0 08 Mar 2017
Opening the Black Box of Deep Neural Networks via Information Ravid Shwartz-Ziv Naftali Tishby AI4CE 79 1,406 0 02 Mar 2017
Unifying Count-Based Exploration and Intrinsic Motivation Marc G. Bellemare S. Srinivasan Georg Ostrovski Tom Schaul D. Saxton Rémi Munos 159 1,465 0 06 Jun 2016
Variational Dropout and the Local Reparameterization Trick Diederik P. Kingma Tim Salimans Max Welling BDL 159 1,500 0 08 Jun 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 476 9,233 0 06 Jun 2015
Bayesian Active Learning for Classification and Preference Learning N. Houlsby Ferenc Huszár Zoubin Ghahramani M. Lengyel 73 901 0 24 Dec 2011