Prediction intervals for Deep Neural Networks

8 October 2020

Tullio Mancini

Hector F. Calvo-Pardo

Papers citing "Prediction intervals for Deep Neural Networks"

20 / 20 papers shown

Title
Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods Eyke Hüllermeier Willem Waegeman PER UD 196 1,405 0 21 Oct 2019
Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout I. Cortés-Ciriano A. Bender OOD 103 47 0 12 Apr 2019
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks Difan Zou Yuan Cao Dongruo Zhou Quanquan Gu ODL 154 448 0 21 Nov 2018
A Mean Field View of the Landscape of Two-Layers Neural Networks Song Mei Andrea Montanari Phan-Minh Nguyen MLT 81 857 0 18 Apr 2018
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach Tim Pearce Mohamed H. Zaki Alexandra Brintrup A. Neely UQCV 149 278 0 20 Feb 2018
The Expressive Power of Neural Networks: A View from the Width Zhou Lu Hongming Pu Feicheng Wang Zhiqiang Hu Liwei Wang 91 892 0 08 Sep 2017
Deep and Confident Prediction for Time Series at Uber Lingxue Zhu N. Laptev BDL AI4TS 125 345 0 06 Sep 2017
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? Alex Kendall Y. Gal BDL OOD UD UQCV PER 326 4,699 0 15 Mar 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 622 5,798 0 05 Dec 2016
On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products Kush R. Varshney H. Alemzadeh 79 224 0 05 Oct 2016
Practical Black-Box Attacks against Machine Learning Nicolas Papernot Patrick McDaniel Ian Goodfellow S. Jha Z. Berkay Celik A. Swami MLAU AAML 62 3,672 0 08 Feb 2016
Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks Stefan Lee Senthil Purushwalkam Michael Cogswell David J. Crandall Dhruv Batra FedML UQCV 76 315 0 19 Nov 2015
Variational Dropout and the Local Reparameterization Trick Diederik P. Kingma Tim Salimans Max Welling BDL 196 1,511 0 08 Jun 2015
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference Y. Gal Zoubin Ghahramani UQCV BDL 252 749 0 06 Jun 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 610 9,290 0 06 Jun 2015
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks José Miguel Hernández-Lobato Ryan P. Adams UQCV BDL 98 944 0 18 Feb 2015
A Bayesian encourages dropout S. Maeda BDL 64 45 0 22 Dec 2014
Deep Learning in Neural Networks: An Overview Jürgen Schmidhuber HAI 216 16,336 0 30 Apr 2014
An empirical analysis of dropout in piecewise linear networks David Warde-Farley Ian Goodfellow Aaron Courville Yoshua Bengio 94 107 0 21 Dec 2013
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 414 7,658 0 03 Jul 2012