Leveraging Uncertainty from Deep Learning for Trustworthy Materials Discovery Workflows

2 December 2020

Papers citing "Leveraging Uncertainty from Deep Learning for Trustworthy Materials Discovery Workflows"

22 / 22 papers shown

Title
Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning Ankur Mallick Chaitanya Dwivedi B. Kailkhura Gauri Joshi T. Y. Han UQCV BDL 59 6 0 18 Jul 2020
A Survey of Deep Learning for Scientific Discovery M. Raghu Erica Schmidt OOD AI4CE 67 123 0 26 Mar 2020
Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning Jize Zhang B. Kailkhura T. Y. Han UQCV 60 222 0 16 Mar 2020
Deep Ensembles: A Loss Landscape Perspective Stanislav Fort Huiyi Hu Balaji Lakshminarayanan OOD UQCV 65 624 0 05 Dec 2019
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 211 42,038 0 03 Dec 2019
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift Yaniv Ovadia Emily Fertig Jie Jessie Ren Zachary Nado D. Sculley Sebastian Nowozin Joshua V. Dillon Balaji Lakshminarayanan Jasper Snoek UQCV 135 1,677 0 06 Jun 2019
Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout I. Cortés-Ciriano A. Bender OOD 103 47 0 12 Apr 2019
Reliable and Explainable Machine Learning Methods for Accelerated Material Discovery B. Kailkhura Brian Gallagher Sookyung Kim A. Hiszpanski T. Y. Han 41 154 0 05 Jan 2019
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem Matthias Hein Maksym Andriushchenko Julian Bitterwolf OODD 133 556 0 13 Dec 2018
Deep Anomaly Detection with Outlier Exposure Dan Hendrycks Mantas Mazeika Thomas G. Dietterich OODD 103 1,467 0 11 Dec 2018
Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networks I. Cortés-Ciriano A. Bender OOD UQCV 47 61 0 24 Sep 2018
Accurate Uncertainties for Deep Learning Using Calibrated Regression Volodymyr Kuleshov Nathan Fenner Stefano Ermon BDL UQCV 131 626 0 01 Jul 2018
Building Data-driven Models with Microstructural Images: Generalization and Interpretability Julia Ling Maxwell Hutchinson Erin Antono Brian L. DeCost Elizabeth A. Holm B. Meredig AI4CE OOD 29 67 0 01 Nov 2017
On Calibration of Modern Neural Networks Chuan Guo Geoff Pleiss Yu Sun Kilian Q. Weinberger UQCV 181 5,774 0 14 Jun 2017
Selective Classification for Deep Neural Networks Yonatan Geifman Ran El-Yaniv CVBM 77 522 0 23 May 2017
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? Alex Kendall Y. Gal BDL OOD UD UQCV PER 250 4,667 0 15 Mar 2017
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks Christos Louizos Max Welling BDL 129 456 0 06 Mar 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 463 5,748 0 05 Dec 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 244 7,951 0 23 May 2016
How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? Junghwan Cho Kyewook Lee Ellie Shin G. Choy Synho Do 46 335 0 19 Nov 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 439 9,233 0 06 Jun 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 678 149,474 0 22 Dec 2014