Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction

4 March 2021

Papers citing "Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction"

20 / 20 papers shown

Title
Concept Bottleneck Models Pang Wei Koh Thao Nguyen Y. S. Tang Stephen Mussmann Emma Pierson Been Kim Percy Liang 94 818 0 09 Jul 2020
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 319 10,591 0 17 Feb 2020
DiffTaichi: Differentiable Programming for Physical Simulation Yuanming Hu Luke Anderson Tzu-Mao Li Qi Sun N. Carr Jonathan Ragan-Kelley F. Durand 59 384 0 01 Oct 2019
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use S. Tonekaboni Shalmali Joshi M. Mccradden Anna Goldenberg 63 391 0 13 May 2019
Unsupervised Deep Learning for Bayesian Brain MRI Segmentation Adrian Dalca Evan M. Yu Polina Golland Bruce Fischl M. Sabuncu Juan Eugenio Iglesias OOD 41 66 0 25 Apr 2019
Deep neural networks can predict mortality from 12-lead electrocardiogram voltage data S. Raghunath Alvaro E. Ulloa Cerna Linyuan Jing David P. vanMaanen Joshua V. Stough ... B. Delisle Amro Alsaid Dominik Beer C. Haggerty Brandon K. Fornwalt 35 183 0 15 Apr 2019
Regularizing Black-box Models for Improved Interpretability Gregory Plumb Maruan Al-Shedivat Ángel Alexander Cabrera Adam Perer Eric Xing Ameet Talwalkar AAML 54 79 0 18 Feb 2019
Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making Carrie J. Cai Emily Reif Narayan Hegde J. Hipp Been Kim ... Martin Wattenberg F. Viégas G. Corrado Martin C. Stumpe Michael Terry 98 402 0 08 Feb 2019
TED: Teaching AI to Explain its Decisions Michael Hind Dennis L. Wei Murray Campbell Noel Codella Amit Dhurandhar Aleksandra Mojsilović Karthikeyan N. Ramamurthy Kush R. Varshney 53 110 0 12 Nov 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 122 940 0 20 Jun 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 191 1,834 0 30 Nov 2017
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions Oscar Li Hao Liu Chaofan Chen Cynthia Rudin 156 588 0 13 Oct 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 236 4,249 0 22 Jun 2017
Contextual Explanation Networks Maruan Al-Shedivat Kumar Avinava Dubey Eric Xing CML 73 82 0 29 May 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 863 21,760 0 22 May 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 175 5,968 0 04 Mar 2017
Rationalizing Neural Predictions Tao Lei Regina Barzilay Tommi Jaakkola 108 811 0 13 Jun 2016
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.4K 149,842 0 22 Dec 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 281 7,279 0 20 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 475 15,861 0 12 Nov 2013