Fetching the paper…
Reading the bibliography…
The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals
A. Goldberger, L. Amaral, L. Glass, J. Hausdorff, P. C. Ivanov, R. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, and H. E. Stanley · 2000
Earlier work this paper cites.
Detecting statistical interactions with additive groves of trees
Daria Sorokina, Rich Caruana, Mirek Riedewald, and Daniel Fink · 2008
Earlier work this paper cites.
Polynomial calculation of the shapley value based on sampling
Javier Castro, Daniel Gómez, and Juan Tejada · 2009
Earlier work this paper cites.
An efficient explanation of individual classifications using game theory
Erik Strumbelj and Igor Kononenko · 2010
Earlier work this paper cites.
Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders · 2012
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
J Springenberg, Alexey Dosovitskiy, Thomas Brox, and M Riedmiller · 2015
Earlier work this paper cites.
Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller · 2016
Earlier work this paper cites.
Recommendations as treatments: Debiasing learning and evaluation
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
Earlier work this paper cites.
Iterative orthogonal feature projection for diagnosing bias in black-box models
Julius Adebayo and Lalana Kagal · 2016
Earlier work this paper cites.
The influence of race/ethnicity and education on family ratings of the quality of dying in the icu
Janet J. Lee, Ann C. Long, J. Randall Curtis, and Ruth A. Engelberg · 2016
Earlier work this paper cites.
Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
Earlier work this paper cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Earlier work this paper cites.
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
Earlier work this paper cites.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Earlier work this paper cites.
Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
Earlier work this paper cites.
Clinical intervention prediction and understanding using deep networks
Harini Suresh, Nathan Hunt, Alistair Johnson, Leo Anthony Celi, Peter Szolovits, and Marzyeh Ghassemi · 2017
Earlier work this paper cites.
A roadmap for a rigorous science of interpretability
Finale Doshi-Velez and Been Kim · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Association between immigrant status and end-of-life care in ontario, canada
Christopher J Yarnell, L. Fu, D. Manuel, P. Tanuseputro, T. Stukel, R. Pinto, D. Scales, A. Laupacis, and R. Fowler · 2017
Earlier work this paper cites.
Benchmarking deep learning models on large healthcare datasets
Sanjay Purushotham, Chuizheng Meng, Zhengping Che, and Yan Liu · 2018
Earlier work this paper cites.
Why is my classifier discriminatory?
Irene Chen, Fredrik D Johansson, and David Sontag · 2018
Earlier work this paper cites.
Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2018
Cited alongside, same era.
Detecting statistical interactions from neural network weights
Michael Tsang, Dehua Cheng, and Yan Liu · 2018
Cited alongside, same era.
Neural interaction transparency (nit): Disentangling learned interactions for improved interpretability
Michael Tsang, Hanpeng Liu, Sanjay Purushotham, Pavankumar Murali, and Yan Liu · 2018
Cited alongside, same era.
Kedar Dhamdhere, Mukund Sundararajan, and Qiqi Yan · 2018
Cited alongside, same era.
Computationally efficient measures of internal neuron importance
Avanti Shrikumar, Jocelin Su, and Anshul Kundaje · 2018
Cited alongside, same era.
Simplicity creates inequity: implications for fairness, stereotypes, and interpretability
Jon Kleinberg and Sendhil Mullainathan · 2019
Later among the works it cites.
Measuring unfairness through game-theoretic interpretability
Juliana Cesaro and Fabio Gagliardi Cozman · 2019
Later among the works it cites.
Democratizing ehr analyses a comprehensive pipeline for learning from clinical data
Michael Sjoding, Shengpu Tang, Parmida Davarmanesh, Yanmeng Song, Danai Koutra, and Jenna Wiens · 2019
Later among the works it cites.
Autoint: Automatic feature interaction learning via self-attentive neural networks
Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, and Jian Tang · 2019
Later among the works it cites.
Attention is not explanation
Sarthak Jain and Byron C Wallace · 2019
Later among the works it cites.
Fairness under unawareness: Assessing disparity when protected class is unobserved
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Influence-directed explanations for deep convolutional networks
Klas Leino, Shayak Sen, Anupam Datta, Matt Fredrikson, and Linyi Li · 2018
Cited alongside, same era.
Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al · 2018
Cited alongside, same era.
Interpretable basis decomposition for visual explanation
Bolei Zhou, Yiyou Sun, David Bau, and Antonio Torralba · 2018
Cited alongside, same era.
The accuracy, fairness, and limits of predicting recidivism
Julia Dressel and Hany Farid · 2018
Cited alongside, same era.
Rachel KE Bellamy, Kuntal Dey, Michael Hind, Samuel C Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, et al · 2018
Cited alongside, same era.
Invariant representations without adversarial training
Daniel Moyer, Shuyang Gao, Rob Brekelmans, Aram Galstyan, and Greg Ver Steeg · 2018
Cited alongside, same era.
The mythos of model interpretability
Zachary C Lipton · 2018
Cited alongside, same era.
Jiahao Chen, Nathan Kallus, Xiaojie Mao, Geoffry Svacha, and Madeleine Udell · 2019
Later among the works it cites.
Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii
Shirly Wang, Matthew BA McDermott, Geeticka Chauhan, Marzyeh Ghassemi, Michael C Hughes, and Tristan Naumann · 2020
Later among the works it cites.
Mimic-iv (version 0.4)
Alistair Johnson, Lucas Bulgarelli, Tom Pollard, Steven Horng, Leo Anthony Celi, and Roger Mark · 2020
Later among the works it cites.
How does this interaction affect me? interpretable attribution for feature interactions
Michael Tsang, Sirisha Rambhatla, and Yan Liu · 2020
Later among the works it cites.
Interpretable Machine Learning
Christoph Molnar · 2020
Later among the works it cites.
The shapley taylor interaction index
Mukund Sundararajan, Kedar Dhamdhere, and Ashish Agarwal · 2020
Later among the works it cites.
Explaining explanations: Axiomatic feature interactions for deep networks
Joseph D Janizek, Pascal Sturmfels, and Su-In Lee · 2020
Later among the works it cites.
Benchmarking deep learning interpretability in time series predictions
Aya Abdelsalam Ismail, Mohamed Gunady, Hector Corrada Bravo, and Soheil Feizi · 2020
Later among the works it cites.
Explaining an increase in predicted risk for clinical alerts
Michaela Hardt, Alvin Rajkomar, Gerardo Flores, Andrew Dai, Michael Howell, Greg Corrado, Claire Cui, and Moritz Hardt · 2020
Later among the works it cites.
Evaluating attribution for graph neural networks
Benjamin Sanchez-Lengeling, Jennifer Wei, Brian Lee, Emily Reif, Peter Wang, Wesley Wei Qian, Kevin McCloskey, Lucy Colwell, and Alexander Wiltschko · 2020
Later among the works it cites.
Artificial intelligence and algorithmic bias: Source, detection, mitigation, and implications
Runshan Fu, Yan Huang, and Param Vir Singh · 2020
Later among the works it cites.
Addressing bias in prediction models by improving subpopulation calibration
Noam Barda, Gal Yona, Guy N Rothblum, Philip Greenland, Morton Leibowitz, Ran Balicer, Eitan Bachmat, and Noa Dagan · 2020
Later among the works it cites.
Minimax pareto fairness: A multi objective perspective
Natalia Martinez, Martin Bertran, and Guillermo Sapiro · 2020
Later among the works it cites.
Hurtful words: quantifying biases in clinical contextual word embeddings
Haoran Zhang, Amy X Lu, Mohamed Abdalla, Matthew McDermott, and Marzyeh Ghassemi · 2020
Later among the works it cites.
Sen Cui, Weishen Pan, Changshui Zhang, and Fei Wang · 2020
Later among the works it cites.
Exploring text specific and blackbox fairness algorithms in multimodal clinical nlp
John Chen, Ian Berlot-Atwell, Safwan Hossain, Xindi Wang, and Frank Rudzicz · 2020
Later among the works it cites.
Games for fairness and interpretability
Eric Chu, Nabeel Gillani, and Sneha Priscilla Makini · 2020
Later among the works it cites.
Fairness in deep learning: A computational perspective
Mengnan Du, Fan Yang, Na Zou, and Xia Hu · 2020
Later among the works it cites.
Why attention is not explanation: Surgical intervention and causal reasoning about neural models
Christopher Grimsley, Elijah Mayfield, and Julia R.S. Bursten · 2020
Later among the works it cites.
Fairness without demographics through adversarially reweighted learning
Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed H Chi · 2021
Closest in time.