ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2102.06761
  4. Cited By
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning
  Models on MIMIC-IV Dataset

MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset

12 February 2021
Chuizheng Meng
Loc Trinh
Nan Xu
Yan Liu
ArXivPDFHTML

Papers citing "MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset"

14 / 14 papers shown
Title
Minimax Pareto Fairness: A Multi Objective Perspective
Minimax Pareto Fairness: A Multi Objective Perspective
Natalia Martínez
Martín Bertrán
Guillermo Sapiro
FaML
47
191
0
03 Nov 2020
Measuring Unfairness through Game-Theoretic Interpretability
Measuring Unfairness through Game-Theoretic Interpretability
Juliana Cesaro
Fabio Gagliardi Cozman
FAtt
54
13
0
12 Oct 2019
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation
  Pipeline for MIMIC-III
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III
Shirly Wang
Matthew B. A. McDermott
Geeticka Chauhan
Michael C. Hughes
Tristan Naumann
Marzyeh Ghassemi
45
207
0
19 Jul 2019
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
Tian Guo
Tao R. Lin
Nino Antulov-Fantulin
AI4TS
47
155
0
28 May 2019
CERTIFAI: Counterfactual Explanations for Robustness, Transparency,
  Interpretability, and Fairness of Artificial Intelligence models
CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models
Shubham Sharma
Jette Henderson
Joydeep Ghosh
33
87
0
20 May 2019
Attention is not Explanation
Attention is not Explanation
Sarthak Jain
Byron C. Wallace
FAtt
87
1,307
0
26 Feb 2019
Clinical Intervention Prediction and Understanding using Deep Networks
Clinical Intervention Prediction and Understanding using Deep Networks
Harini Suresh
Nathan Hunt
Alistair E. W. Johnson
Leo Anthony Celi
Peter Szolovits
Marzyeh Ghassemi
OOD
52
131
0
23 May 2017
Learning Important Features Through Propagating Activation Differences
Learning Important Features Through Propagating Activation Differences
Avanti Shrikumar
Peyton Greenside
A. Kundaje
FAtt
123
3,848
0
10 Apr 2017
Multitask learning and benchmarking with clinical time series data
Multitask learning and benchmarking with clinical time series data
Hrayr Harutyunyan
Hrant Khachatrian
David C. Kale
Greg Ver Steeg
Aram Galstyan
OOD
AI4TS
120
867
0
22 Mar 2017
Axiomatic Attribution for Deep Networks
Axiomatic Attribution for Deep Networks
Mukund Sundararajan
Ankur Taly
Qiqi Yan
OOD
FAtt
122
5,920
0
04 Mar 2017
Inherent Trade-Offs in the Fair Determination of Risk Scores
Inherent Trade-Offs in the Fair Determination of Risk Scores
Jon M. Kleinberg
S. Mullainathan
Manish Raghavan
FaML
84
1,762
0
19 Sep 2016
The Mythos of Model Interpretability
The Mythos of Model Interpretability
Zachary Chase Lipton
FaML
123
3,672
0
10 Jun 2016
Striving for Simplicity: The All Convolutional Net
Striving for Simplicity: The All Convolutional Net
Jost Tobias Springenberg
Alexey Dosovitskiy
Thomas Brox
Martin Riedmiller
FAtt
181
4,653
0
21 Dec 2014
Visualizing and Understanding Convolutional Networks
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler
Rob Fergus
FAtt
SSL
329
15,825
0
12 Nov 2013
1