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Techniques for Interpretable Machine Learning
31 July 2018
Mengnan Du
Ninghao Liu
Helen Zhou
FaML
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Papers citing
"Techniques for Interpretable Machine Learning"
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A General Taylor Framework for Unifying and Revisiting Attribution Methods
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215
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0
28 May 2021
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
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David Ahmedt-Aristizabal
M. Armin
Akila Pemasiri
Clinton Fookes
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27 May 2021
Explainable Tsetlin Machine framework for fake news detection with credibility score assessment
International Conference on Language Resources and Evaluation (LREC), 2021
Bimal Bhattarai
Ole-Christoffer Granmo
Lei Jiao
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19 May 2021
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts
Data mining and knowledge discovery (DMKD), 2021
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Bettina Finzel
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452
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15 May 2021
A Graph Neural Network Approach for Product Relationship Prediction
Design Automation Conference (DAC), 2021
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Yaxin Cui
Yan Fu
Wei Chen
GNN
106
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12 May 2021
Intelligent interactive technologies for mental health and well-being
Artificial Intelligence (AI), 2021
M. Jovanovic
Aleksandar Jevremovic
M. Pejović-Milovančević
159
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0
11 May 2021
Metamorphic Detection of Repackaged Malware
International Workshop on Metamorphic Testing (IWMT), 2021
S. Singh
Gail E. Kaiser
83
9
0
27 Apr 2021
SurvNAM: The machine learning survival model explanation
Neural Networks (NN), 2021
Lev V. Utkin
Egor D. Satyukov
A. Konstantinov
AAML
FAtt
215
36
0
18 Apr 2021
Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution
Knowledge Discovery and Data Mining (KDD), 2021
Huiqi Deng
Na Zou
Weifu Chen
Guo-Can Feng
Mengnan Du
Helen Zhou
FAtt
181
7
0
14 Apr 2021
LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Information
Ioannis Mollas
Nick Bassiliades
Grigorios Tsoumakas
159
8
0
13 Apr 2021
CACTUS: Detecting and Resolving Conflicts in Objective Functions
Subhajit Das
Alex Endert
101
0
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13 Mar 2021
Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Models
North American Chapter of the Association for Computational Linguistics (NAACL), 2021
Mengnan Du
Varun Manjunatha
R. Jain
Ruchi Deshpande
Franck Dernoncourt
Jiuxiang Gu
Tong Sun
Helen Zhou
296
118
0
11 Mar 2021
Deep Learning for Android Malware Defenses: a Systematic Literature Review
ACM Computing Surveys (CSUR), 2021
Yue Liu
Chakkrit Tantithamthavorn
Li Li
Yepang Liu
AAML
292
104
0
09 Mar 2021
An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery
L. Brito
Gian Antonio Susto
J. N. Brito
M. Duarte
131
229
0
23 Feb 2021
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs
International Conference on Intelligent User Interfaces (IUI), 2021
Harini Suresh
Kathleen M. Lewis
John Guttag
Arvind Satyanarayan
FAtt
247
29
0
17 Feb 2021
Towards Designing Computer Vision-based Explainable-AI Solution: A Use Case of Livestock Mart Industry
Devam Dave
Het Naik
Smiti Singhal
Rudresh Dwivedi
Pankesh Patel
87
1
0
08 Feb 2021
Interpretable Neural Networks based classifiers for categorical inputs
S. Zamuner
P. Rios
FAtt
MILM
69
8
0
05 Feb 2021
EUCA: the End-User-Centered Explainable AI Framework
Weina Jin
Jianyu Fan
D. Gromala
Philippe Pasquier
Ghassan Hamarneh
295
28
0
04 Feb 2021
Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond
Information Fusion (Inf. Fusion), 2021
Guang Yang
Qinghao Ye
Jun Xia
286
584
0
03 Feb 2021
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs
International Conference on Human Factors in Computing Systems (CHI), 2021
Harini Suresh
Steven R. Gomez
K. Nam
Arvind Satyanarayan
283
144
0
24 Jan 2021
Distilling Interpretable Models into Human-Readable Code
Walker Ravina
Ethan Sterling
Olexiy Oryeshko
Nathan Bell
Honglei Zhuang
Xuanhui Wang
Yonghui Wu
Alexander Grushetsky
154
2
0
21 Jan 2021
Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation
SIGKDD Explorations (SIGKDD Explor.), 2021
Fan Yang
Ninghao Liu
Mengnan Du
X. Hu
OOD
140
18
0
18 Jan 2021
Elastic Net based Feature Ranking and Selection
Shaode Yu
Haobo Chen
Hang Yu
Zhicheng Zhang
Xiaokun Liang
Wenjian Qin
Yaoqin Xie
Ping Shi
OOD
CML
98
8
0
30 Dec 2020
XAI-P-T: A Brief Review of Explainable Artificial Intelligence from Practice to Theory
Nazanin Fouladgar
Kary Främling
XAI
74
5
0
17 Dec 2020
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning
International Conference on Human Factors in Computing Systems (CHI), 2020
Wencan Zhang
Mariella Dimiccoli
Brian Y. Lim
FAtt
376
20
0
10 Dec 2020
Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations
Computer Vision and Pattern Recognition (CVPR), 2020
Wolfgang Stammer
P. Schramowski
Kristian Kersting
FAtt
527
127
0
25 Nov 2020
Domain-Level Explainability -- A Challenge for Creating Trust in Superhuman AI Strategies
Jonas Andrulis
Ole Meyer
Grégory Schott
Samuel Weinbach
V. Gruhn
155
5
0
12 Nov 2020
Explaining black-box text classifiers for disease-treatment information extraction
M. Moradi
Matthias Samwald
188
2
0
21 Oct 2020
Interpreting convolutional networks trained on textual data
International Conference on Pattern Recognition Applications and Methods (ICPRAM), 2020
Reza Marzban
Christopher Crick
FAtt
103
3
0
20 Oct 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges
Christoph Molnar
Giuseppe Casalicchio
J. Herbinger
AI4TS
AI4CE
385
474
0
19 Oct 2020
Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels
International Conference on Pattern Recognition (ICPR), 2020
Xiangwei Shi
Seyran Khademi
Yun-qiang Li
Jan van Gemert
VLM
WSOL
276
23
0
16 Oct 2020
Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines
A. Konstantinov
Lev V. Utkin
FedML
AI4CE
177
194
0
14 Oct 2020
Deep Learning for Information Systems Research
Sagar Samtani
Hongyi Zhu
Balaji Padmanabhan
Yidong Chai
Hsinchun Chen
133
1
0
07 Oct 2020
Accounts, Accountability and Agency for Safe and Ethical AI
Rob Procter
M. Rouncefield
P. Tolmie
85
2
0
03 Oct 2020
XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification
Kevin Fauvel
Tao Lin
Véronique Masson
Elisa Fromont
Alexandre Termier
BDL
AI4TS
161
131
0
10 Sep 2020
A Unified Taylor Framework for Revisiting Attribution Methods
Huiqi Deng
Na Zou
Mengnan Du
Weifu Chen
Guo-Can Feng
Helen Zhou
FAtt
TDI
358
23
0
21 Aug 2020
Explainable Recommender Systems via Resolving Learning Representations
Ninghao Liu
Yong Ge
Li Li
Helen Zhou
Rui Chen
Soo-Hyun Choi
143
29
0
21 Aug 2020
Prototype-based interpretation of the functionality of neurons in winner-take-all neural networks
Ramin Zarei-Sabzevar
Kamaledin Ghiasi-Shirazi
Ahad Harati
AAML
130
11
0
20 Aug 2020
DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models
Furui Cheng
Yao Ming
Huamin Qu
CML
HAI
140
124
0
19 Aug 2020
XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP
Sven Weinzierl
Sandra Zilker
Jens Brunk
K. Revoredo
Martin Matzner
J. Becker
165
32
0
18 Aug 2020
A Technique for Determining Relevance Scores of Process Activities using Graph-based Neural Networks
Decision Support Systems (DSS), 2020
M. Stierle
Sven Weinzierl
Maximilian Harl
Martin Matzner
108
24
0
07 Aug 2020
Dynamically Extracting Outcome-Specific Problem Lists from Clinical Notes with Guided Multi-Headed Attention
Machine Learning in Health Care (MLHC), 2020
Justin Lovelace
N. Hurley
A. Haimovich
B. Mortazavi
155
5
0
25 Jul 2020
Machine Learning Explanations to Prevent Overtrust in Fake News Detection
International Conference on Web and Social Media (ICWSM), 2020
Sina Mohseni
Fan Yang
Shiva K. Pentyala
Mengnan Du
Lu Dong
Nic Lupfer
Helen Zhou
Shuiwang Ji
Eric D. Ragan
230
50
0
24 Jul 2020
timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers
Felix Mujkanovic
Vanja Doskoc
Martin Schirneck
Patrick Schäfer
Tobias Friedrich
FAtt
AI4TS
148
28
0
15 Jul 2020
Cause vs. Effect in Context-Sensitive Prediction of Business Process Instances
Information Systems (Inf. Syst.), 2020
Jens Brunk
M. Stierle
Leon Papke
K. Revoredo
Martin Matzner
J. Becker
137
25
0
15 Jul 2020
Usefulness of interpretability methods to explain deep learning based plant stress phenotyping
Koushik Nagasubramanian
Asheesh K. Singh
Arti Singh
Soumik Sarkar
Baskar Ganapathysubramanian
FAtt
98
17
0
11 Jul 2020
Deep Learning for Anomaly Detection: A Review
Guansong Pang
Chunhua Shen
LongBing Cao
Anton Van Den Hengel
308
1,176
0
06 Jul 2020
Data-driven Regularization via Racecar Training for Generalizing Neural Networks
You Xie
Nils Thuerey
130
0
0
30 Jun 2020
Counterfactual explanation of machine learning survival models
M. Kovalev
Lev V. Utkin
CML
OffRL
261
24
0
26 Jun 2020
An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks
Ruixiang Tang
Mengnan Du
Ninghao Liu
Fan Yang
Helen Zhou
AAML
223
207
0
15 Jun 2020
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