Title
Towards a More Reliable Interpretation of Machine Learning Outputs for Safety-Critical Systems using Feature Importance Fusion D. Rengasamy Benjamin Rothwell Grazziela Figueredo FaML FAtt 27 35 0 11 Sep 2020
Transparency and granularity in the SP Theory of Intelligence and its realisation in the SP Computer Model J. Wolff 28 5 0 07 Sep 2020
Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic Dataset Erico Tjoa Cuntai Guan XAI FAtt 103 27 0 07 Sep 2020
Model extraction from counterfactual explanations Ulrich Aïvodji Alexandre Bolot Sébastien Gambs MIACV MLAU 87 52 0 03 Sep 2020
Micro-entries: Encouraging Deeper Evaluation of Mental Models Over Time for Interactive Data Systems Jeremy E. Block Eric D. Ragan 54 8 0 02 Sep 2020
Face Image Quality Assessment: A Literature Survey Torsten Schlett Christian Rathgeb O. Henniger Javier Galbally Julian Fierrez Christoph Busch CVBM 116 131 0 02 Sep 2020
Machine Reasoning Explainability K. Čyras R. Badrinath S. Mohalik A. Mujumdar Alexandros Nikou Alessandro Previti Vaishnavi Sundararajan Aneta Vulgarakis Feljan LRM 79 13 0 01 Sep 2020
Algorithmic Transparency with Strategic Users Qiaochu Wang Yan-ping Huang Stefanus Jasin P. Singh FaML FedML 44 24 0 21 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 62 27 0 18 Aug 2020
Explainability in Deep Reinforcement Learning Alexandre Heuillet Fabien Couthouis Natalia Díaz Rodríguez XAI 239 283 0 15 Aug 2020
Explainable Artificial Intelligence Based Fault Diagnosis and Insight Harvesting for Steel Plates Manufacturing A. Kharal 14 6 0 10 Aug 2020
Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges Aritz D. Martinez Javier Del Ser Esther Villar-Rodriguez E. Osaba Javier Poyatos Siham Tabik Daniel Molina Francisco Herrera 132 28 0 09 Aug 2020
Learning CNN filters from user-drawn image markers for coconut-tree image classification Italos Estilon de Souza A. X. Falcão 33 23 0 08 Aug 2020
Fuzzy Jaccard Index: A robust comparison of ordered lists Matej Petković Blaž Škrlj D. Kocev Nikola Simidjievski 37 13 0 05 Aug 2020
Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence Yuzhu Wu Zhen Zhang Gang Kou Hengjie Zhang Xiangrui Chao Congcong Li Yucheng Dong Francisco Herrera 18 163 0 04 Aug 2020
Improving concave point detection to better segment overlapped objects in images Miquel Miró-Nicolau Gabriel Moyà-Alcover Manuel González Hidalgo Antoni Jaume-i-Capó 11 1 0 03 Aug 2020
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies A. Markus J. Kors P. Rijnbeek 91 472 0 31 Jul 2020
Supervised Machine Learning Techniques: An Overview with Applications to Banking Linwei Hu Jie Chen J. Vaughan Hanyu Yang Kelly Wang Agus Sudjianto V. Nair 52 23 0 28 Jul 2020
Memory networks for consumer protection:unfairness exposed Federico Ruggeri F. Lagioia Marco Lippi Paolo Torroni 33 0 0 24 Jul 2020
InstanceFlow: Visualizing the Evolution of Classifier Confusion on the Instance Level Michael Pühringer A. Hinterreiter M. Streit 45 17 0 22 Jul 2020
On Disentangling Spoof Trace for Generic Face Anti-Spoofing Yaojie Liu J. Stehouwer Xiaoming Liu AAML CVBM 78 109 0 17 Jul 2020
Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review A. Shoeibi Marjane Khodatars M. Jafari Navid Ghassemi Delaram Sadeghi ... Z. Sani F. Khozeimeh S. Nahavandi U. Acharya Juan M Gorriz 170 187 0 16 Jul 2020
On quantitative aspects of model interpretability An-phi Nguyen María Rodríguez Martínez 73 115 0 15 Jul 2020
VAE-LIME: Deep Generative Model Based Approach for Local Data-Driven Model Interpretability Applied to the Ironmaking Industry C. Schockaert Vadim Macher A. Schmitz 48 18 0 15 Jul 2020
Explaining Deep Neural Networks using Unsupervised Clustering Yu-Han Liu Sercan O. Arik SSL AI4CE 76 11 0 15 Jul 2020
When stakes are high: balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates Roel Henckaerts Katrien Antonio Marie-Pier Côté 25 3 0 14 Jul 2020
Addressing the interpretability problem for deep learning using many valued quantum logic S. Shah AI4CE 13 1 0 02 Jul 2020
Counterfactual explanation of machine learning survival models M. Kovalev Lev V. Utkin CML OffRL 117 19 0 26 Jun 2020
DOME: Recommendations for supervised machine learning validation in biology Ian Walsh D. Fishman Dario Garcia-Gasulla T. Titma Gianluca Pollastri The ELIXIR Machine Learning focus group J. Harrow Fotis Psomopoulos Silvio C.E. Tosatto AI4CE 19 3 0 25 Jun 2020
Improving Workflow Integration with xPath: Design and Evaluation of a Human-AI Diagnosis System in Pathology H. Gu Yuan Liang Yifan Xu Christopher Kazu Williams S. Magaki ... Wenzhong Yan X. R. Zhang Yang Li Mohammad Haeri Xiang Ánthony' Chen 91 31 0 23 Jun 2020
Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AI A. Tavanaei XAI 97 31 0 19 Jun 2020
On the Learnability of Concepts: With Applications to Comparing Word Embedding Algorithms Adam Sutton N. Cristianini 54 8 0 17 Jun 2020
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey Arun Das P. Rad XAI 179 608 0 16 Jun 2020
Generating Reliable and Efficient Predictions of Human Motion: A Promising Encounter between Physics and Neural Networks Alessandro Antonucci G. P. R. Papini Luigi Palopoli Daniele Fontanelli 3DH 43 17 0 15 Jun 2020
Explaining Predictions by Approximating the Local Decision Boundary G. Vlassopoulos T. Erven Henry Brighton Vlado Menkovski FAtt 51 8 0 14 Jun 2020
A framework for step-wise explaining how to solve constraint satisfaction problems B. Bogaerts Emilio Gamba Tias Guns LRM 32 17 0 11 Jun 2020
Artificial Intelligence (AI)-Centric Management of Resources in Modern Distributed Computing Systems Shashikant Ilager R. Muralidhar Rajkumar Buyya GNN 44 22 0 09 Jun 2020
Higher-Order Explanations of Graph Neural Networks via Relevant Walks Thomas Schnake Oliver Eberle Jonas Lederer Shinichi Nakajima Kristof T. Schütt Klaus-Robert Muller G. Montavon 113 224 0 05 Jun 2020
ExKMC: Expanding Explainable $k$ -Means Clustering Nave Frost Michal Moshkovitz Cyrus Rashtchian 78 56 0 03 Jun 2020
COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images Siham Tabik A. Gómez-Ríos J. L. Martín-Rodríguez I. Sevillano-García M. Rey-Area ... J. Luengo M. A. Valero-González P. García-Villanova E. Olmedo-Sánchez Francisco Herrera 74 273 0 02 Jun 2020
Data-Driven Methods to Monitor, Model, Forecast and Control Covid-19 Pandemic: Leveraging Data Science, Epidemiology and Control Theory Teodoro Alamo Daniel Gutiérrez-Reina P. Millán 52 27 0 01 Jun 2020
Explainable deep learning models in medical image analysis Amitojdeep Singh S. Sengupta Vasudevan Lakshminarayanan XAI 97 502 0 28 May 2020
An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges D. Charte F. Charte M. J. D. Jesus Francisco Herrera SSL OOD 145 51 0 21 May 2020
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps Tobias Huber Katharina Weitz Elisabeth André Ofra Amir FAtt 77 67 0 18 May 2020
Applying Genetic Programming to Improve Interpretability in Machine Learning Models Leonardo Augusto Ferreira F. G. Guimarães Rodrigo C. P. Silva 41 37 0 18 May 2020
Evolved Explainable Classifications for Lymph Node Metastases Iam Palatnik de Sousa M. Vellasco E. C. Silva 31 6 0 14 May 2020
Towards explainable classifiers using the counterfactual approach -- global explanations for discovering bias in data Agnieszka Mikołajczyk M. Grochowski Arkadiusz Kwasigroch FAtt CML 39 3 0 05 May 2020
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds M. Kovalev Lev V. Utkin AAML 81 32 0 05 May 2020
SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models Lev V. Utkin M. Kovalev E. Kasimov 53 10 0 05 May 2020
A multi-component framework for the analysis and design of explainable artificial intelligence S. Atakishiyev H. Babiker Nawshad Farruque R. Goebel1 Myeongjung Kima M. H. Motallebi J. Rabelo T. Syed O. R. Zaïane 65 39 0 05 May 2020