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Concept-based explanations permit to understand the predictions of a deep neural network (DNN) through the lens of concepts specified by users.
On Estimation of a Probability Density Function and Mode
Emanuel Parzen · 1962
Earlier work this paper cites.
Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition
Thomas M. Cover · 1965
Earlier work this paper cites.
Robust linear programming discrimination of two linearly inseparable sets
Kristin P Bennett and O L Mangasarian · 1992
Earlier work this paper cites.
Support-vector networks
Corinna Cortes and Vladimir Vapnik · 1995
Earlier work this paper cites.
Python reference manual
Guido Van Rossum and Fred L Drake Jr · 1995
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals
A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, and H. E. Stanley · 2000
Earlier work this paper cites.
The impact of the mit-bih arrhythmia database
G.B. Moody and R.G. Mark · 2001
Earlier work this paper cites.
Clustering methods
Lior Rokach and Oded Maimon · 2005
Earlier work this paper cites.
Semi-Supervised Learning
Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien · 2006
Earlier work this paper cites.
Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M Bishop · 2006
Earlier work this paper cites.
Kernel methods in machine learning
Thomas Hofmann, Bernhard Schölkopf, and Alexander J Smola · 2008
Earlier work this paper cites.
Visualizing Data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Computer vision: algorithms and applications
Richard Szeliski · 2010
Earlier work this paper cites.
Reproducing kernel Hilbert spaces in probability and statistics
Alain Berlinet and Christine Thomas-Agnan · 2011
Earlier work this paper cites.
The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Earlier work this paper cites.
SMOTE: Synthetic Minority Over-sampling Technique
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer · 2011
Earlier work this paper cites.
Methods for comparing scanpaths and saliency maps: Strengths and weaknesses
Olivier Le Meur and Thierry Baccino · 2013
Earlier work this paper cites.
GloVe: Global Vectors for Word Representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
Earlier work this paper cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Earlier work this paper cites.
The Mythos of Model Interpretability
Zachary C Lipton · 2016
Earlier work this paper cites.
RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism
Edward Choi, Mohammad Taha Bahadori, Joshua A. Kulas, Andy Schuetz, Walter F. Stewart, and Jimeng Sun · 2016
Earlier work this paper cites.
Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers
Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, Klaus Robert Müller, and Wojciech Samek · 2016
Earlier work this paper cites.
"Why should i trust you?" Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Grading of prostatic adenocarcinoma: current state and prognostic implications
Jennifer Gordetsky and Jonathan Epstein · 2016
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Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez and Been Kim · 2017
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Interpretable Explanations of Black Boxes by Meaningful Perturbation
Ruth C Fong and Andrea Vedaldi · 2017
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Learning Important Features Through Propagating Activation Differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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A Unified Approach to Interpreting Model Predictions
Scott Lundberg and Su-In Lee · 2017
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Axiomatic Attribution for Deep Networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Understanding Black-box Predictions via Influence Functions
Pang Wei Koh and Percy Liang · 2017
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Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres · 2017
Cited alongside, same era.
Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2017
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
Erico Tjoa and Cuntai Guan · 2020
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Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, and Francisco Herrera · 2020
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Estimating Training Data Influence by Tracing Gradient Descent
Garima Pruthi, Frederick Liu, Mukund Sundararajan, and Satyen Kale · 2020
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On Completeness-aware Concept-Based Explanations in Deep Neural Networks
Chih-Kuan Yeh, Been Kim, Sercan Ö Arık, Chun-Liang Li, Tomas Pfister, and Pradeep Ravikumar · 2020
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Conceptual Explanations of Neural Network Prediction for Time Series
Ferdinand Kusters, Peter Schichtel, Sheraz Ahmed, and Andreas Dengel · 2020
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Validation of prostate-specific antigen laboratory values recorded in Surveillance, Epidemiology, and End Results registries
Margaret Peggy Adamo, Jessica A. Boten, Linda M. Coyle, Kathleen A. Cronin, Clara J.K. Lam, Serban Negoita, Lynne Penberthy, Jennifer L. Stevens, and Kevin C. Ward · 2017
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Recent trends in deep learning based natural language processing
Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria · 2018
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Deep Learning for Computer Vision: A Brief Review
Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, and Eftychios Protopapadakis · 2018
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Opportunities and obstacles for deep learning in biology and medicine
Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin, Brian T. Do, Gregory P. Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M. Hoffman, Wei Xie, Gail L. Rosen, Benjamin J. Lengerich, Johnny Israeli, Jack Lanchantin, Stephen Woloszynek, Anne E. Carpenter, Avanti Shrikumar, Jinbo Xu, Evan M. Cofer, Christopher A. Lavender, Srinivas C. Turaga, Amr M. Alexandari, Zhiyong Lu, David J. Harris, Dave DeCaprio, Yanjun Qi, Anshul Kundaje, Yifan Peng, Laura K. Wiley, Marwin H. S. Segler, Simina M. Boca, S. Joshua Swamidass, Austin Huang, Anthony Gitter, and Casey S. Greene · 2018
Cited alongside, same era.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Amina Adadi and Mohammed Berrada · 2018
Cited alongside, same era.
This Looks Like That: Deep Learning for Interpretable Image Recognition
Chaofan Chen, Oscar Li, Chaofan Tao, Alina Jade Barnett, Jonathan Su, and Cynthia Rudin · 2018
Cited alongside, same era.
Concept-based model explanations for Electronic Health Records
Diana Mincu, Eric Loreaux, Shaobo Hou, Sebastien Baur, Ivan Protsyuk, Martin G Seneviratne, Anne Mottram, Nenad Tomasev, Alan Karthikesanlingam, and Jessica Schrouff · 2020
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Concept whitening for interpretable image recognition
Zhi Chen, Yijie Bei, and Cynthia Rudin · 2020
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Adversarial TCAV – Robust and Effective Interpretation of Intermediate Layers in Neural Networks
Rahul Soni, Naresh Shah, Chua Tat Seng, and Jimmy D Moore · 2020
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Concept Bottleneck Models
Pang Wei Koh, Thao Nguye, Yew Siang Tang, Stephen Mussmann, Emma Pierso, Been Kim, and Percy Liang · 2020
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Explainable Machine Learning for Scientific Insights and Discoveries
Ribana Roscher, Bastian Bohn, Marco F. Duarte, and Jochen Garcke · 2020
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Captum: A unified and generic model interpretability library for PyTorch
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, and Orion Reblitz-Richardson · 2020
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Visualizing the Impact of Feature Attribution Baselines
Pascal Sturmfels, Scott Lundberg, and Su-In Lee · 2020
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A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Highly accurate protein structure prediction with AlphaFold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021
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Advancing mathematics by guiding human intuition with AI
Alex Davies, Petar Veličković, Lars Buesing, Sam Blackwell, Daniel Zheng, Nenad Tomašev, Richard Tanburn, Peter Battaglia, Charles Blundell, András Juhász, Marc Lackenby, Geordie Williamson, Demis Hassabis, and Pushmeet Kohli · 2021
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Pushing the frontiers of density functionals by solving the fractional electron problem
James Kirkpatrick, Brendan McMorrow, David H.P. Turban, Alexander L. Gaunt, James S. Spencer, Alexander G.D.G. Matthews, Annette Obika, Louis Thiry, Meire Fortunato, David Pfau, Lara Román Castellanos, Stig Petersen, Alexander W.R. Nelson, Pushmeet Kohli, Paula Mori-Sánchez, Demis Hassabis, and Aron J. Cohen · 2021
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What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research
Markus Langer, Daniel Oster, Timo Speith, Holger Hermanns, Lena Kästner, Eva Schmidt, Andreas Sesing, and Kevin Baum · 2021
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Explaining Time Series Predictions with Dynamic Masks
Jonathan Crabbé and Mihaela van der Schaar · 2021
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Explaining Latent Representations with a Corpus of Examples
Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, and Mihaela van der Schaar · 2021
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Best of both worlds: local and global explanations with human-understandable concepts
Jessica Schrouff, Sebastien Baur, Shaobo Hou, Diana Mincu, Eric Loreaux, Ralph Blanes, James Wexler, Alan Karthikesalingam, and Been Kim · 2021
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Entropy-based Logic Explanations of Neural Networks
Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Pietro Lió, Marco Gori, and Stefano Melacci · 2021
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Algorithmic Concept-based Explainable Reasoning
Dobrik Georgiev, Pietro Barbiero, Dmitry Kazhdan, Petar Veličkovi´, and Pietro Liò · 2021
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Do Concept Bottleneck Models Learn as Intended?
Andrei Margeloiu, Matthew Ashman, Umang Bhatt, Yanzhi Chen, Mateja Jamnik, and Adrian Weller · 2021
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Promises and Pitfalls of Black-Box Concept Learning Models
Anita Mahinpei, Justin Clark, Isaac Lage, Finale Doshi-Velez, and Weiwei Pan · 2021
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Explaining by Removing: A Unified Framework for Model Explanation
Ian Covert, Scott Lundberg, and Su-In Lee · 2021
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Magnetic control of tokamak plasmas through deep reinforcement learning
Jonas Degrave, Federico Felici, Jonas Buchli, Michael Neunert, Brendan Tracey, Francesco Carpanese, Timo Ewalds, Roland Hafner, Abbas Abdolmaleki, Diego de las Casas, Craig Donner, Leslie Fritz, Cristian Galperti, Andrea Huber, James Keeling, Maria Tsimpoukelli, Jackie Kay, Antoine Merle, Jean Marc Moret, Seb Noury, Federico Pesamosca, David Pfau, Olivier Sauter, Cristian Sommariva, Stefano Coda, Basil Duval, Ambrogio Fasoli, Pushmeet Kohli, Koray Kavukcuoglu, Demis Hassabis, and Martin Riedmiller · 2022
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Label-Free Explainability for Unsupervised Models
Jonathan Crabbé and Mihaela van der Schaar · 2022
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