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Deep neural networks have been well-known for their superb handling of various machine learning and artificial intelligence tasks.
Reinforcement learning: A survey
Leslie Pack Kaelbling, Michael L. Littman, and Andrew W. Moore · 1996
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Fab: content-based, collaborative recommendation
Marko Balabanović and Yoav Shoham · 1997
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Explaining collaborative filtering recommendations
Jonathan L. Herlocker, Joseph A. Konstan, and John Riedl · 2000
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Visualizing higher-layer features of a deep network
Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2009
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Causal inference in statistics: An overview
Judea Pearl et al · 2009
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Caltech-UCSD birds 200
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona · 2010
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Prototype selection for interpretable classification
Jacob Bien and Robert Tibshirani · 2011
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Introduction to recommender systems handbook
Francesco Ricci, Lior Rokach, and Bracha Shapira · 2011
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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Computer-aided classification of lung nodules on computed tomography images via deep learning technique
Kai-Lung Hua, Che-Hao Hsu, Shintami Chusnul Hidayati, Wen-Huang Cheng, and Yu-Jen Chen · 2015
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Deep neural decision forests
Peter Kontschieder, Madalina Fiterau, Antonio Criminisi, and Samuel Rota Bulò · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey E. Hinton · 2015
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Understanding deep image representations by inverting them
Aravindh Mahendran and Andrea Vedaldi · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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Layer-wise relevance propagation for neural networks with local renormalization layers
Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, Klaus-Robert Müller, and Wojciech Samek · 2016
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Wide & deep learning for recommender systems
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah · 2016
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Deep neural networks for youtube recommendations
Paul Covington, Jay Adams, and Emre Sargin · 2016
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The netflix recommender system: Algorithms, business value, and innovation
Carlos Alberto Gomez-Uribe and Neil Hunt · 2016
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Anh Mai Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune · 2016
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“Why should I trust you?”: Explaining the predictions of any classifier
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Àgata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Deep reinforcement learning: A brief survey
Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath · 2017
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Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
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Improving interpretability of deep neural networks with semantic information
Yinpeng Dong, Hang Su, Jun Zhu, and Bo Zhang · 2017
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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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Deep learning for computational chemistry
Garrett B. Goh, Nathan O. Hodas, and Abhinav Vishnu · 2017
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BadNets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Saliency guided end-to-end learning for weakly supervised object detection
Baisheng Lai and Xiaojin Gong · 2017
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Interpretable & explorable approximations of black box models
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, and Jure Leskovec · 2017
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Deep reinforcement learning: An overview
Yuxi Li · 2017
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A survey on deep learning in medical image analysis
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A. W. M. van der Laak, Bram van Ginneken, and Clara I. Sánchez · 2017
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A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee · 2017
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Explaining nonlinear classification decisions with deep taylor decomposition
Grégoire Montavon, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller · 2017
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Dynamic routing between capsules
Sara Sabour, Nicholas Frosst, and Geoffrey E. Hinton · 2017
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Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller · 2017
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Interpretable convolutional neural networks with dual local and global attention for review rating prediction
Sungyong Seo, Jing Huang, Hao Yang, and Yan Liu · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy P. Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, and Demis Hassabis · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda B. Viégas, and Martin Wattenberg · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the GDPR
Sandra Wachter, Brent D. Mittelstadt, and Chris Russell · 2017
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Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer
Sergey Zagoruyko and Nikos Komodakis · 2017
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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, and Been Kim · 2018
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Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation
Jiwoon Ahn and Suha Kwak · 2018
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On the robustness of interpretability methods
David Alvarez-Melis and Tommi S. Jaakkola · 2018
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Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2018
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Adversarial attacks and defences: A survey
Anirban Chakraborty, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, and Debdeep Mukhopadhyay · 2018
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Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks
Aditya Chattopadhyay, Anirban Sarkar, Prantik Howlader, and Vineeth N. Balasubramanian · 2018
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Neural attentional rating regression with review-level explanations
Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma · 2018
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Interpreting recurrent and attention-based neural models: a case study on natural language inference
Reza Ghaeini, Xiaoli Z. Fern, and Prasad Tadepalli · 2018
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Visualizing and understanding atari agents
Samuel Greydanus, Anurag Koul, Jonathan Dodge, and Alan Fern · 2018
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Human perceptions of fairness in algorithmic decision making: A case study of criminal risk prediction
Nina Grgic-Hlaca, Elissa M. Redmiles, Krishna P. Gummadi, and Adrian Weller · 2018
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Understanding individual decisions of cnns via contrastive backpropagation
Jindong Gu, Yinchong Yang, and Volker Tresp · 2018
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Matrix capsules with EM routing
Geoffrey E. Hinton, Sara Sabour, and Nicholas Frosst · 2018
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Transparency and explanation in deep reinforcement learning neural networks
Rahul Iyer, Yuezhang Li, Huao Li, Michael Lewis, Ramitha Sundar, and Katia P. Sycara · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie J. Cai, James Wexler, Fernanda B. Viégas, and Rory Sayres · 2018
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Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
Sergey Levine, Peter Pastor, Alex Krizhevsky, Julian Ibarz, and Deirdre Quillen · 2018
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Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions
Oscar Li, Hao Liu, Chaofan Chen, and Cynthia Rudin · 2018
Cited alongside, same era.
The mythos of model interpretability
Zachary C. Lipton · 2018
Cited alongside, same era.
Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2018
Cited alongside, same era.
RISE: randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
Cited alongside, same era.
Model agnostic supervised local explanations
Gregory Plumb, Denali Molitor, and Ameet Talwalkar · 2018
Cited alongside, same era.
Anchors: High-precision model-agnostic explanations
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
Cited alongside, same era.
Interpreting cnns via decision trees
Quanshi Zhang, Yu Yang, Haotian Ma, and Ying Nian Wu · 2019
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Deep learning based recommender system: A survey and new perspectives
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay · 2019
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Interpreting adversarially trained convolutional neural networks
Tianyuan Zhang and Zhanxing Zhu · 2019
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Quantifying attention flow in transformers
Samira Abnar and Willem H. Zuidema · 2020
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Debugging tests for model explanations
Julius Adebayo, Michael Muelly, Ilaria Liccardi, and Been Kim · 2020
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Learning dexterous in-hand manipulation
Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Józefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, Jonas Schneider, Szymon Sidor, Josh Tobin, Peter Welinder, Lilian Weng, and Wojciech Zaremba · 2020
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Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
Andrew Slavin Ross and Finale Doshi-Velez · 2018
Cited alongside, same era.
Lstmvis: A tool for visual analysis of hidden state dynamics in recurrent neural networks
Hendrik Strobelt, Sebastian Gehrmann, Hanspeter Pfister, and Alexander M. Rush · 2018
Cited alongside, same era.
Personalized top-n sequential recommendation via convolutional sequence embedding
Jiaxi Tang and Ke Wang · 2018
Cited alongside, same era.
CBAM: convolutional block attention module
Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon · 2018
Cited alongside, same era.
Mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
Top-down neural attention by excitation backprop
Jianming Zhang, Sarah Adel Bargal, Zhe Lin, Jonathan Brandt, Xiaohui Shen, and Stan Sclaroff · 2018
Cited alongside, same era.
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Generating fact checking explanations
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein · 2020
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Exploratory not explanatory: Counterfactual analysis of saliency maps for deep reinforcement learning
Akanksha Atrey, Kaleigh Clary, and David D. Jensen · 2020
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SAM: the sensitivity of attribution methods to hyperparameters
Naman Bansal, Chirag Agarwal, and Anh Nguyen · 2020
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A generic and model-agnostic exemplar synthetization framework for explainable AI
Antonio Barbalau, Adrian Cosma, Radu Tudor Ionescu, and Marius Popescu · 2020
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Robustbench: a standardized adversarial robustness benchmark
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, and Matthias Hein · 2020
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Ablation-CAM: Visual explanations for deep convolutional network via gradient-free localization
Saurabh Desai and Harish G. Ramaswamy · 2020
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Contrastive graph neural network explanation
Lukas Faber, Amin K. Moghaddam, and Roger Wattenhofer · 2020
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Does learning require memorization? a short tale about a long tail
Vitaly Feldman · 2020
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What neural networks memorize and why: Discovering the long tail via influence estimation
Vitaly Feldman and Chiyuan Zhang · 2020
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard S. Zemel, Wieland Brendel, Matthias Bethge, and Felix A. Wichmann · 2020
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Graphlime: Local interpretable model explanations for graph neural networks
Qiang Huang, Makoto Yamada, Yuan Tian, Dinesh Singh, Dawei Yin, and Yi Chang · 2020
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Towards quantification of explainability in explainable artificial intelligence methods
Sheikh Rabiul Islam, William Eberle, and Sheikh K. Ghafoor · 2020
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Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg · 2020
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Puzzle mix: Exploiting saliency and local statistics for optimal mixup
Jang-Hyun Kim, Wonho Choo, and Hyun Oh Song · 2020
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NILE : Natural language inference with faithful natural language explanations
Sawan Kumar and Partha P. Talukdar · 2020
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Parameterized explainer for graph neural network
Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang · 2020
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Improving interpretability in medical imaging diagnosis using adversarial training
Andrei Margeloiu, Nikola Simidjievski, Mateja Jamnik, and Adrian Weller · 2020
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Causal interpretability for machine learning - problems, methods and evaluation
Raha Moraffah, Mansooreh Karami, Ruocheng Guo, Adrienne Raglin, and Huan Liu · 2020
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Explaining machine learning classifiers through diverse counterfactual explanations
Ramaravind Kommiya Mothilal, Amit Sharma, and Chenhao Tan · 2020
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Relative attributing propagation: Interpreting the comparative contributions of individual units in deep neural networks
Woo-Jeoung Nam, Shir Gur, Jaesik Choi, Lior Wolf, and Seong-Whan Lee · 2020
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Identifying mislabeled data using the area under the margin ranking
Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg, and Kilian Q. Weinberger · 2020
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Regularizing black-box models for improved interpretability
Gregory Plumb, Maruan Al-Shedivat, Ángel Alexander Cabrera, Adam Perer, Eric P. Xing, and Ameet Talwalkar · 2020
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Controlling generative models with continuous factors of variations
Antoine Plumerault, Hervé Le Borgne, and Céline Hudelot · 2020
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Explainable reinforcement learning: A survey
Erika Puiutta and Eric M. S. P. Veith · 2020
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Explain your move: Understanding agent actions using specific and relevant feature attribution
Nikaash Puri, Sukriti Verma, Piyush Gupta, Dhruv Kayastha, Shripad Deshmukh, Balaji Krishnamurthy, and Sameer Singh · 2020
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Chexaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV
Pranav Rajpurkar, Chloe O’Connell, Amit Schechter, Nishit Asnani, Jason Li, Amirhossein Kiani, Robyn L Ball, Marc Mendelson, Gary Maartens, Daniël J van Hoving, et al · 2020
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Grad-CAM: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2020
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Ophthalmic diagnosis using deep learning with fundus images - A critical review
Sourya Sengupta, Amitojdeep Singh, Henry A. Leopold, Tanmay Gulati, and Vasudevan Lakshminarayanan · 2020
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Explainable deep learning models in medical image analysis
Amitojdeep Singh, Sourya Sengupta, and Vasudevan Lakshminarayanan · 2020
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Dataset cartography: Mapping and diagnosing datasets with training dynamics
Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, and Yejin Choi · 2020
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Counterfactual explanations for machine learning: A review
Sahil Verma, John P. Dickerson, and Keegan Hines · 2020
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Unsupervised discovery of interpretable directions in the GAN latent space
Andrey Voynov and Artem Babenko · 2020
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Score-CAM: Score-weighted visual explanations for convolutional neural networks
Haofan Wang, Zifan Wang, Mengnan Du, Fan Yang, Zijian Zhang, Sirui Ding, Piotr Mardziel, and Xia Hu · 2020
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Causality learning: A new perspective for interpretable machine learning
Guandong Xu, Tri Dung Duong, Qian Li, Shaowu Liu, and Xianzhi Wang · 2020
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Explainable recommendation: A survey and new perspectives
Yongfeng Zhang and Xu Chen · 2020
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Neural additive models: Interpretable machine learning with neural nets
Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Benjamin J. Lengerich, Rich Caruana, and Geoffrey E. Hinton · 2021
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Getting a CLUE: A method for explaining uncertainty estimates
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Robust counterfactual explanations on graph neural networks
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Deep learning through the lens of example difficulty
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Generic attention-model explainability for interpreting bi-modal and encoder-decoder transformers
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Transformer interpretability beyond attention visualization
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Highly accurate protein structure prediction with alphafold
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Asad Khan, E. A. Huerta, and Huihuo Zheng · 2021
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Keep CALM and improve visual feature attribution
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Trustworthy AI: from principles to practices
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Cross-model consensus of explanations and beyond for image classification models: An empirical study
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What do you see?: Evaluation of explainable artificial intelligence (XAI) interpretability through neural backdoors
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Explaining deep neural networks and beyond: A review of methods and applications
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Closed-form factorization of latent semantics in gans
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Explanation-based data augmentation for image classification
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Semantic hierarchy emerges in deep generative representations for scene synthesis
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Non-salient region object mining for weakly supervised semantic segmentation
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Explaining information flow inside vision transformers using markov chain
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Understanding deep learning (still) requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2021
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Distilling a neural network into a soft decision tree
Nicholas Frosst and Geoffrey E. Hinton · 2071
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