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Most explanation methods in deep learning map importance estimates for a model's prediction back to the original input space.
Induction of decision trees
J. Ross Quinlan · 1986
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A differential approach to inference in bayesian networks
Adnan Darwiche · 2003
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Learning to detect unseen object classes by between-class attribute transfer
Christoph H. Lampert, Hannes Nickisch, and Stefan Harmeling · 2009
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Thinking, fast and slow
Daniel Kahneman · 2011
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Sum-product networks: A new deep architecture
Hoifung Poon and Pedro M. Domingos · 2011
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Neural-symbolic learning systems: foundations and applications
Artur S d’Avila Garcez, Krysia B Broda, and Dov M Gabbay · 2012
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ADADELTA: an adaptive learning rate method
Matthew D. Zeiler · 2012
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Attribute-based classification for zero-shot visual object categorization
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2013
Earlier work this paper cites.
Probabilistic sentential decision diagrams
Doga Kisa, Guy Van den Broeck, Arthur Choi, and Adnan Darwiche · 2014
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Fifty years of classification and regression trees
Wei-Yin Loh · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick · 2017
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
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Right for the right reasons: Training differentiable models by constraining their explanations
Andrew Slavin Ross, Michael C. Hughes, and Finale Doshi-Velez · 2017
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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 · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda 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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Explicit reasoning over end-to-end neural architectures for visual question answering
Somak Aditya, Yezhou Yang, and Chitta Baral · 2018
Earlier work this paper cites.
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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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
Cited alongside, same era.
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.
Deepproblog: Neural probabilistic logic programming
Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, and Luc De Raedt · 2018
Cited alongside, same era.
Transparency by design: Closing the gap between performance and interpretability in visual reasoning
David Mascharka, Philip Tran, Ryan Soklaski, and Arjun Majumdar · 2018
Cited alongside, same era.
Beyond word importance: Contextual decomposition to extract interactions from lstms
W. James Murdoch, Peter J. Liu, and Bin Yu · 2018
Cited alongside, same era.
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2019
Later among the works it cites.
Enriching visual with verbal explanations for relational concepts–combining lime with aleph
Johannes Rabold, Hannah Deininger, Michael Siebers, and Ute Schmid · 2019
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Taking a HINT: leveraging explanations to make vision and language models more grounded
Ramprasaath Ramasamy Selvaraju, Stefan Lee, Yilin Shen, Hongxia Jin, Shalini Ghosh, Larry P. Heck, Dhruv Batra, and Devi Parikh · 2019
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Explanatory interactive machine learning
Stefano Teso and Kristian Kersting · 2019
Later among the works it cites.
Probabilistic neural-symbolic models for interpretable visual question answering
Ramakrishna Vedantam, Karan Desai, Stefan Lee, Marcus Rohrbach, Dhruv Batra, and D. Parikh · 2019
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The challenge of crafting intelligible intelligence
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Multimodal explanations: Justifying decisions and pointing to the evidence
Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Anna Rohrbach, Bernt Schiele, Trevor Darrell, and Marcus Rohrbach · 2018
Cited alongside, same era.
The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Philipp Tschandl, Cliff Rosendahl, and Harald Kittler · 2018
Cited alongside, same era.
A semantic loss function for deep learning with symbolic knowledge
Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, and Guy Van den Broeck · 2018
Cited alongside, same era.
Neural-symbolic VQA: disentangling reasoning from vision and language understanding
Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, and Josh Tenenbaum · 2018
Cited alongside, same era.
Interpretable basis decomposition for visual explanation
Bolei Zhou, Yiyou Sun, David Bau, and Antonio Torralba · 2018
Cited alongside, same era.
Noel Codella, Veronica Rotemberg, Philipp Tschandl, M Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, et al · 2019
Cited alongside, same era.
Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning
Artur S. d’Avila Garcez, Marco Gori, Luís C. Lamb, Luciano Serafini, Michael Spranger, and Son N. Tran · 2019
Cited alongside, same era.
Daniel S. Weld and Gagan Bansal · 2019
Later among the works it cites.
Self-critical reasoning for robust visual question answering
Jialin Wu and Raymond J. Mooney · 2019
Later among the works it cites.
Deep set prediction networks
Yan Zhang, Jonathon S. Hare, and Adam Prügel-Bennett · 2019
Later among the works it cites.
Human-driven FOL explanations of deep learning
Gabriele Ciravegna, Francesco Giannini, Marco Gori, Marco Maggini, and Stefano Melacci · 2020
Closest in time.
CATER: A diagnostic dataset for compositional actions & temporal reasoning
Rohit Girdhar and Deva Ramanan · 2020
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Learning adaptive language interfaces through decomposition
Siddharth Karamcheti, Dorsa Sadigh, and Percy Liang · 2020
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Visual concept reasoning networks
Kim, Kim, and Bengio · 2020
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Concept bottleneck models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang · 2020
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Conditional set generation with transformers
Adam R Kosiorek, Hyunjik Kim, and Danilo J Rezende · 2020
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Object-centric learning with slot attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf · 2020
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Shaping visual representations with language for few-shot classification
Jesse Mu, Percy Liang, and Noah Goodman · 2020
Closest in time.
ESPRIT: explaining solutions to physical reasoning tasks
Nazneen Fatema Rajani, Rui Zhang, Yi Chern Tan, Stephan Zheng, Jeremy Weiss, Aadit Vyas, Abhijit Gupta, Caiming Xiong, Richard Socher, and Dragomir R. Radev · 2020
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Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
Laura Rieger, Chandan Singh, W James Murdoch, and Bin Yu · 2020
Closest in time.
Making deep neural networks right for the right scientific reasons by interacting with their explanations
Patrick Schramowski, Wolfgang Stammer, Stefano Teso, Anna Brugger, Franziska Herbert, Xiaoting Shao, Hans-Georg Luigs, Anne-Katrin Mahlein, and Kristian Kersting · 2020
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Restricting the flow: Information bottlenecks for attribution
Karl Schulz, Leon Sixt, Federico Tombari, and Tim Landgraf · 2020
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Explainable deep learning: A field guide for the uninitiated
Ning Xie, Gabrielle Ras, Marcel van Gerven, and Derek Doran · 2020
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