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Concept bottleneck models have been successfully used for explainable machine learning by encoding information within the model with a set of human-defined concepts.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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The structure and function of explanations
Tania Lombrozo · 2006
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Autonomous driving in urban environments: Boss and the urban challenge
Chris Urmson, Joshua Anhalt, Drew Bagnell, Christopher Baker, Robert Bittner, MN Clark, John Dolan, Dave Duggins, Tugrul Galatali, Chris Geyer, et al · 2008
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The DARPA urban challenge: autonomous vehicles in city traffic
Martin Buehler, Karl Iagnemma, and Sanjiv Singh · 2009
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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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Towards fully autonomous driving: Systems and algorithms
Jesse Levinson, Jake Askeland, Jan Becker, Jennifer Dolson, David Held, Soeren Kammel, J Zico Kolter, Dirk Langer, Oliver Pink, Vaughan Pratt, et al · 2011
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Explanation and abductive inference
Tania Lombrozo · 2012
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Deep residual learning for image recognition, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
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Generating visual explanations
Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, and Trevor Darrell · 2016
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Bridging nonlinearities and stochastic regularizers with gaussian error linear units
Dan Hendrycks and Kevin Gimpel · 2016
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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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Neural network modeling for steering control of an autonomous vehicle
Gowtham Garimella, Joseph Funke, Chuang Wang, and Marin Kobilarov · 2017
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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 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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End-to-end learning of driving models from large-scale video datasets
Huazhe Xu, Yang Gao, Fisher Yu, and Trevor Darrell · 2017
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End-to-end learning of driving models with surround-view cameras and route planners
Simon Hecker, Dengxin Dai, and Luc Van Gool · 2018
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Grounding visual explanations
Lisa Anne Hendricks, Ronghang Hu, Trevor Darrell, and Zeynep Akata · 2018
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Textual explanations for self-driving vehicles
Vivit: A video vision transformer
Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid · 2021
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Video transformer network
Daniel Neimark, Omri Bar, Maya Zohar, and Dotan Asselmann · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Vehicle trajectory prediction works, but not everywhere
Mohammadhossein Bahari, Saeed Saadatnejad, Ahmad Rahimi, Mohammad Shaverdikondori, Amir Hossein Shahidzadeh, Seyed-Mohsen Moosavi-Dezfooli, and Alexandre Alahi · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Jinkyu Kim, Anna Rohrbach, Trevor Darrell, John Canny, and Zeynep Akata · 2018
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A commute in data: The comma2k19 dataset
Harald Schafer, Eder Santana, Andrew Haden, and Riccardo Biasini · 2018
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Grounding human-to-vehicle advice for self-driving vehicles
Jinkyu Kim, Teruhisa Misu, Yi-Ting Chen, Ashish Tawari, and John Canny · 2019
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Neural network vehicle models for high-performance automated driving
Nathan A Spielberg, Matthew Brown, Nitin R Kapania, John C Kegelman, and J Christian Gerdes · 2019
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Deep object-centric policies for autonomous driving
Dequan Wang, Coline Devin, Qi-Zhi Cai, Fisher Yu, and Trevor Darrell · 2019
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Longformer: The long-document transformer
Iz Beltagy, Matthew E Peters, and Arman Cohan · 2020
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nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2020
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Fast autoregressive transformers meet rnns for personalized adaptive cruise control
Noveen Sachdeva, Ziran Wang, Kyungtae Han, Rohit Gupta, and Julian McAuley · 2022
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Concept bottleneck model with additional unsupervised concepts
Yoshihide Sawada and Keigo Nakamura · 2022
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Recurring the transformer for video action recognition
Jiewen Yang, Xingbo Dong, Liujun Liu, Chao Zhang, Jiajun Shen, and Dahai Yu · 2022
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Concept correlation and its effects on concept-based models
Lena Heidemann, Maureen Monnet, and Karsten Roscher · 2023
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Domain adaptive object detection for autonomous driving under foggy weather
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Drama: Joint risk localization and captioning in driving
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Ego-vehicle action recognition based on semi-supervised contrastive learning
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Label-free concept bottleneck models
Tuomas Oikarinen, Subhro Das, Lam M Nguyen, and Tsui-Wei Weng · 2023
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Robust and interpretable medical image classifiers via concept bottleneck models
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Learning concise and descriptive attributes for visual recognition, 2023
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