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Concept Bottleneck Models (CBMs) tackle the opacity of neural architectures by constructing and explaining their predictions using a set of high-level concepts.
Explaining classifiers with causal concept effect (CaCE)
Yash Goyal, Amir Feder, Uri Shalit, and Been Kim · 1907
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Explaining classifiers with causal concept effect (cace)
Yash Goyal, Amir Feder, Uri Shalit, and Been Kim · 1907
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Group collaboration in assessment: Multiple objectives, processes, and outcomes
Noreen M Webb · 1995
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A framework for behavioural cloning
Michael Bain and Claude Sammut · 1995
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Models, reasoning and inference
Judea Pearl et al · 2000
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Mass collaboration systems on the world-wide web
AnHai Doan, Raghu Ramakrishnan, and Alon Y Halevy · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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The caltech-ucsd birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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The MNIST database of handwritten digit images for machine learning research
Li Deng · 2012
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Understanding the exploding gradient problem
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Rectifier nonlinearities improve neural network acoustic models
Andrew L Maas, Awni Y Hannun, Andrew Y Ng, et al · 2013
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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Tensorflow: a system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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
Cited alongside, same era.
Misconception-driven feedback: Results from an experimental study
Luke Gusukuma, Austin Cory Bart, Dennis Kafura, and Jeremy Ernst · 2018
Cited alongside, same era.
Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks
Ruth Fong and Andrea Vedaldi · 2018
Cited alongside, same era.
Interpretability Beyond Feature Attribution: Quantitative Testing With Concept Activation Vectors (TCAV)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al · 2018
Cited alongside, same era.
Joint active feature acquisition and classification with variable-size set encoding
Hajin Shim, Sung Ju Hwang, and Eunho Yang · 2018
Cited alongside, same era.
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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Concept embedding models: Beyond the accuracy-explainability trade-off
Mateo Espinosa Zarlenga, Barbiero Pietro, Ciravegna Gabriele, Marra Giuseppe, Francesco Giannini, Michelangelo Diligenti, Shams Zohreh, Precioso Frederic, Stefano Melacci, Weller Adrian, et al · 2022
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Addressing leakage in concept bottleneck models
Marton Havasi, Sonali Parbhoo, and Finale Doshi-Velez · 2022
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Encoding concepts in graph neural networks
Lucie Charlotte Magister, Pietro Barbiero, Dmitry Kazhdan, Federico Siciliano, Gabriele Ciravegna, Fabrizio Silvestri, Mateja Jamnik, and Pietro Lio · 2022
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Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2018
Cited alongside, same era.
Towards robust interpretability with self-explaining neural networks
David Alvarez-Melis and Tommi S Jaakkola · 2018
Cited alongside, same era.
Towards automatic concept-based explanations
Amirata Ghorbani, James Wexler, James Zou, and Been Kim · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Concept bottleneck models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang · 2020
Cited alongside, same era.
Now you see me (cme): concept-based model extraction
Dmitry Kazhdan, Botty Dimanov, Mateja Jamnik, Pietro Liò, and Adrian Weller · 2020
Cited alongside, same era.
On completeness-aware concept-based explanations in deep neural networks
Chih-Kuan Yeh, Been Kim, Sercan Arik, Chun-Liang Li, Tomas Pfister, and Pradeep Ravikumar · 2020
Cited alongside, same era.
Kushal Chauhan, Rishabh Tiwari, Jan Freyberg, Pradeep Shenoy, and Krishnamurthy Dvijotham · 2022
Later among the works it cites.
Posterior matching for arbitrary conditioning
Ryan Strauss and Junier B Oliva · 2022
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Learning from uncertain concepts via test time interventions
Ivaxi Sheth, Aamer Abdul Rahman, Laya Rafiee Sevyeri, Mohammad Havaei, and Samira Ebrahimi Kahou · 2022
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Glancenets: Interpretable, leak-proof concept-based models
Emanuele Marconato, Andrea Passerini, and Stefano Teso · 2022
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A closer look at the intervention procedure of concept bottleneck models
Sungbin Shin, Yohan Jo, Sungsoo Ahn, and Namhoon Lee · 2023
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Towards robust metrics for concept representation evaluation
Mateo Espinosa Zarlenga, Pietro Barbiero, Zohreh Shams, Dmitry Kazhdan, Umang Bhatt, Adrian Weller, and Mateja Jamnik · 2023
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Selective concept models: Permitting stakeholder customisation at test-time
Matthew Barker, Katherine M Collins, Krishnamurthy Dvijotham, Adrian Weller, and Umang Bhatt · 2023
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Human uncertainty in concept-based ai systems
Katherine M. Collins, Matthew Barker, Mateo Espinosa Zarlenga, Naveen Raman, Umang Bhatt, Mateja Jamnik, Ilia Sucholutsky, Adrian Weller, and Krishnamurthy Dvijotham · 2023
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Post-hoc concept bottleneck models
Mert Yuksekgonul, Maggie Wang, and James Zou · 2023
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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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Probabilistic concept bottleneck models
Eunji Kim, Dahuin Jung, Sangha Park, Siwon Kim, and Sungroh Yoon · 2023
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