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Concept Bottleneck Models (CBMs) map the black-box visual representations extracted by deep neural networks onto a set of interpretable concepts and use the concepts to make predictions, enhancing the transparency of the decision-making process.
Automated flower classification over a large number of classes
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Learning multiple layers of features from tiny images
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Representation learning: A review and new perspectives
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Food-101–mining discriminative components with random forests
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Adam: A method for stochastic optimization
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Tiny imagenet visual recognition challenge
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Can we open the black box of ai?
Davide Castelvecchi · 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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Visual genome: Connecting language and vision using crowdsourced dense image annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al · 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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Opening the black box of deep neural networks via information
Ravid Shwartz-Ziv and Naftali Tishby · 2017
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Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi · 2017
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Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader, and Vineeth N Balasubramanian · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Grounding visual explanations
Lisa Anne Hendricks, Ronghang Hu, Trevor Darrell, and Zeynep Akata · 2018
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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, et al · 2018
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Deep learning for medical image processing: Overview, challenges and the future
Muhammad Imran Razzak, Saeeda Naz, and Ahmad Zaib · 2018
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A review of modularization techniques in artificial neural networks
Mohammed Amer and Tomás Maul · 2019
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Fine-grained visual-textual representation learning
Xiangteng He and Yuxin Peng · 2019
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Interpretability beyond classification output: Semantic bottleneck networks
Max Losch, Mario Fritz, and Bernt Schiele · 2019
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A large-scale attribute dataset for zero-shot learning
Bo Zhao, Yanwei Fu, Rui Liang, Jiahong Wu, Yonggang Wang, and Yizhou Wang · 2019
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Debiasing concept-based explanations with causal analysis
Mohammad Taha Bahadori and David E Heckerman · 2020
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What is the state of neural network pruning?
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag · 2020
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Concept embedding models: Beyond the accuracy-explainability trade-off
Mateo Espinosa Zarlenga, Pietro Barbiero, Gabriele Ciravegna, Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Zohreh Shams, Frederic Precioso, Stefano Melacci, Adrian Weller, et al · 2022
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Training data influence analysis and estimation: A survey
Zayd Hammoudeh and Daniel Lowd · 2022
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A survey on vision transformer
Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen, Jianyuan Guo, Zhenhua Liu, Yehui Tang, An Xiao, Chunjing Xu, Yixing Xu, et al · 2022
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Datamodels: Predicting predictions from training data
Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, and Aleksander Madry · 2022
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Fine-tuning can distort pretrained features and underperform out-of-distribution
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Amirata Ghorbani and James Y Zou · 2020
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Now you see me (cme): concept-based model extraction
Dmitry Kazhdan, Botty Dimanov, Mateja Jamnik, Pietro Liò, and Adrian Weller · 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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Deep learning for financial applications: A survey
Ahmet Murat Ozbayoglu, Mehmet Ugur Gudelek, and Omer Berat Sezer · 2020
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Dan Wang, Xinrui Cui, and Z Jane Wang · 2020
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Supermasks in superposition
Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, Jason Yosinski, and Ali Farhadi · 2020
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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
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Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, and Percy Liang · 2022
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Glancenets: Interpretable, leak-proof concept-based models
Emanuele Marconato, Andrea Passerini, and Stefano Teso · 2022
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Vikram V Ramaswamy, Sunnie SY Kim, Ruth Fong, and Olga Russakovsky · 2022
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Concept embedding analysis: A review
Gesina Schwalbe · 2022
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Post-hoc concept bottleneck models
Mert Yuksekgonul, Maggie Wang, and James Zou · 2022
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A survey of transformer-based multimodal pre-trained modals
Xue Han, Yi-Tong Wang, Jun-Lan Feng, Chao Deng, Zhan-Heng Chen, Yu-An Huang, Hui Su, Lun Hu, and Peng-Wei Hu · 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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Robust speech recognition via large-scale weak supervision
Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever · 2023
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Toward transparent ai: A survey on interpreting the inner structures of deep neural networks
Tilman Räuker, Anson Ho, Stephen Casper, and Dylan Hadfield-Menell · 2023
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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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Learning bottleneck concepts in image classification
Bowen Wang, Liangzhi Li, Yuta Nakashima, and Hajime Nagahara · 2023
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Discover and cure: Concept-aware mitigation of spurious correlation
Shirley Wu, Mert Yuksekgonul, Linjun Zhang, and James Zou · 2023
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Language in a bottle: Language model guided concept bottlenecks for interpretable image classification
Yue Yang, Artemis Panagopoulou, Shenghao Zhou, Daniel Jin, Chris Callison-Burch, and Mark Yatskar · 2023
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Assisting language learners: Automated trans-lingual definition generation via contrastive prompt learning
Hengyuan Zhang, Dawei Li, Yanran Li, Chenming Shang, Chufan Shi, and Yong Jiang · 2023
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Connecting large language models with evolutionary algorithms yields powerful prompt optimizers, 2024
Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, and Yujiu Yang · 2024
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Understanding multimodal deep neural networks: A concept selection view, 2024
Chenming Shang, Hengyuan Zhang, Hao Wen, and Yujiu Yang · 2024
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