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Traditional tabular classifiers provide explainable decision-making with interpretable features(concepts).
Application of the logistic function to bio-assay
Joseph Berkson · 1944
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Mathematical methods of organizing and planning production
Leonid V Kantorovich · 1960
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Support vector machines
Marti A. Hearst, Susan T Dumais, Edgar Osuna, John Platt, and Bernhard Scholkopf · 1998
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Reducing the dimensionality of data with neural networks
Geoffrey E Hinton and Ruslan R Salakhutdinov · 2006
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Xgboost: A scalable tree boosting system
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Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
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Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville · 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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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
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Towards robust interpretability with self-explaining neural networks
David Alvarez Melis and Tommi Jaakkola · 2018
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Catboost: gradient boosting with categorical features support
Anna Veronika Dorogush, Vasily Ershov, and Andrey Gulin · 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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A guide for making black box models explainable
Christoph Molnar · 2018
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Large scale adversarial representation learning
Jeff Donahue and Karen Simonyan · 2019
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Towards automatic concept-based explanations
Amirata Ghorbani, James Wexler, James Y Zou, and Been Kim · 2019
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Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, et al · 2019
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Explanation by progressive exaggeration
Sumedha Singla, Brian Pollack, Junxiang Chen, and Kayhan Batmanghelich · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Cocox: Generating conceptual and counterfactual explanations via fault-lines
Restyle: A residual-based stylegan encoder via iterative refinement
Yuval Alaluf, Or Patashnik, and Daniel Cohen-Or · 2021
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Tabnet: Attentive interpretable tabular learning
Sercan Ö Arik and Tomas Pfister · 2021
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Deep neural networks and tabular data: A survey
Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, and Gjergji Kasneci · 2021
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Dissect: Disentangled simultaneous explanations via concept traversals
Asma Ghandeharioun, Been Kim, Chun-Liang Li, Brendan Jou, Brian Eoff, and Rosalind W Picard · 2021
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Explaining in style: Training a gan to explain a classifier in stylespace
Oran Lang, Yossi Gandelsman, Michal Yarom, Yoav Wald, Gal Elidan, Avinatan Hassidim, William T Freeman, Phillip Isola, Amir Globerson, Michal Irani, et al · 2021
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Arjun Akula, Shuai Wang, and Song-Chun Zhu · 2020
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Ganspace: Discovering interpretable gan controls
Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris · 2020
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Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 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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Adversarial latent autoencoders
Stanislav Pidhorskyi, Donald A Adjeroh, and Gianfranco Doretto · 2020
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Interfacegan: Interpreting the disentangled face representation learned by gans
Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou · 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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Encoding in style: a stylegan encoder for image-to-image translation
Elad Richardson, Yuval Alaluf, Or Patashnik, Yotam Nitzan, Yaniv Azar, Stav Shapiro, and Daniel Cohen-Or · 2021
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Designing an encoder for stylegan image manipulation
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Stylespace analysis: Disentangled controls for stylegan image generation
Zongze Wu, Dani Lischinski, and Eli Shechtman · 2021
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Generative hierarchical features from synthesizing images
Yinghao Xu, Yujun Shen, Jiapeng Zhu, Ceyuan Yang, and Bolei Zhou · 2021
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Hyperstyle: Stylegan inversion with hypernetworks for real image editing
Yuval Alaluf, Omer Tov, Ron Mokady, Rinon Gal, and Amit Bermano · 2022
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Large: Latent-based regression through gan semantics
Yotam Nitzan, Rinon Gal, Ofir Brenner, and Daniel Cohen-Or · 2022
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Pivotal tuning for latent-based editing of real images
Daniel Roich, Ron Mokady, Amit H Bermano, and Daniel Cohen-Or · 2022
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A framework for learning ante-hoc explainable models via concepts
Anirban Sarkar, Deepak Vijaykeerthy, Anindya Sarkar, and Vineeth N Balasubramanian · 2022
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