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Aligning model representations to humans has been found to improve robustness and generalization.
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Using “annotator rationales” to improve machine learning for text categorization
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The influence of categories on perception: explaining the perceptual magnet effect as optimal statistical inference
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Jonathan R. Folstein, Isabel Gauthier, and Thomas J. Palmeri · 2012
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Category learning increases discriminability of relevant object dimensions in visual cortex
Jonathan R Folstein, Thomas J Palmeri, and Isabel Gauthier · 2013
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Learning classification models with soft-label information
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Understanding malicious behavior in crowdsourcing platforms: The case of online surveys
Ujwal Gadiraju, Ricardo Kawase, Stefan Dietze, and Gianluca Demartini · 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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Mastering the game of Go with deep neural networks and tree search
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Iterative machine teaching
Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Chen Yu, Linda B Smith, James M Rehg, and Le Song · 2017
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Learning from between-class examples for deep sound recognition
Yuji Tokozume, Yoshitaka Ushiku, and Tatsuya Harada · 2017
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How does mixup help with robustness and generalization?
Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, and James Zou · 2020
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Diffusion models beat GANs on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Zeyad Emam, Andrew Kondrich, Sasha Harrison, Felix Lau, Yushi Wang, Aerin Kim, and Elliot Branson · 2021
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Mutual information and categorical perception
Jacob Feldman · 2021
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Explaining the efficacy of counterfactually-augmented data
Divyansh Kaushik, Amrith Setlur, Eduard H. Hovy, and Zachary C. Lipton · 2021
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Exploring alignment of representations with human perception
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Perceptual dominance in brief presentations of mixed images: Human perception vs. deep neural networks
Liron Z Gruber, Aia Haruvi, Ronen Basri, and Michal Irani · 2018
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Prolific. ac—a subject pool for online experiments
Stefan Palan and Christian Schitter · 2018
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Between-class learning for image classification
Yuji Tokozume, Yoshitaka Ushiku, and Tatsuya Harada · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz · 2018
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Efficient elicitation approaches to estimate collective crowd answers
John Joon Young Chung, Jean Y. Song, Sindhu Kutty, Sungsoo (Ray) Hong, Juho Kim, and Walter S. Lasecki · 2019
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Shape discrimination along morph-spaces
Nathan Destler, Manish Singh, and Jacob Feldman · 2019
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Augmix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2019
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Vedant Nanda, Ayan Majumdar, Camila Kolling, John P Dickerson, Krishna P Gummadi, Bradley C Love, and Adrian Weller · 2021
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Do humans trust advice more if it comes from AI? an analysis of human-ai interactions
Kailas Vodrahalli, Tobias Gerstenberg, and James Zou · 2021
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Datasetgan: Efficient labeled data factory with minimal human effort
Yuxuan Zhang, Huan Ling, Jun Gao, Kangxue Yin, Jean-Francois Lafleche, Adela Barriuso, Antonio Torralba, and Sanja Fidler · 2021
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Perspectives on incorporating expert feedback into model updates
Valerie Chen, Umang Bhatt, Hoda Heidari, Adrian Weller, and Ameet Talwalkar · 2022
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Next-generation deep learning based on simulators and synthetic data
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Harmonizing the object recognition strategies of deep neural networks with humans
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Synthetic data–what, why and how?
James Jordon, Lukasz Szpruch, Florimond Houssiau, Mirko Bottarelli, Giovanni Cherubin, Carsten Maple, Samuel N Cohen, and Adrian Weller · 2022
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Iterative teaching by data hallucination
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Ambiguous images with human judgments for robust visual event classification
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Genlabel: Mixup relabeling using generative models
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How transparency modulates trust in artificial intelligence
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When and how mixup improves calibration
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Alignment with human representations supports robust few-shot learning
Ilia Sucholutsky and Thomas L Griffiths · 2023
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On the informativeness of supervision signals
Ilia Sucholutsky, Ruairidh M. Battleday, Katherine M. Collins, Raja Marjieh, Joshua Peterson, Pulkit Singh, Umang Bhatt, Nori Jacoby, Adrian Weller, and Thomas L. Griffiths · 2023
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