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Although much research has been done on proposing new models or loss functions to improve the generalisation of artificial neural networks (ANNs), less attention has been directed to the impact of the training data on generalisation.
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Cumulative cultural evolution in the laboratory: An experimental approach to the origins of structure in human language
Simon Kirby, Hannah Cornish, and Kenny Smith · 2008
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Learning multiple layers of features from tiny images
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Burr Settles · 2012
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Iterated learning and the evolution of language
Simon Kirby, Tom Griffiths, and Kenny Smith · 2014
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Deep residual learning for image recognition
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Mastering the game of go with deep neural networks and tree search
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 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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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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An empirical study of example forgetting during deep neural network learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J Gordon · 2018
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Wide neural networks of any depth evolve as linear models under gradient descent
Jaehoon Lee, Lechao Xiao, Samuel Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington · 2019
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Grad-match: Gradient matching based data subset selection for efficient deep model training
Krishnateja Killamsetty, S Durga, Ganesh Ramakrishnan, Abir De, and Rishabh Iyer · 2021
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Dataset distillation with infinitely wide convolutional networks
Timothy Nguyen, Roman Novak, Lechao Xiao, and Jaehoon Lee · 2021
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Deep learning on a data diet: Finding important examples early in training
Mansheej Paul, Surya Ganguli, and Gintare Karolina Dziugaite · 2021
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Iterated learning for emergent systematicity in {vqa}
Ankit Vani, Max Schwarzer, Yuchen Lu, Eeshan Dhekane, and Aaron Courville · 2021
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Deep active learning by leveraging training dynamics
Haonan Wang, Wei Huang, Andrew Margenot, Hanghang Tong, and Jingrui He · 2021
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Wide feedforward or recurrent neural networks of any architecture are gaussian processes
Greg Yang · 2019
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Does learning require memorization? a short tale about a long tail
Vitaly Feldman · 2020
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The emergence of compositional languages for numeric concepts through iterated learning in neural agents
Shangmin Guo, Yi Ren, Serhii Havrylov, Stella Frank, Ivan Titov, and Kenny Smith · 2020
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Countering language drift with seeded iterated learning
Yuchen Lu, Soumye Singhal, Florian Strub, Aaron Courville, and Olivier Pietquin · 2020
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Dataset meta-learning from kernel ridge-regression
Timothy Nguyen, Zhourong Chen, and Jaehoon Lee · 2020
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Towards nngp-guided neural architecture search
Daniel S Park, Jaehoon Lee, Daiyi Peng, Yuan Cao, and Jascha Sohl-Dickstein · 2020
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Tensor programs iib: Architectural universality of neural tangent kernel training dynamics
Greg Yang and Etai Littwin · 2021
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Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2021
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Meta-learning with neural tangent kernels
Yufan Zhou, Zhenyi Wang, Jiayi Xian, Changyou Chen, and Jinhui Xu · 2021
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Deepcore: A comprehensive library for coreset selection in deep learning
Chengcheng Guo, Bo Zhao, and Yanbing Bai · 2022
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A framework and benchmark for deep batch active learning for regression
David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart · 2022
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A fast, well-founded approximation to the empirical neural tangent kernel
Mohamad Amin Mohamadi and Danica J Sutherland · 2022
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Multi-label iterated learning for image classification with label ambiguity
Sai Rajeswar, Pau Rodriguez, Soumye Singhal, David Vazquez, and Aaron Courville · 2022
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Better supervisory signals by observing learning paths
Yi Ren, Shangmin Guo, and Danica J. Sutherland · 2022
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Beyond neural scaling laws: beating power law scaling via data pruning
Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari S Morcos · 2022
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Lethal dose conjecture on data poisoning
Wenxiao Wang, Alexander Levine, and Soheil Feizi · 2022
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A note on exponential inequality
Wei Xiong · 2022
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