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In humans and animals, curriculum learning -- presenting data in a curated order - is critical to rapid learning and effective pedagogy.
The transfer of a discrimination along a continuum
Douglas H Lawrence · 1952
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Discrimination transfer along a pitch continuum
Robert A Baker and Stanley W Osgood · 1954
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Levels of processing versus transfer appropriate processing
C Donald Morris, John D Bransford, and Jeffery J Franks · 1977
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The effects of information order and learning mode on schema abstraction
Renee Elio and John Anderson · 1984
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Spin glass theory and beyond: An Introduction to the Replica Method and Its Applications
Marc Mézard, Giorgio Parisi, and Miguel Angel Virasoro · 1987
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From rote learning to system building: acquiring verb morphology in children and connectionist nets
Kim Plunkett, Virginia Marchman, and Steen Ladegaard Knudsen · 1991
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U-shaped learning and frequency effects in a multi-layered perception: Implications for child language acquisition
Kim Plunkett and Virginia Marchman · 1991
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Learning and development in neural networks: the importance of starting small
Jeffrey L. Elman · 1993
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Analytical solution of the off-equilibrium dynamics of a long-range spin-glass model
Leticia F Cugliandolo and Jorge Kurchan · 1993
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Learning by on-line gradient descent
Michael Biehl and Holm Schwarze · 1995
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Exact solution for on-line learning in multilayer neural networks
David Saad and Sara A Solla · 1995
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Teaching by Examples: Implications for the Process of Category Acquisition
Judith Avrahami, Yaakov Kareev, Yonatan Bogot, Ruth Caspi, Salomka Dunaevsky, and Sharon Lerner · 1997
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Task difficulty and the specificity of perceptual learning
Merav Ahissar and Shaul Hochstein · 1997
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Phase diagram of coupled glassy systems: A mean-field study
Silvio Franz and Giorgio Parisi · 1997
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Statistical mechanics of learning
Andreas Engel and Christian Van den Broeck · 2001
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The easy-to-hard effect in human (homo sapiens) and rat (rattus norvegicus) auditory identification
Estella H Liu, Eduardo Mercado III, Barbara A Church, and Itzel Orduña · 2008
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Flexible shaping: how learning in small steps helps
Kai A. Krueger and Peter Dayan · 2009
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky · 2009
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Minqi Jiang, Edward Grefenstette, and Tim Rocktäschel · 2010
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Conditioned reflexes: an investigation of the physiological activity of the cerebral cortex
P Ivan Pavlov · 2010
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Embodied attention and word learning by toddlers
Chen Yu and Linda B. Smith · 2012
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Evoked-potential changes following discrimination learning involving complex sounds
Itzel Orduña, Estella H Liu, Barbara A Church, Ann C Eddins, and Eduardo Mercado III · 2012
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When does fading enhance perceptual category learning?
Harold Pashler and Michael C. Mozer · 2013
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Temporal dynamics in auditory perceptual learning: impact of sequencing and incidental learning
Barbara A Church, Eduardo Mercado III, Matthew G Wisniewski, and Estella H Liu · 2013
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Improved classification of mammograms following idealized training
Tuning and jamming reduced to their minima
Miguel Ruiz-García, Andrea J Liu, and Eleni Katifori · 2019
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Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup
Sebastian Goldt, Madhu Advani, Andrew M Saxe, Florent Krzakala, and Lenka Zdeborová · 2019
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Passed & spurious: Descent algorithms and local minima in spiked matrix-tensor models
Stefano Sarao Mannelli, Florent Krzakala, Pierfrancesco Urbani, and Lenka Zdeborova · 2019
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Who is afraid of big bad minima? analysis of gradient-flow in spiked matrix-tensor models
Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, and Lenka Zdeborová · 2019
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The behavior of organisms: An experimental analysis
Burrhus Frederic Skinner · 2019
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Adam N. Hornsby and Bradley C. Love · 2014
Cited alongside, same era.
Curriculum learning of multiple tasks
Anastasia Pentina, Viktoriia Sharmanska, and Christoph H. Lampert · 2015
Cited alongside, same era.
Statistical physics of inference: Thresholds and algorithms
Lenka Zdeborová and Florent Krzakala · 2016
Cited alongside, same era.
Semeval-2016 task 10: Detecting minimal semantic units and their meanings (dimsum)
Nathan Schneider, Dirk Hovy, Anders Johannsen, and Marine Carpuat · 2016
Cited alongside, same era.
Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
Cited alongside, same era.
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, et al · 2017
Cited alongside, same era.
Curriculum Learning and Minibatch Bucketing in Neural Machine Translation
Tom Kocmi and Ondřej Bojar · 2017
Cited alongside, same era.
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J. Gordon · 2019
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Curriculum Learning for Domain Adaptation in Neural Machine Translation
Xuan Zhang, Pamela Shapiro, Gaurav Kumar, Paul McNamee, Marine Carpuat, and Kevin Duh · 2019
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How do infants start learning object names in a sea of clutter?
Hadar Karmazyn Raz, Drew H. Abney, David Crandall, Chen Yu, and Linda B. Smith · 2019
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When do curricula work?
Xiaoxia Wu, Ethan Dyer, and Behnam Neyshabur · 2020
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Language Models are Few-Shot Learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Theory of curriculum learning, with convex loss functions
Daphna Weinshall and Dan Amir · 2020
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Statistical mechanics of deep learning
Yasaman Bahri, Jonathan Kadmon, Jeffrey Pennington, Sam S Schoenholz, Jascha Sohl-Dickstein, and Surya Ganguli · 2020
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High-dimensional dynamics of generalization error in neural networks
M.S. Advani, A.M. Saxe, and H. Sompolinsky · 2020
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Complex dynamics in simple neural networks: Understanding gradient flow in phase retrieval
Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, Pierfrancesco Urbani, and Lenka Zdeborová · 2020
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Large deviations for the perceptron model and consequences for active learning
Hugo Cui, Luca Saglietti, and Lenka Zdeborová · 2020
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Solvable model for inheriting the regularization through knowledge distillation
Luca Saglietti and Lenka Zdeborová · 2020
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Standardized and reproducible measurement of decision-making in mice
The International Brain Laboratory, Valeria Aguillon-Rodriguez, Dora Angelaki, Hannah Bayer, Niccolo Bonacchi, Matteo Carandini, Fanny Cazettes, Gaelle Chapuis, Anne K Churchland, Yang Dan, Eric Dewitt, Mayo Faulkner, Hamish Forrest, Laura Haetzel, Michael Häusser, Sonja B Hofer, Fei Hu, Anup Khanal, Christopher Krasniak, Ines Laranjeira, Zachary F Mainen, Guido Meijer, Nathaniel J Miska, Thomas D Mrsic-Flogel, Masayoshi Murakami, Jean-Paul Noel, Alejandro Pan-Vazquez, Cyrille Rossant, Joshua Sanders, Karolina Socha, Rebecca Terry, Anne E Urai, Hernando Vergara, Miles Wells, Christian J Wilson, Ilana B Witten, Lauren E Wool, and Anthony M Zador · 2021
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Tilting the playing field: Dynamical loss functions for machine learning
Miguel Ruiz-Garcia, Ge Zhang, Samuel S Schoenholz, and Andrea J Liu · 2021
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Curriculum learning as a tool to uncover learning principles in the brain
Daniel R. Kepple, Rainer Engelken, and Rajan Kanaka · 2022
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