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The Behavioral and Brain Sciences|September 23, 2024
The hard problem of meta-learning is what-to-learnYosef Prat, Ehud LammThe Behavioral and Brain Sciences|September 23, 2024
Meta-learned models beyond and beneath the cognitiveMihnea MoldoveanuThe Behavioral and Brain Sciences|September 23, 2024
Meta-learning: Data, architecture, and bothMarcel Binz, Ishita Dasgupta, Akshay Jagadish, et al.The Behavioral and Brain Sciences|September 23, 2024
Bayes beyond the predictive distributionAnna Székely, Gergő OrbánThe Behavioral and Brain Sciences|September 23, 2024
Combining meta-learned models with process models of cognitionAdam N Sanborn, Haijiang Yan, Christian TsvetkovThe Behavioral and Brain Sciences|September 23, 2024
Meta-learning as a bridge between neural networks and symbolic Bayesian modelsR Thomas McCoy, Thomas L GriffithsThe Behavioral and Brain Sciences|September 23, 2024
Learning and memory are inextricableSue LlewellynThe Behavioral and Brain Sciences|September 23, 2024
The reinforcement metalearner as a biologically plausible meta-learning frameworkTim Vriens, Mattias Horan, Jacqueline Gottlieb, et al.The Behavioral and Brain Sciences|September 23, 2024
Linking meta-learning to meta-structureMalte Schilling, Helge J Ritter, Frank W OhlThe Behavioral and Brain Sciences|September 23, 2024
The meta-learning toolkit needs stronger constraintsErin GrantPageof 459