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In-context learning, the ability to adapt based on a few examples in the input prompt, is a ubiquitous feature of large language models (LLMs).
THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images
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Improving Generalization for Temporal Difference Learning: The Successor Representation
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A framework for mesencephalic dopamine systems based on predictive hebbian learning
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A neural substrate of prediction and reward
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Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David McAllester, Satinder Singh, and Yishay Mansour · 1999
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Discrete coding of reward probability and uncertainty by dopamine neurons
Christopher D Fiorillo, Philippe N Tobler, and Wolfram Schultz · 2003
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Representational similarity analysis-connecting the branches of systems neuroscience
Nikolaus Kriegeskorte, Marieke Mur, and Peter A Bandettini · 2008
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Model-Based Influences on Humans’ Choices and Striatal Prediction Errors
Nathaniel D. Daw, Samuel J. Gershman, Ben Seymour, Peter Dayan, and Raymond J. Dolan · 2011
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A selective role for dopamine in stimulus–reward learning
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Efficient bayes-adaptive reinforcement learning using sample-based search
Arthur Guez, David Silver, and Peter Dayan · 2012
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Neural representations of events arise from temporal community structure
Anna C. Schapiro, Timothy T. Rogers, Natalia I. Cordova, Nicholas B. Turk-Browne, and Matthew M. Botvinick · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Reinforcement learning in multidimensional environments relies on attention mechanisms
Yael Niv, Reka Daniel, Andra Geana, Samuel J Gershman, Yuan Chang Leong, Angela Radulescu, and Robert C Wilson · 2015
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When Does Model-Based Control Pay Off?
Wouter Kool, Fiery A. Cushman, and Samuel J. Gershman · 2016
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Deep successor reinforcement learning
Tejas D Kulkarni, Ardavan Saeedi, Simanta Gautam, and Samuel J Gershman · 2016
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Statistical learning of temporal community structure in the hippocampus
Anna C. Schapiro, Nicholas B. Turk-Browne, Kenneth A. Norman, and Matthew M. Botvinick · 2016
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Successor features for transfer in reinforcement learning
André Barreto, Will Dabney, Rémi Munos, Jonathan J Hunt, Tom Schaul, Hado P van Hasselt, and David Silver · 2017
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Eigenoption discovery through the deep successor representation
Marlos C Machado, Clemens Rosenbaum, Xiaoxiao Guo, Miao Liu, Gerald Tesauro, and Murray Campbell · 2017
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Emergent Abilities of Large Language Models, October 2022
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus · 2022
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Using cognitive psychology to understand GPT-3
Marcel Binz and Eric Schulz · 2023
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Towards Monosemanticity: Decomposing Language Models With Dictionary Learning
Trenton Bricken, Adly Templeton, Joshua Batson, Brian Chen, Adam Jermyn, Tom Conerly, Nick Turner, Cem Anil, Carson Denison, Amanda Askell, Robert Lasenby, Yifan Wu, Shauna Kravec, Nicholas Schiefer, Tim Maxwell, Nicholas Joseph, Zac Hatfield-Dodds, Alex Tamkin, Karina Nguyen, Brayden McLean, Josiah E Burke, Tristan Hume, Shan Carter, Tom Henighan, and Christopher Olah · 2023
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Meta-in-context learning in large language models
Julian Coda-Forno, Marcel Binz, Zeynep Akata, Matt Botvinick, Jane Wang, and Eric Schulz · 2023
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Sparse autoencoders find highly interpretable features in language models
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The hippocampus as a predictive map
Kimberly L Stachenfeld, Matthew M Botvinick, and Samuel J Gershman · 2017
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Exploration by random network distillation
Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov · 2018
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Rethinking dopamine as generalized prediction error
Matthew P H Gardner, Geoffrey Schoenbaum, and Samuel J Gershman · 2018
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The successor representation: its computational logic and neural substrates
Samuel J Gershman · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Reinforcement learning: An introduction, 2nd ed
Richard S. Sutton and Andrew G. Barto · 2018
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Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton · 2019
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Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, and Lee Sharkey · 2023
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Hippocampal spatio-predictive cognitive maps adaptively guide reward generalization
Mona M Garvert, Tankred Saanum, Eric Schulz, Nicolas W Schuck, and Christian F Doeller · 2023
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Finding neurons in a haystack: Case studies with sparse probing
Wes Gurnee, Neel Nanda, Matthew Pauly, Katherine Harvey, Dmitrii Troitskii, and Dimitris Bertsimas · 2023
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Transformers learn in-context by gradient descent
Johannes Von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, and Max Vladymyrov · 2023
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Transformers learn to implement preconditioned gradient descent for in-context learning
Kwangjun Ahn, Xiang Cheng, Hadi Daneshmand, and Suvrit Sra · 2024
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In-context language learning: Arhitectures and algorithms
Ekin Akyürek, Bailin Wang, Yoon Kim, and Jacob Andreas · 2024
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
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Scaling and evaluating sparse autoencoders
Leo Gao, Tom Dupré la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, and Jeffrey Wu · 2024
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Large language models are biased reinforcement learners
William M Hayes, Nicolas Yax, and Stefano Palminteri · 2024
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Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2
Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Nicolas Sonnerat, Vikrant Varma, János Kramár, Anca Dragan, Rohin Shah, and Neel Nanda · 2024
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Reinforcement learning with simple sequence priors
Tankred Saanum, Noémi Éltető, Peter Dayan, Marcel Binz, and Eric Schulz · 2024
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In-context learning agents are asymmetric belief updaters
Johannes A Schubert, Akshay K Jagadish, Marcel Binz, and Eric Schulz · 2024
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Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao · 2024
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Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet
Adly Templeton · 2024
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Transformers learn temporal difference methods for in-context reinforcement learning
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Decoding in-context learning: Neuroscience-inspired analysis of representations in large language models, 2024
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A map of abstract relational knowledge in the human hippocampal–entorhinal cortex
Mona M Garvert, Raymond J Dolan, and Timothy EJ Behrens · 2050
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