Jane X. Wang

According to our database, Jane X. Wang authored at least 30 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

CogBench: a large language model walks into a psychology lab.

Julian Coda-Forno, Marcel Binz, Jane X. Wang, Eric Schulz

CoRR, 2024

2023

A social path to human-like artificial intelligence.

Edgar A. Duéñez-Guzmán, Suzanne Sadedin, Jane X. Wang, Kevin R. McKee, Joel Z. Leibo

Nat. Mac. Intell., October, 2023

Meta-Learned Models of Cognition.

Marcel Binz, Ishita Dasgupta, Akshay Jagadish, Matthew M. Botvinick, Jane X. Wang, Eric Schulz

CoRR, 2023

DiscoGen: Learning to Discover Gene Regulatory Networks.

Nan Rosemary Ke, Sara-Jane Dunn, Jörg Bornschein, Silvia Chiappa, Mélanie Rey, Jean-Baptiste Lespiau, Albin Cassirer, Jane X. Wang, Theophane Weber, David G. T. Barrett, Matthew M. Botvinick, Anirudh Goyal, Michael Mozer, Danilo J. Rezende

CoRR, 2023

Passive learning of active causal strategies in agents and language models.

Andrew K. Lampinen, Stephanie C. Y. Chan, Ishita Dasgupta, Andrew J. Nam, Jane X. Wang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Meta-in-context learning in large language models.

Julian Coda-Forno, Marcel Binz, Zeynep Akata, Matt M. Botvinick, Jane X. Wang, Eric Schulz

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning to Induce Causal Structure.

Nan Rosemary Ke, Silvia Chiappa, Jane X. Wang, Jörg Bornschein, Anirudh Goyal, Mélanie Rey, Theophane Weber, Matthew M. Botvinick, Michael Curtis Mozer, Danilo Jimenez Rezende

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Beyond Bayes-optimality: meta-learning what you know you don't know.

Jordi Grau-Moya, Grégoire Delétang, Markus Kunesch, Tim Genewein, Elliot Catt, Kevin Li, Anian Ruoss, Chris Cundy, Joel Veness, Jane X. Wang, Marcus Hutter, Christopher Summerfield, Shane Legg, Pedro A. Ortega

CoRR, 2022

Learning to Induce Causal Structure.

Nan Rosemary Ke, Silvia Chiappa, Jane Wang, Jörg Bornschein, Theophane Weber, Anirudh Goyal, Matthew M. Botvinick, Michael Mozer, Danilo Jimenez Rezende

CoRR, 2022

Semantic Exploration from Language Abstractions and Pretrained Representations.

Allison C. Tam, Neil C. Rabinowitz, Andrew K. Lampinen, Nicholas A. Roy, Stephanie C. Y. Chan, DJ Strouse, Jane Wang, Andrea Banino, Felix Hill

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Data Distributional Properties Drive Emergent In-Context Learning in Transformers.

Stephanie C. Y. Chan, Adam Santoro, Andrew K. Lampinen, Jane X. Wang, Aaditya K. Singh, Pierre H. Richemond, James L. McClelland, Felix Hill

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Tell me why! Explanations support learning relational and causal structure.

Andrew K. Lampinen, Nicholas A. Roy, Ishita Dasgupta, Stephanie C. Y. Chan, Allison C. Tam, James L. McClelland, Chen Yan, Adam Santoro, Neil C. Rabinowitz, Jane X. Wang, Felix Hill

Proceedings of the International Conference on Machine Learning, 2022

Can language models learn from explanations in context?

Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, Kory W. Mathewson, Michael Henry Tessler, Antonia Creswell, James L. McClelland, Jane Wang, Felix Hill

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

2021

Alchemy: A structured task distribution for meta-reinforcement learning.

Jane X. Wang, Michael King, Nicolas Porcel, Zeb Kurth-Nelson, Tina Zhu, Charlie Deck, Peter Choy, Mary Cassin, Malcolm Reynolds, H. Francis Song, Gavin Buttimore, David P. Reichert, Neil C. Rabinowitz, Loic Matthey, Demis Hassabis, Alexander Lerchner, Matthew M. Botvinick

CoRR, 2021

Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents.

Jane Wang, Michael King, Nicolas Porcel, Zeb Kurth-Nelson, Tina Zhu, Charles Deck, Peter Choy, Mary Cassin, Malcolm Reynolds, H. Francis Song, Gavin Buttimore, David P. Reichert, Neil C. Rabinowitz, Loic Matthey, Demis Hassabis, Alexander Lerchner, Matt M. Botvinick

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

2020

Meta-learning in natural and artificial intelligence.

Jane X. Wang

CoRR, 2020

Temporal Difference Uncertainties as a Signal for Exploration.

Sebastian Flennerhag, Jane X. Wang, Pablo Sprechmann, Francesco Visin, Alexandre Galashov, Steven Kapturowski, Diana L. Borsa, Nicolas Heess, André Barreto, Razvan Pascanu

CoRR, 2020

Amortized learning of neural causal representations.

Nan Rosemary Ke, Jane X. Wang, Jovana Mitrovic, Martin Szummer, Danilo J. Rezende

CoRR, 2020

Deep Reinforcement Learning and its Neuroscientific Implications.

Matthew M. Botvinick, Jane X. Wang, Will Dabney, Kevin J. Miller, Zeb Kurth-Nelson

CoRR, 2020

Causally Correct Partial Models for Reinforcement Learning.

Danilo J. Rezende, Ivo Danihelka, George Papamakarios, Nan Rosemary Ke, Ray Jiang, Theophane Weber, Karol Gregor, Hamza Merzic, Fabio Viola, Jane Wang, Jovana Mitrovic, Frederic Besse, Ioannis Antonoglou, Lars Buesing

CoRR, 2020

2019

Structural and Functional MRI Evidence for Distinct Medial Temporal and Prefrontal Roles in Context-dependent Relational Memory.

Hillary D. Schwarb, Curtis L. Johnson, Michael R. Dulas, Matthew D. J. McGarry, Joseph L. Holtrop, Patrick D. Watson, Jane X. Wang, Joel L. Voss, Bradley P. Sutton, Neal J. Cohen

J. Cogn. Neurosci., 2019

Meta-learning of Sequential Strategies.

Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alexander Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin J. Miller, Mohammad Gheshlaghi Azar, Ian Osband, Neil C. Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew M. Botvinick, Shane Legg

CoRR, 2019

Causal Reasoning from Meta-reinforcement Learning.

Ishita Dasgupta, Jane X. Wang, Silvia Chiappa, Jovana Mitrovic, Pedro A. Ortega, David Raposo, Edward Hughes, Peter W. Battaglia, Matthew M. Botvinick, Zeb Kurth-Nelson

CoRR, 2019

Evolving Intrinsic Motivations for Altruistic Behavior.

Jane X. Wang, Edward Hughes, Chrisantha Fernando, Wojciech M. Czarnecki, Edgar A. Duéñez-Guzmán, Joel Z. Leibo

Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

2018

Been There, Done That: Meta-Learning with Episodic Recall.

Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson, Siddhant M. Jayakumar, Charles Blundell, Razvan Pascanu, Matthew M. Botvinick

Proceedings of the 35th International Conference on Machine Learning, 2018

Meta-learning by the Baldwin effect.

Chrisantha Fernando, Jakub Sygnowski, Simon Osindero, Jane Wang, Tom Schaul, Denis Teplyashin, Pablo Sprechmann, Alexander Pritzel, Andrei A. Rusu

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018

Episodic Control through Meta-Reinforcement Learning.

Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson, Matthew M. Botvinick

Proceedings of the 40th Annual Meeting of the Cognitive Science Society, 2018

2017

Learning to reinforcement learn.

Jane Wang, Zeb Kurth-Nelson, Hubert Soyer, Joel Z. Leibo, Dhruva Tirumala, Rémi Munos, Charles Blundell, Dharshan Kumaran, Matt M. Botvinick

Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017

2014

A high-reproducibility and high-accuracy method for automated topic classification.

Andrea Lancichinetti, M. Irmak Sirer, Jane X. Wang, Daniel E. Acuna, Konrad P. Körding, Luís A. Nunes Amaral

CoRR, 2014

2012

Interactions of Excitatory and Inhibitory Feedback Topologies in Facilitating Pattern Separation and Retrieval.

Jane X. Wang, Michal Zochowski

Neural Comput., 2012