Interactions of Excitatory and Inhibitory Feedback Topologies in Facilitating Pattern Separation and Retrieval. - Jane X. Wang; Michal Zochowski (Neural Comput., 2012)

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)

Learning to reinforcement learn. - Jane Wang 001; Zeb Kurth-Nelson; Hubert Soyer; Joel Z. Leibo; Dhruva Tirumala; Rémi Munos; Charles Blundell; Dharshan Kumaran; Matt M. Botvinick (CogSci, 2017)

Episodic Control through Meta-Reinforcement Learning. - Samuel Ritter; Jane X. Wang; Zeb Kurth-Nelson; Matthew M. Botvinick (CogSci, 2018)

Meta-learning by the baldwin effect. - Chrisantha Fernando; Jakub Sygnowski; Simon Osindero; Jane Wang 001; Tom Schaul; Denis Teplyashin; Pablo Sprechmann; Alexander Pritzel; Andrei A. Rusu (GECCO (Companion), 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 (ICML, 2018)

Evolving Intrinsic Motivations for Altruistic Behavior. - Jane X. Wang; Edward Hughes 001; Chrisantha Fernando; Wojciech M. Czarnecki; Edgar A. Duéñez-Guzmán; Joel Z. Leibo (AAMAS, 2019)

Causal Reasoning from Meta-reinforcement Learning. - Ishita Dasgupta 001; Jane X. Wang; Silvia Chiappa; Jovana Mitrovic; Pedro A. Ortega; David Raposo; Edward Hughes 001; Peter W. Battaglia; Matthew M. Botvinick; Zeb Kurth-Nelson (CoRR, 2019)

Meta-learning of Sequential Strategies. - Pedro A. Ortega; Jane X. Wang; Mark Rowland 001; Tim Genewein; Zeb Kurth-Nelson; Razvan Pascanu; Nicolas Heess; Joel Veness; Alexander Pritzel; Pablo Sprechmann; Siddhant M. Jayakumar; Tom McGrath; et al. (CoRR, 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)

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 001; Jovana Mitrovic; Frederic Besse; et al. (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)

Amortized learning of neural causal representations. - Nan Rosemary Ke; Jane X. Wang; Jovana Mitrovic; Martin Szummer; Danilo J. Rezende (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 001; Razvan Pascanu (CoRR, 2020)

Meta-learning in natural and artificial intelligence. - Jane X. Wang (CoRR, 2020)

Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents. - Jane Wang 001; 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; et al. (NeurIPS Datasets and Benchmarks, 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; et al. (CoRR, 2021)

Can language models learn from explanations in context? - Andrew K. Lampinen; Ishita Dasgupta 001; Stephanie C. Y. Chan; Kory W. Mathewson; Michael Henry Tessler; Antonia Creswell; James L. McClelland; Jane Wang 001; Felix Hill (EMNLP (Findings), 2022)

Tell me why! Explanations support learning relational and causal structure. - Andrew K. Lampinen; Nicholas A. Roy; Ishita Dasgupta 001; Stephanie C. Y. Chan; Allison C. Tam; James L. McClelland; Chen Yan; Adam Santoro; Neil C. Rabinowitz; Jane X. Wang; Felix Hill (ICML, 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 (NeurIPS, 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 001; Andrea Banino; Felix Hill (NeurIPS, 2022)

Learning to Induce Causal Structure. - Nan Rosemary Ke; Silvia Chiappa; Jane Wang 001; Jörg Bornschein; Theophane Weber; Anirudh Goyal; Matthew M. Botvinick; Michael Mozer; Danilo Jimenez Rezende (CoRR, 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; et al. (CoRR, 2022)

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 (ICLR, 2023)

Meta-in-context learning in large language models. - Julian Coda-Forno; Marcel Binz; Zeynep Akata; Matt M. Botvinick; Jane X. Wang; Eric Schulz (NeurIPS, 2023)

Passive learning of active causal strategies in agents and language models. - Andrew K. Lampinen; Stephanie C. Y. Chan; Ishita Dasgupta 001; Andrew J. Nam; Jane X. Wang (NeurIPS, 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; et al. (CoRR, 2023)

Meta-Learned Models of Cognition. - Marcel Binz; Ishita Dasgupta 001; Akshay K. Jagadish; Matthew M. Botvinick; Jane X. Wang; Eric Schulz (CoRR, 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., 2023)

CogBench: a large language model walks into a psychology lab. - Julian Coda-Forno; Marcel Binz; Jane X. Wang; Eric Schulz (ICML, 2024)

Scaling Instructable Agents Across Many Simulated Worlds. - SIMA Team; Maria Abi Raad; Arun Ahuja; Catarina Barros; Frederic Besse; Andrew Bolt; Adrian Bolton; Bethanie Brownfield; Gavin Buttimore; Max Cant; Sarah Chakera; Stephanie C. Y. Chan; et al. (CoRR, 2024)

Can foundation models actively gather information in interactive environments to test hypotheses? - Nan Rosemary Ke; Danny P. Sawyer; Hubert Soyer; Martin Engelcke; David P. Reichert; Drew A. Hudson; John Reid; Alexander Lerchner; Danilo Jimenez Rezende; Timothy P. Lillicrap; Michael Mozer; Jane X. Wang (CoRR, 2024)