Analogies Emerge from Learning Dyamics in Neural Networks. - Andrew K. Lampinen; Shaw Hsu; James L. McClelland (CogSci, 2017)

Improving image generative models with human interactions. - Andrew Kyle Lampinen; David So; Douglas Eck; Fred Bertsch (CoRR, 2017)

One-shot and few-shot learning of word embeddings. - Andrew K. Lampinen; James L. McClelland (CoRR, 2017)

An analytic theory of generalization dynamics and transfer learning in deep linear networks. - Andrew K. Lampinen; Surya Ganguli (ICLR (Poster), 2019)

Embedded Meta-Learning: Toward more flexible deep-learning models. - Andrew K. Lampinen; James L. McClelland (CoRR, 2019)

Automated curricula through setter-solver interactions. - Sébastien Racanière; Andrew K. Lampinen; Adam Santoro; David P. Reichert; Vlad Firoiu; Timothy P. Lillicrap (CoRR, 2019)

Emergent Systematic Generalization in a Situated Agent. - Felix Hill; Andrew K. Lampinen; Rosalia Schneider; Stephen Clark; Matthew M. Botvinick; James L. McClelland; Adam Santoro (CoRR, 2019)

Environmental drivers of systematicity and generalization in a situated agent. - Felix Hill; Andrew K. Lampinen; Rosalia Schneider; Stephen Clark; Matthew M. Botvinick; James L. McClelland; Adam Santoro (ICLR, 2020)

Automated curriculum generation through setter-solver interactions. - Sébastien Racanière; Andrew K. Lampinen; Adam Santoro; David P. Reichert; Vlad Firoiu; Timothy P. Lillicrap (ICLR, 2020)

What shapes feature representations? Exploring datasets, architectures, and training. - Katherine L. Hermann; Andrew K. Lampinen (NeurIPS, 2020)

Transforming task representations to allow deep learning models to perform novel tasks. - Andrew K. Lampinen; James L. McClelland (CoRR, 2020)

Transforming task representations to perform novel tasks. - Andrew K. Lampinen; James L. McClelland (Proc. Natl. Acad. Sci. USA, 2020)

Towards mental time travel: a hierarchical memory for reinforcement learning agents. - Andrew K. Lampinen; Stephanie C. Y. Chan; Andrea Banino; Felix Hill (NeurIPS, 2021)

Symbolic Behaviour in Artificial Intelligence. - Adam Santoro; Andrew K. Lampinen; Kory W. Mathewson; Timothy P. Lillicrap; David Raposo (CoRR, 2021)

Zipfian Environments for Reinforcement Learning. - Stephanie C. Y. Chan; Andrew Kyle Lampinen; Pierre Harvey Richemond; Felix Hill (CoLLAs, 2022)

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)

Know your audience: specializing grounded language models with the game of Dixit. - Aaditya K. Singh; David Ding; Andrew M. Saxe; Felix Hill; Andrew K. Lampinen (CoRR, 2022)

Language models show human-like content effects on reasoning. - Ishita Dasgupta 001; Andrew K. Lampinen; Stephanie C. Y. Chan; Antonia Creswell; Dharshan Kumaran; James L. McClelland; Felix Hill (CoRR, 2022)

Transformers generalize differently from information stored in context vs in weights. - Stephanie C. Y. Chan; Ishita Dasgupta 001; Junkyung Kim; Dharshan Kumaran; Andrew K. Lampinen; Felix Hill (CoRR, 2022)

Know your audience: specializing grounded language models with listener subtraction. - Aaditya K. Singh; David Ding; Andrew M. Saxe; Felix Hill; Andrew K. Lampinen (EACL, 2023)

Symbol tuning improves in-context learning in language models. - Jerry W. Wei; Le Hou; Andrew K. Lampinen; Xiangning Chen; Da Huang; Yi Tay; Xinyun Chen; Yifeng Lu; Denny Zhou; Tengyu Ma 001; Quoc V. Le (EMNLP, 2023)

Combining Behaviors with the Successor Features Keyboard. - Wilka Carvalho; Andre Saraiva 001; Angelos Filos; Andrew K. Lampinen; Loic Matthey; Richard L. Lewis; Honglak Lee; Satinder Singh 001; Danilo Jimenez Rezende; Daniel Zoran (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)

Improving neural network representations using human similarity judgments. - Lukas Muttenthaler; Lorenz Linhardt; Jonas Dippel; Robert A. Vandermeulen; Katherine L. Hermann; Andrew K. Lampinen; Simon Kornblith (NeurIPS, 2023)

Getting aligned on representational alignment. - Ilia Sucholutsky; Lukas Muttenthaler; Adrian Weller; Andi Peng; Andreea Bobu; Been Kim; Bradley C. Love; Erin Grant; Jascha Achterberg; Joshua B. Tenenbaum; Katherine M. Collins; Katherine L. Hermann; et al. (CoRR, 2023)

Feature-Attending Recurrent Modules for Generalization in Reinforcement Learning. - Wilka Carvalho; Andrew Kyle Lampinen; Kyriacos Nikiforou; Felix Hill; Murray Shanahan (Trans. Mach. Learn. Res., 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)

SODA: Bottleneck Diffusion Models for Representation Learning. - Drew A. Hudson; Daniel Zoran; Mateusz Malinowski; Andrew K. Lampinen; Andrew Jaegle; James L. McClelland; Loic Matthey; Felix Hill; Alexander Lerchner (CVPR, 2024)

Interpretability Illusions in the Generalization of Simplified Models. - Dan Friedman; Andrew Kyle Lampinen; Lucas Dixon; Danqi Chen 001; Asma Ghandeharioun (ICML, 2024)

Understanding Visual Feature Reliance through the Lens of Complexity. - Thomas Fel; Louis Béthune; Andrew K. Lampinen; Thomas Serre; Katherine L. Hermann (NeurIPS, 2024)

Can Language Models Handle Recursively Nested Grammatical Structures? A Case Study on Comparing Models and Humans. - Andrew K. Lampinen (Comput. Linguistics, 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)

Aligning Machine and Human Visual Representations across Abstraction Levels. - Lukas Muttenthaler; Klaus Greff; Frieda Born; Bernhard Spitzer; Simon Kornblith; Michael C. Mozer; Klaus-Robert Müller; Thomas Unterthiner; Andrew K. Lampinen (CoRR, 2024)

The broader spectrum of in-context learning. - Andrew Kyle Lampinen; Stephanie C. Y. Chan; Aaditya K. Singh; Murray Shanahan (CoRR, 2024)

Learned feature representations are biased by complexity, learning order, position, and more. - Andrew Kyle Lampinen; Stephanie C. Y. Chan; Katherine L. Hermann (Trans. Mach. Learn. Res., 2024)

Evaluating Spatial Understanding of Large Language Models. - Yutaro Yamada; Yihan Bao; Andrew Kyle Lampinen; Jungo Kasai; Ilker Yildirim (Trans. Mach. Learn. Res., 2024)

The in-context inductive biases of vision-language models differ across modalities. - Kelsey Allen; Ishita Dasgupta 001; Eliza Kosoy; Andrew K. Lampinen (CoRR, 2025)

Naturalistic Computational Cognitive Science: Towards generalizable models and theories that capture the full range of natural behavior. - Wilka Carvalho; Andrew K. Lampinen (CoRR, 2025)

Decoupling the components of geometric understanding in Vision Language Models. - Eliza Kosoy; Annya Dahmani; Andrew K. Lampinen; Iulia M. Comsa; Soojin Jeong; Ishita Dasgupta 001; Kelsey Allen (CoRR, 2025)

How do language models learn facts? Dynamics, curricula and hallucinations. - Nicolas Zucchet; Jörg Bornschein; Stephanie C. Y. Chan; Andrew K. Lampinen; Razvan Pascanu; Soham De (CoRR, 2025)

On the generalization of language models from in-context learning and finetuning: a controlled study. - Andrew K. Lampinen; Arslan Chaudhry; Stephanie C. Y. Chan; Cody Wild; Diane Wan; Alex Ku; Jörg Bornschein; Razvan Pascanu; Murray Shanahan; James L. McClelland (CoRR, 2025)

The emergence of sparse attention: impact of data distribution and benefits of repetition. - Nicolas Zucchet; Francesco D'Angelo; Andrew K. Lampinen; Stephanie C. Y. Chan (CoRR, 2025)

Distinct Computations Emerge From Compositional Curricula in In-Context Learning. - Jin Hwa Lee; Andrew K. Lampinen; Aaditya K. Singh; Andrew M. Saxe (CoRR, 2025)

Representation biases: will we achieve complete understanding by analyzing representations? - Andrew Kyle Lampinen; Stephanie C. Y. Chan; Yuxuan Li; Katherine L. Hermann (CoRR, 2025)