Meta-Learning with Memory-Augmented Neural Networks. - Adam Santoro; Sergey Bartunov; Matthew M. Botvinick; Daan Wierstra; Timothy P. Lillicrap (ICML, 2016)

One-shot Learning with Memory-Augmented Neural Networks. - Adam Santoro; Sergey Bartunov; Matthew M. Botvinick; Daan Wierstra; Timothy P. Lillicrap (CoRR, 2016)

Discovering objects and their relations from entangled scene representations. - David Raposo; Adam Santoro; David G. T. Barrett; Razvan Pascanu; Tim Lillicrap; Peter W. Battaglia (ICLR (Workshop), 2017)

Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study. - Samuel Ritter; David G. T. Barrett; Adam Santoro; Matt M. Botvinick (ICML, 2017)

A simple neural network module for relational reasoning. - Adam Santoro; David Raposo; David G. T. Barrett; Mateusz Malinowski; Razvan Pascanu; Peter W. Battaglia; Tim Lillicrap (NIPS, 2017)

Generative Temporal Models with Memory. - Mevlana Gemici; Chia-Chun Hung; Adam Santoro; Greg Wayne; Shakir Mohamed; Danilo Jimenez Rezende; David Amos; Timothy P. Lillicrap (CoRR, 2017)

Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. - Matthew M. Botvinick; David G. T. Barrett; Peter W. Battaglia; Nando de Freitas; Dharshan Kumaran; Joel Z. Leibo; Tim Lillicrap; Joseph Modayil; S. Mohamed; Neil C. Rabinowitz; Danilo Jimenez Rezende; Adam Santoro; et al. (CoRR, 2017)

Learning Visual Question Answering by Bootstrapping Hard Attention. - Mateusz Malinowski; Carl Doersch; Adam Santoro; Peter W. Battaglia (ECCV (6), 2018)

Measuring abstract reasoning in neural networks. - Adam Santoro; Felix Hill; David G. T. Barrett; Ari S. Morcos; Timothy P. Lillicrap (ICML, 2018)

Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures. - Sergey Bartunov; Adam Santoro; Blake A. Richards; Luke Marris; Geoffrey E. Hinton; Timothy P. Lillicrap (NeurIPS, 2018)

Relational recurrent neural networks. - Adam Santoro; Ryan Faulkner 001; David Raposo; Jack W. Rae; Mike Chrzanowski; Theophane Weber; Daan Wierstra; Oriol Vinyals; Razvan Pascanu; Timothy P. Lillicrap (NeurIPS, 2018)

Unsupervised Predictive Memory in a Goal-Directed Agent. - Greg Wayne; Chia-Chun Hung; David Amos; Mehdi Mirza; Arun Ahuja; Agnieszka Grabska-Barwinska; Jack W. Rae; Piotr Mirowski; Joel Z. Leibo; Adam Santoro; Mevlana Gemici; Malcolm Reynolds; et al. (CoRR, 2018)

Relational inductive biases, deep learning, and graph networks. - Peter W. Battaglia; Jessica B. Hamrick; Victor Bapst; Alvaro Sanchez-Gonzalez; Vinícius Flores Zambaldi; Mateusz Malinowski; Andrea Tacchetti; David Raposo; Adam Santoro; Ryan Faulkner 001; Çaglar Gülçehre; H. Francis Song; et al. (CoRR, 2018)

Relational Deep Reinforcement Learning. - Vinícius Flores Zambaldi; David Raposo; Adam Santoro; Victor Bapst; Yujia Li 001; Igor Babuschkin; Karl Tuyls; David P. Reichert; Timothy P. Lillicrap; Edward Lockhart; Murray Shanahan; Victoria Langston; et al. (CoRR, 2018)

Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures. - Sergey Bartunov; Adam Santoro; Blake A. Richards; Geoffrey E. Hinton; Timothy P. Lillicrap (CoRR, 2018)

Hyperbolic Attention Networks. - Çaglar Gülçehre; Misha Denil; Mateusz Malinowski; Ali Razavi; Razvan Pascanu; Karl Moritz Hermann; Peter W. Battaglia; Victor Bapst; David Raposo; Adam Santoro; Nando de Freitas (ICLR (Poster), 2019)

Learning to Make Analogies by Contrasting Abstract Relational Structure. - Felix Hill; Adam Santoro; David G. T. Barrett; Ari S. Morcos; Timothy P. Lillicrap (ICLR (Poster), 2019)

Deep reinforcement learning with relational inductive biases. - Vinícius Flores Zambaldi; David Raposo; Adam Santoro; Victor Bapst; Yujia Li 001; Igor Babuschkin; Karl Tuyls; David P. Reichert; Timothy P. Lillicrap; Edward Lockhart; Murray Shanahan; Victoria Langston; et al. (ICLR (Poster), 2019)

An Investigation of Model-Free Planning. - Arthur Guez; Mehdi Mirza; Karol Gregor; Rishabh Kabra; Sébastien Racanière; Theophane Weber; David Raposo; Adam Santoro; Laurent Orseau; Tom Eccles; Greg Wayne; David Silver; et al. (ICML, 2019)

Is coding a relevant metaphor for building AI? A commentary on "Is coding a relevant metaphor for the brain?", by Romain Brette. - Adam Santoro; Felix Hill; David G. T. Barrett; David Raposo; Matthew M. Botvinick; Timothy P. Lillicrap (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)

Cognitive consequences of structured education in a connectionist model of analogical reasoning. - David Barrett 001; Felix Hill; Adam Santoro; Jay McClelland (CogSci, 2020)

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)

Imitating Interactive Intelligence. - Josh Abramson; Arun Ahuja; Arthur Brussee; Federico Carnevale; Mary Cassin; Stephen Clark; Andrew Dudzik; Petko Georgiev; Aurelia Guy; Tim Harley; Felix Hill; Alden Hung; et al. (CoRR, 2020)

Object-based attention for spatio-temporal reasoning: Outperforming neuro-symbolic models with flexible distributed architectures. - David Ding; Felix Hill; Adam Santoro; Matt M. Botvinick (CoRR, 2020)

Rapid Task-Solving in Novel Environments. - Samuel Ritter; Ryan Faulkner 001; Laurent Sartran; Adam Santoro; Matthew M. Botvinick; David Raposo (ICLR, 2021)

Attention over Learned Object Embeddings Enables Complex Visual Reasoning. - David Ding; Felix Hill; Adam Santoro; Malcolm Reynolds; Matt M. Botvinick (NeurIPS, 2021)

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

Synthetic Returns for Long-Term Credit Assignment. - David Raposo; Samuel Ritter; Adam Santoro; Greg Wayne; Theophane Weber; Matt M. Botvinick; Hado van Hasselt; H. Francis Song (CoRR, 2021)

Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning. - Josh Abramson; Arun Ahuja; Arthur Brussee; Federico Carnevale; Mary Cassin; Felix Fischer 004; Petko Georgiev; Alex Goldin; Tim Harley; Felix Hill; Peter Conway Humphreys; Alden Hung; et al. (CoRR, 2021)

A data-driven approach for learning to control computers. - Peter Conway Humphreys; David Raposo; Tobias Pohlen; Gregory Thornton; Rachita Chhaparia; Alistair Muldal; Josh Abramson; Petko Georgiev; Adam Santoro; Timothy P. Lillicrap (ICML, 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)

Intra-agent speech permits zero-shot task acquisition. - Chen Yan; Federico Carnevale; Petko Georgiev; Adam Santoro; Aurelia Guy; Alistair Muldal; Chia-Chun Hung; Josh Abramson; Timothy P. Lillicrap; Gregory Wayne (NeurIPS, 2022)

A data-driven approach for learning to control computers. - Peter Conway Humphreys; David Raposo; Toby Pohlen; Gregory Thornton; Rachita Chhaparia; Alistair Muldal; Josh Abramson; Petko Georgiev; Alex Goldin; Adam Santoro; Timothy P. Lillicrap (CoRR, 2022)

Evaluating Multimodal Interactive Agents. - Josh Abramson; Arun Ahuja; Federico Carnevale; Petko Georgiev; Alex Goldin; Alden Hung; Jessica Landon; Timothy P. Lillicrap; Alistair Muldal; Blake A. Richards; Adam Santoro; Tamara von Glehn; et al. (CoRR, 2022)

Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback. - Josh Abramson; Arun Ahuja; Federico Carnevale; Petko Georgiev; Alex Goldin; Alden Hung; Jessica Landon; Jirka Lhotka; Timothy P. Lillicrap; Alistair Muldal; George Powell; Adam Santoro; et al. (CoRR, 2022)

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)

Mixture-of-Depths: Dynamically allocating compute in transformer-based language models. - David Raposo; Samuel Ritter; Blake A. Richards; Timothy P. Lillicrap; Peter Conway Humphreys; Adam Santoro (CoRR, 2024)