Modeling Unsupervised Perceptual Category Learning. - Brenden M. Lake; Gautam K. Vallabha; James L. McClelland (IEEE Trans. Auton. Ment. Dev., 2009)

Estimating the strength of unlabeled information during semi-supervised learning. - Brenden M. Lake; James L. McClelland (CogSci, 2011)

One shot learning of simple visual concepts. - Brenden M. Lake; Ruslan Salakhutdinov; Jason Gross; Joshua B. Tenenbaum (CogSci, 2011)

Concept learning as motor program induction: A large-scale empirical study. - Brenden M. Lake; Ruslan Salakhutdinov; Joshua B. Tenenbaum (CogSci, 2012)

One-shot learning by inverting a compositional causal process. - Brenden M. Lake; Ruslan Salakhutdinov; Joshua B. Tenenbaum (NIPS, 2013)

Adaptive teaching: Improving the efficiency of learning through hypothesis-dependent selection of training data. - Patricia Angie Chan; Douglas Markant; Brenden M. Lake; Todd M. Gureckis (CogSci, 2014)

One-shot learning of generative speech concepts. - Brenden M. Lake; Chia-ying Lee; James R. Glass; Joshua B. Tenenbaum (CogSci, 2014)

Computational Creativity: Generating new objects with a hierarchical Bayesian model. - Brenden M. Lake; Josh Tenenbaum 001 (CogSci, 2014)

Deep Neural Networks Predict Category Typicality Ratings for Images. - Brenden M. Lake; Wojciech Zaremba; Rob Fergus; Todd M. Gureckis (CogSci, 2015)

Asking useful questions: Active learning with rich queries. - Anselm Rothe; Brenden M. Lake; Todd M. Gureckis (CogSci, 2015)

Softstar: Heuristic-Guided Probabilistic Inference. - Mathew Monfort; Brenden M. Lake; Brian D. Ziebart; Patrick Lucey; Joshua B. Tenenbaum (NIPS, 2015)

Searching large hypothesis spaces by asking questions. - Alexander Cohen; Brenden M. Lake (CogSci, 2016)

Asking and evaluating natural language questions. - Anselm Rothe; Brenden M. Lake; Todd M. Gureckis (CogSci, 2016)

The Emergence of Organizing Structure in Conceptual Representation. - Brenden M. Lake; Neil D. Lawrence; Joshua B. Tenenbaum (CoRR, 2016)

Building Machines That Learn and Think Like People. - Brenden M. Lake; Tomer D. Ullman; Joshua B. Tenenbaum; Samuel J. Gershman (CoRR, 2016)

One-shot Learning and Classification in Children. - Eliza Kosoy; Brenden M. Lake; Josh Tenenbaum 001 (CogSci, 2017)

Progress in building a machine that can ask interesting and informative questions. - Anselm Rothe; Brenden M. Lake; Todd M. Gureckis (CogSci, 2017)

Question Asking as Program Generation. - Anselm Rothe; Brenden M. Lake; Todd M. Gureckis (NIPS, 2017)

Still not systematic after all these years: On the compositional skills of sequence-to-sequence recurrent networks. - Brenden M. Lake; Marco Baroni (CoRR, 2017)

Learning Inductive Biases with Simple Neural Networks. - Reuben Feinman; Brenden M. Lake (CogSci, 2018)

Rearranging the Familiar: Testing Compositional Generalization in Recurrent Networks. - João Loula; Marco Baroni; Brenden M. Lake (BlackboxNLP@EMNLP, 2018)

Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks. - Brenden M. Lake; Marco Baroni (ICML, 2018)

Learning a smooth kernel regularizer for convolutional neural networks. - Reuben Feinman; Brenden M. Lake (CogSci, 2019)

Human few-shot learning of compositional instructions. - Brenden M. Lake; Tal Linzen; Marco Baroni (CogSci, 2019)

Asking goal-oriented questions and learning from answers. - Anselm Rothe; Brenden M. Lake; Todd M. Gureckis (CogSci, 2019)

Compositional generalization through meta sequence-to-sequence learning. - Brenden M. Lake (NeurIPS, 2019)

The Omniglot Challenge: A 3-Year Progress Report. - Brenden M. Lake; Ruslan Salakhutdinov; Joshua B. Tenenbaum (CoRR, 2019)

People infer recursive visual concepts from just a few examples. - Brenden M. Lake; Steven T. Piantadosi (CoRR, 2019)

Improving the robustness of ImageNet classifiers using elements of human visual cognition. - A. Emin Orhan; Brenden M. Lake (CoRR, 2019)

Mutual exclusivity as a challenge for neural networks. - Kanishk Gandhi; Brenden M. Lake (CoRR, 2019)

Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning. - Guy Davidson; Brenden M. Lake (CogSci, 2020)

Generating new concepts with hybrid neuro-symbolic models. - Reuben Feinman; Brenden M. Lake (CogSci, 2020)

Extending the Rogers and McClelland Model of Semantic Cognition (2003) to work with Raw Pixel Information. - Arihant Jain; Brenden M. Lake; Todd M. Gureckis (CogSci, 2020)

Learning word-referent mappings and concepts from raw inputs. - Wai Keen Vong; Brenden M. Lake (CogSci, 2020)

Mutual exclusivity as a challenge for deep neural networks. - Kanishk Gandhi; Brenden M. Lake (NeurIPS, 2020)

Learning Compositional Rules via Neural Program Synthesis. - Maxwell I. Nye; Armando Solar-Lezama; Josh Tenenbaum 001; Brenden M. Lake (NeurIPS, 2020)

Self-supervised learning through the eyes of a child. - A. Emin Orhan; Vaibhav V. Gupta; Brenden M. Lake (NeurIPS, 2020)

A Benchmark for Systematic Generalization in Grounded Language Understanding. - Laura Ruis; Jacob Andreas; Marco Baroni; Diane Bouchacourt; Brenden M. Lake (NeurIPS, 2020)

Word meaning in minds and machines. - Brenden M. Lake; Gregory L. Murphy (CoRR, 2020)

Examining Infant Relation Categorization Through Deep Neural Networks. - Guy Davidson; Brenden M. Lake (CogSci, 2021)

Fast and Flexible: Human program induction in abstract reasoning tasks. - Aysja Johnson; Wai Keen Vong; Brenden M. Lake; Todd M. Gureckis (CogSci, 2021)

The Omniglot Jr. challenge; Can a model achieve child-level character generation and classification? - Eliza Kosoy; Masha Belyi; Charlie Snell; Brenden M. Lake; Josh Tenenbaum 001; Alison Gopnik (CogSci, 2021)

Evaluating infants' reasoning about agents using the Baby Intuitions Benchmark (BIB). - Gala Stojnic; Kanishk Gandhi; Brenden M. Lake; Moira R. Dillon (CogSci, 2021)

Modeling artificial category learning from pixels: Revisiting Shepard, Hovland, and Jenkins (1961) with deep neural networks. - Alexa R. Tartaglini; Wai Keen Vong; Brenden M. Lake (CogSci, 2021)

Modeling Question Asking Using Neural Program Generation. - Ziyun Wang; Brenden M. Lake (CogSci, 2021)

Learning Task-General Representations with Generative Neuro-Symbolic Modeling. - Reuben Feinman; Brenden M. Lake (ICLR, 2021)

CURI: A Benchmark for Productive Concept Learning Under Uncertainty. - Ramakrishna Vedantam; Arthur Szlam; Maximilian Nickel; Ari Morcos; Brenden M. Lake (ICML, 2021)

Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others. - Kanishk Gandhi; Gala Stojnic; Brenden M. Lake; Moira R. Dillon (NeurIPS, 2021)

Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning. - Maxwell I. Nye; Michael Henry Tessler; Joshua B. Tenenbaum; Brenden M. Lake (NeurIPS, 2021)

Flexible Compositional Learning of Structured Visual Concepts. - Yanli Zhou; Brenden M. Lake (CoRR, 2021)

Creativity, Compositionality, and Common Sense in Human Goal Generation. - Guy Davidson; Todd M. Gureckis; Brenden M. Lake (CogSci, 2022)

Evaluating locality in NMT models. - Itay Itzhak; Koustuv Sinha; Brenden M. Lake; Adina Williams; Dieuwke Hupkes (CogSci, 2022)

Improving Systematic Generalization Through Modularity and Augmentation. - Laura Ruis; Brenden M. Lake (CogSci, 2022)

A Developmentally-Inspired Examination of Shape versus Texture Bias in Machines. - Alexa R. Tartaglini; Wai Keen Vong; Brenden M. Lake (CogSci, 2022)

Categorising images by generating natural language rules. - Wai Keen Vong; Brenden M. Lake (CogSci, 2022)

Cross-Situational Word Learning With Multimodal Neural Networks. - Wai Keen Vong; Brenden M. Lake (Cogn. Sci., 2022)

What can generic neural networks learn from a child's visual experience? - A. Emin Orhan; Brenden M. Lake (CoRR, 2023)

Compositional diversity in visual concept learning. - Yanli Zhou; Reuben Feinman; Brenden M. Lake (CoRR, 2023)

Deep Neural Networks Can Learn Generalizable Same-Different Visual Relations. - Alexa R. Tartaglini; Sheridan Feucht; Michael A. Lepori; Wai Keen Vong; Charles Lovering; Brenden M. Lake; Ellie Pavlick (CoRR, 2023)

Human-like systematic generalization through a meta-learning neural network. - Brenden M. Lake; Marco Baroni (Nat., 2023)

Beyond the Doors of Perception: Vision Transformers Represent Relations Between Objects. - Michael A. Lepori; Alexa R. Tartaglini; Wai Keen Vong; Thomas Serre; Brenden M. Lake; Ellie Pavlick (NeurIPS, 2024)

Self-supervised learning of video representations from a child's perspective. - A. Emin Orhan; Wentao Wang; Alex N. Wang; Mengye Ren; Brenden M. Lake (CoRR, 2024)

Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction. - Sreejan Kumar; Raja Marjieh; Byron Zhang; Declan Campbell; Michael Y. Hu; Umang Bhatt; Brenden M. Lake; Thomas L. Griffiths 001 (CoRR, 2024)

A systematic investigation of learnability from single child linguistic input. - Yulu Qin; Wentao Wang; Brenden M. Lake (CoRR, 2024)

Compositional learning of functions in humans and machines. - Yanli Zhou; Brenden M. Lake; Adina Williams (CoRR, 2024)

CoLLEGe: Concept Embedding Generation for Large Language Models. - Ryan Teehan; Brenden M. Lake; Mengye Ren (CoRR, 2024)

H-ARC: A Robust Estimate of Human Performance on the Abstraction and Reasoning Corpus Benchmark. - Solim LeGris; Wai Keen Vong; Brenden M. Lake; Todd M. Gureckis (CoRR, 2024)

Neural networks that overcome classic challenges through practice. - Kazuki Irie; Brenden M. Lake (CoRR, 2024)

Learning high-level visual representations from a child's perspective without strong inductive biases. - A. Emin Orhan; Brenden M. Lake (Nat. Mac. Intell., 2024)

Goals as reward-producing programs. - Guy Davidson; Graham Todd; Julian Togelius; Todd M. Gureckis; Brenden M. Lake (Nat. Mac. Intell., 2025)

Do Large Language Models Reason Causally Like Us? Even Better? - Hanna M. Dettki; Brenden M. Lake; Charley M. Wu; Bob Rehder (CoRR, 2025)

Rapid Word Learning Through Meta In-Context Learning. - Wentao Wang; Guangyuan Jiang; Tal Linzen; Brenden M. Lake (CoRR, 2025)

An explainable transformer circuit for compositional generalization. - Cheng Tang; Brenden M. Lake; Mehrdad Jazayeri (CoRR, 2025)

Do different prompting methods yield a common task representation in language models? - Guy Davidson; Todd M. Gureckis; Brenden M. Lake; Adina Williams (CoRR, 2025)

SAGE-Eval: Evaluating LLMs for Systematic Generalizations of Safety Facts. - Chen Yueh-Han; Guy Davidson; Brenden M. Lake (CoRR, 2025)

On the robustness of modeling grounded word learning through a child's egocentric input. - Wai Keen Vong; Brenden M. Lake (CoRR, 2025)

Whither symbols in the era of advanced neural networks? - Thomas L. Griffiths 001; Brenden M. Lake; R. Thomas McCoy; Ellie Pavlick; Taylor W. Webb (CoRR, 2025)