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ORCIDBrenden M. Lake
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- affiliation: New York University, USA
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2020 – today
- 2024
[j4]A. Emin Orhan
, Brenden M. Lake:
Learning high-level visual representations from a child's perspective without strong inductive biases. Nat. Mac. Intell. 6(3): 271-283 (2024)- [i40]A. Emin Orhan, Wentao Wang, Alex N. Wang, Mengye Ren, Brenden M. Lake:
Self-supervised learning of video representations from a child's perspective. CoRR abs/2402.00300 (2024) - [i39]Sreejan Kumar, Raja Marjieh, Byron Zhang, Declan Campbell, Michael Y. Hu, Umang Bhatt, Brenden M. Lake, Thomas L. Griffiths:
Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction. CoRR abs/2402.03618 (2024) - [i38]Yulu Qin, Wentao Wang, Brenden M. Lake:
A systematic investigation of learnability from single child linguistic input. CoRR abs/2402.07899 (2024) - [i37]Yanli Zhou, Brenden M. Lake, Adina Williams:
Compositional learning of functions in humans and machines. CoRR abs/2403.12201 (2024) - [i36]Ryan Teehan, Brenden M. Lake, Mengye Ren:
CoLLEGe: Concept Embedding Generation for Large Language Models. CoRR abs/2403.15362 (2024) - [i35]Guy Davidson, Graham Todd, Julian Togelius, Todd M. Gureckis, Brenden M. Lake:
Goals as Reward-Producing Programs. CoRR abs/2405.13242 (2024) - [i34]Michael A. Lepori, Alexa R. Tartaglini, Wai Keen Vong, Thomas Serre, Brenden M. Lake, Ellie Pavlick:
Beyond the Doors of Perception: Vision Transformers Represent Relations Between Objects. CoRR abs/2406.15955 (2024) - [i33]Solim LeGris, Wai Keen Vong, Brenden M. Lake, Todd M. Gureckis:
H-ARC: A Robust Estimate of Human Performance on the Abstraction and Reasoning Corpus Benchmark. CoRR abs/2409.01374 (2024) - 2023
- [j3]Brenden M. Lake, Marco Baroni:
Human-like systematic generalization through a meta-learning neural network. Nat. 623(7985): 115-121 (2023) - [i32]A. Emin Orhan, Brenden M. Lake:
What can generic neural networks learn from a child's visual experience? CoRR abs/2305.15372 (2023) - [i31]Yanli Zhou, Reuben Feinman, Brenden M. Lake:
Compositional diversity in visual concept learning. CoRR abs/2305.19374 (2023) - [i30]Alexa R. Tartaglini, Sheridan Feucht, Michael A. Lepori, Wai Keen Vong, Charles Lovering, Brenden M. Lake, Ellie Pavlick:
Deep Neural Networks Can Learn Generalizable Same-Different Visual Relations. CoRR abs/2310.09612 (2023) - 2022
- [j2]Wai Keen Vong, Brenden M. Lake:
Cross-Situational Word Learning With Multimodal Neural Networks. Cogn. Sci. 46(4) (2022) - [c45]Guy Davidson, Todd M. Gureckis, Brenden M. Lake:
Creativity, Compositionality, and Common Sense in Human Goal Generation. CogSci 2022 - [c44]Itay Itzhak, Koustuv Sinha, Brenden M. Lake, Adina Williams, Dieuwke Hupkes:
Evaluating locality in NMT models. CogSci 2022 - [c43]Laura Ruis, Brenden M. Lake:
Improving Systematic Generalization Through Modularity and Augmentation. CogSci 2022 - [c42]Alexa R. Tartaglini, Wai Keen Vong, Brenden M. Lake:
A Developmentally-Inspired Examination of Shape versus Texture Bias in Machines. CogSci 2022 - [c41]Wai Keen Vong, Brenden M. Lake:
Categorising images by generating natural language rules. CogSci 2022 - [i29]Alexa R. Tartaglini, Wai Keen Vong, Brenden M. Lake:
A Developmentally-Inspired Examination of Shape versus Texture Bias in Machines. CoRR abs/2202.08340 (2022) - [i28]Laura Ruis, Brenden M. Lake:
Improving Systematic Generalization Through Modularity and Augmentation. CoRR abs/2202.10745 (2022) - 2021
- [c40]Guy Davidson, Brenden M. Lake:
Examining Infant Relation Categorization Through Deep Neural Networks. CogSci 2021 - [c39]Aysja Johnson, Wai Keen Vong, Brenden M. Lake, Todd M. Gureckis:
Fast and Flexible: Human program induction in abstract reasoning tasks. CogSci 2021 - [c38]Eliza Kosoy, Masha Belyi, Charlie Snell, Brenden M. Lake, Josh Tenenbaum, Alison Gopnik:
The Omniglot Jr. challenge; Can a model achieve child-level character generation and classification? CogSci 2021 - [c37]Gala Stojnic, Kanishk Gandhi, Brenden M. Lake, Moira R. Dillon:
Evaluating infants' reasoning about agents using the Baby Intuitions Benchmark (BIB). CogSci 2021 - [c36]Alexa R. Tartaglini, Wai Keen Vong, Brenden M. Lake:
Modeling artificial category learning from pixels: Revisiting Shepard, Hovland, and Jenkins (1961) with deep neural networks. CogSci 2021 - [c35]Ziyun Wang, Brenden M. Lake:
Modeling Question Asking Using Neural Program Generation. CogSci 2021 - [c34]Reuben Feinman, Brenden M. Lake:
Learning Task-General Representations with Generative Neuro-Symbolic Modeling. ICLR 2021 - [c33]Ramakrishna Vedantam, Arthur Szlam, Maximilian Nickel, Ari Morcos, Brenden M. Lake:
CURI: A Benchmark for Productive Concept Learning Under Uncertainty. ICML 2021: 10519-10529 - [c32]Kanishk Gandhi, Gala Stojnic, Brenden M. Lake, Moira R. Dillon:
Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others. NeurIPS 2021: 9963-9976 - [c31]Maxwell I. Nye, Michael Henry Tessler, Joshua B. Tenenbaum, Brenden M. Lake:
Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning. NeurIPS 2021: 25192-25204 - [i27]Kanishk Gandhi, Gala Stojnic, Brenden M. Lake, Moira R. Dillon:
Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others. CoRR abs/2102.11938 (2021) - [i26]Aysja Johnson, Wai Keen Vong, Brenden M. Lake, Todd M. Gureckis:
Fast and flexible: Human program induction in abstract reasoning tasks. CoRR abs/2103.05823 (2021) - [i25]Yanli Zhou, Brenden M. Lake:
Flexible Compositional Learning of Structured Visual Concepts. CoRR abs/2105.09848 (2021) - [i24]Maxwell I. Nye, Michael Henry Tessler, Joshua B. Tenenbaum, Brenden M. Lake:
Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning. CoRR abs/2107.02794 (2021) - 2020
- [c30]Guy Davidson, Brenden M. Lake:
Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning. CogSci 2020 - [c29]Reuben Feinman, Brenden M. Lake:
Generating new concepts with hybrid neuro-symbolic models. CogSci 2020 - [c28]Arihant Jain, Brenden M. Lake, Todd M. Gureckis:
Extending the Rogers and McClelland Model of Semantic Cognition (2003) to work with Raw Pixel Information. CogSci 2020 - [c27]Wai Keen Vong, Brenden M. Lake:
Learning word-referent mappings and concepts from raw inputs. CogSci 2020 - [c26]Kanishk Gandhi, Brenden M. Lake:
Mutual exclusivity as a challenge for deep neural networks. NeurIPS 2020 - [c25]Maxwell I. Nye, Armando Solar-Lezama, Josh Tenenbaum, Brenden M. Lake:
Learning Compositional Rules via Neural Program Synthesis. NeurIPS 2020 - [c24]A. Emin Orhan, Vaibhav V. Gupta, Brenden M. Lake:
Self-supervised learning through the eyes of a child. NeurIPS 2020 - [c23]Laura Ruis, Jacob Andreas, Marco Baroni, Diane Bouchacourt, Brenden M. Lake:
A Benchmark for Systematic Generalization in Grounded Language Understanding. NeurIPS 2020 - [i23]Guy Davidson, Brenden M. Lake:
Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning. CoRR abs/2002.06703 (2020) - [i22]Laura Ruis, Jacob Andreas, Marco Baroni, Diane Bouchacourt, Brenden M. Lake:
A Benchmark for Systematic Generalization in Grounded Language Understanding. CoRR abs/2003.05161 (2020) - [i21]Maxwell I. Nye, Armando Solar-Lezama, Joshua B. Tenenbaum, Brenden M. Lake:
Learning Compositional Rules via Neural Program Synthesis. CoRR abs/2003.05562 (2020) - [i20]Wai Keen Vong, Brenden M. Lake:
Learning word-referent mappings and concepts from raw inputs. CoRR abs/2003.05573 (2020) - [i19]Reuben Feinman, Brenden M. Lake:
Generating new concepts with hybrid neuro-symbolic models. CoRR abs/2003.08978 (2020) - [i18]Reuben Feinman, Brenden M. Lake:
Learning Task-General Representations with Generative Neuro-Symbolic Modeling. CoRR abs/2006.14448 (2020) - [i17]A. Emin Orhan, Vaibhav V. Gupta, Brenden M. Lake:
Self-supervised learning through the eyes of a child. CoRR abs/2007.16189 (2020) - [i16]Brenden M. Lake, Gregory L. Murphy:
Word meaning in minds and machines. CoRR abs/2008.01766 (2020) - [i15]Ramakrishna Vedantam, Arthur Szlam, Maximilian Nickel, Ari Morcos, Brenden M. Lake:
CURI: A Benchmark for Productive Concept Learning Under Uncertainty. CoRR abs/2010.02855 (2020)
2010 – 2019
- 2019
- [c22]Brenden M. Lake, Tal Linzen, Marco Baroni:
Human few-shot learning of compositional instructions. CogSci 2019: 611-617 - [c21]Anselm Rothe, Brenden M. Lake, Todd M. Gureckis:
Asking goal-oriented questions and learning from answers. CogSci 2019: 981-986 - [c20]Reuben Feinman, Brenden M. Lake:
Learning a smooth kernel regularizer for convolutional neural networks. CogSci 2019: 1710-1716 - [c19]Brenden M. Lake:
Compositional generalization through meta sequence-to-sequence learning. NeurIPS 2019: 9788-9798 - [i14]Brenden M. Lake, Tal Linzen, Marco Baroni:
Human few-shot learning of compositional instructions. CoRR abs/1901.04587 (2019) - [i13]Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum:
The Omniglot Challenge: A 3-Year Progress Report. CoRR abs/1902.03477 (2019) - [i12]Reuben Feinman, Brenden M. Lake:
Learning a smooth kernel regularizer for convolutional neural networks. CoRR abs/1903.01882 (2019) - [i11]Brenden M. Lake, Steven T. Piantadosi:
People infer recursive visual concepts from just a few examples. CoRR abs/1904.08034 (2019) - [i10]Brenden M. Lake:
Compositional generalization through meta sequence-to-sequence learning. CoRR abs/1906.05381 (2019) - [i9]A. Emin Orhan, Brenden M. Lake:
Improving the robustness of ImageNet classifiers using elements of human visual cognition. CoRR abs/1906.08416 (2019) - [i8]Kanishk Gandhi, Brenden M. Lake:
Mutual exclusivity as a challenge for neural networks. CoRR abs/1906.10197 (2019) - [i7]Ziyun Wang, Brenden M. Lake:
Modeling question asking using neural program generation. CoRR abs/1907.09899 (2019) - 2018
- [c18]Reuben Feinman, Brenden M. Lake:
Learning Inductive Biases with Simple Neural Networks. CogSci 2018 - [c17]João Loula, Marco Baroni, Brenden M. Lake:
Rearranging the Familiar: Testing Compositional Generalization in Recurrent Networks. BlackboxNLP@EMNLP 2018: 108-114 - [c16]Brenden M. Lake, Marco Baroni:
Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks. ICML 2018: 2879-2888 - [i6]Reuben Feinman, Brenden M. Lake:
Learning Inductive Biases with Simple Neural Networks. CoRR abs/1802.02745 (2018) - [i5]João Loula, Marco Baroni, Brenden M. Lake:
Rearranging the Familiar: Testing Compositional Generalization in Recurrent Networks. CoRR abs/1807.07545 (2018) - 2017
- [c15]Eliza Kosoy, Brenden M. Lake, Josh Tenenbaum:
One-shot Learning and Classification in Children. CogSci 2017 - [c14]Anselm Rothe, Brenden M. Lake, Todd M. Gureckis:
Progress in building a machine that can ask interesting and informative questions. CogSci 2017 - [c13]Anselm Rothe, Brenden M. Lake, Todd M. Gureckis:
Question Asking as Program Generation. NIPS 2017: 1046-1055 - [i4]Brenden M. Lake, Marco Baroni:
Still not systematic after all these years: On the compositional skills of sequence-to-sequence recurrent networks. CoRR abs/1711.00350 (2017) - [i3]Anselm Rothe, Brenden M. Lake, Todd M. Gureckis:
Question Asking as Program Generation. CoRR abs/1711.06351 (2017) - 2016
- [c12]Alexander Cohen, Brenden M. Lake:
Searching large hypothesis spaces by asking questions. CogSci 2016 - [c11]Anselm Rothe, Brenden M. Lake, Todd M. Gureckis:
Asking and evaluating natural language questions. CogSci 2016 - [i2]Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, Samuel J. Gershman:
Building Machines That Learn and Think Like People. CoRR abs/1604.00289 (2016) - [i1]Brenden M. Lake, Neil D. Lawrence, Joshua B. Tenenbaum:
The Emergence of Organizing Structure in Conceptual Representation. CoRR abs/1611.09384 (2016) - 2015
- [c10]Brenden M. Lake, Wojciech Zaremba, Rob Fergus, Todd M. Gureckis:
Deep Neural Networks Predict Category Typicality Ratings for Images. CogSci 2015 - [c9]Anselm Rothe, Brenden M. Lake, Todd M. Gureckis:
Asking useful questions: Active learning with rich queries. CogSci 2015 - [c8]Mathew Monfort, Brenden M. Lake, Brian D. Ziebart, Patrick Lucey, Joshua B. Tenenbaum:
Softstar: Heuristic-Guided Probabilistic Inference. NIPS 2015: 2764-2772 - 2014
- [c7]Patricia Angie Chan, Douglas Markant, Brenden M. Lake, Todd M. Gureckis:
Adaptive teaching: Improving the efficiency of learning through hypothesis-dependent selection of training data. CogSci 2014 - [c6]Brenden M. Lake, Chia-ying Lee, James R. Glass, Joshua B. Tenenbaum:
One-shot learning of generative speech concepts. CogSci 2014 - [c5]Brenden M. Lake, Josh Tenenbaum:
Computational Creativity: Generating new objects with a hierarchical Bayesian model. CogSci 2014 - 2013
- [c4]Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum:
One-shot learning by inverting a compositional causal process. NIPS 2013: 2526-2534 - 2012
- [c3]Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum:
Concept learning as motor program induction: A large-scale empirical study. CogSci 2012 - 2011
- [c2]Brenden M. Lake, James L. McClelland:
Estimating the strength of unlabeled information during semi-supervised learning. CogSci 2011 - [c1]Brenden M. Lake, Ruslan Salakhutdinov, Jason Gross, Joshua B. Tenenbaum:
One shot learning of simple visual concepts. CogSci 2011
2000 – 2009
- 2009
- [j1]Brenden M. Lake, Gautam K. Vallabha, James L. McClelland:
Modeling Unsupervised Perceptual Category Learning. IEEE Trans. Auton. Ment. Dev. 1(1): 35-43 (2009)
Coauthor Index
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