77 citations · 479 across the 36 of their papers we have counts for
43 papers
Continual Visual and Verbal Learning Through a Child's Egocentric Input
Xiaoyang Jiang, Yanlai Yang, Kenneth A. Norman +2
Children learn the meanings of words from a continuous, temporally structured stream of egocentric experience. Recent work shows that neural networks can also learn word-referent m…
On the robustness of modeling grounded word learning through a child's egocentric input
Wai Keen Vong, Brenden M. Lake
What insights can machine learning bring to understanding human language acquisition? Large language and multimodal models have achieved remarkable capabilities, but their reliance…
SAGE-Eval: Evaluating LLMs for Systematic Generalizations of Safety Facts
Chen Yueh-Han, Guy Davidson, Brenden M. Lake
Do LLMs robustly generalize critical safety facts to novel situations? Lacking this ability is dangerous when users ask naive questions. For instance, "I'm considering packing melo…
Do different prompting methods yield a common task representation in language models?
Guy Davidson, Todd M. Gureckis, Brenden M. Lake +1
Demonstrations and instructions are two primary approaches for prompting language models to perform in-context learning (ICL) tasks. Do identical tasks elicited in different ways r…
Do Large Language Models Reason Causally Like Us? Even Better?
Hanna M. Dettki, Brenden M. Lake, Charley M. Wu +1
Causal reasoning is a core component of intelligence. Large language models (LLMs) have shown impressive capabilities in generating human-like text, raising questions about whether…
Overcoming classic challenges for artificial neural networks by providing incentives and practice
Kazuki Irie, Brenden M. Lake
Since the earliest proposals for artificial neural network (ANN) models of the mind and brain, critics have pointed out key weaknesses in these models compared to human cognitive a…
H-ARC: A Robust Estimate of Human Performance on the Abstraction and Reasoning Corpus Benchmark
Solim LeGris, Wai Keen Vong, Brenden M. Lake +1
The Abstraction and Reasoning Corpus (ARC) is a visual program synthesis benchmark designed to test challenging out-of-distribution generalization in humans and machines. Since 201…
Beyond the Doors of Perception: Vision Transformers Represent Relations Between Objects
Michael A. Lepori, Alexa R. Tartaglini, Wai Keen Vong +3
Though vision transformers (ViTs) have achieved state-of-the-art performance in a variety of settings, they exhibit surprising failures when performing tasks involving visual relat…
Goals as Reward-Producing Programs
Guy Davidson, Graham Todd, Julian Togelius +2
People are remarkably capable of generating their own goals, beginning with child's play and continuing into adulthood. Despite considerable empirical and computational work on goa…
Compositional learning of functions in humans and machines
Yanli Zhou, Brenden M. Lake, Adina Williams
The ability to learn and compose functions is foundational to efficient learning and reasoning in humans, enabling flexible generalizations such as creating new dishes from known c…
A systematic investigation of learnability from single child linguistic input
Yulu Qin, Wentao Wang, Brenden M. Lake
Language models (LMs) have demonstrated remarkable proficiency in generating linguistically coherent text, sparking discussions about their relevance to understanding human languag…
Self-supervised learning of video representations from a child's perspective
A. Emin Orhan, Wentao Wang, Alex N. Wang +2
Children learn powerful internal models of the world around them from a few years of egocentric visual experience. Can such internal models be learned from a child's visual experie…
Deep Neural Networks Can Learn Generalizable Same-Different Visual Relations
Alexa R. Tartaglini, Sheridan Feucht, Michael A. Lepori +4
Although deep neural networks can achieve human-level performance on many object recognition benchmarks, prior work suggests that these same models fail to learn simple abstract re…
Compositional diversity in visual concept learning
Yanli Zhou, Reuben Feinman, Brenden M. Lake
Humans leverage compositionality to efficiently learn new concepts, understanding how familiar parts can combine together to form novel objects. In contrast, popular computer visio…
Learning high-level visual representations from a child's perspective without strong inductive biases
A. Emin Orhan, Brenden M. Lake
Young children develop sophisticated internal models of the world based on their visual experience. Can such models be learned from a child's visual experience without strong induc…
Improving Systematic Generalization Through Modularity and Augmentation
Laura Ruis, Brenden Lake
Systematic generalization is the ability to combine known parts into novel meaning; an important aspect of efficient human learning, but a weakness of neural network learning. In t…
A Developmentally-Inspired Examination of Shape versus Texture Bias in Machines
Alexa R. Tartaglini, Wai Keen Vong, Brenden M. Lake
Early in development, children learn to extend novel category labels to objects with the same shape, a phenomenon known as the shape bias. Inspired by these findings, Geirhos et al…
Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning
Maxwell Nye, Michael Henry Tessler, Joshua B. Tenenbaum +1
Human reasoning can often be understood as an interplay between two systems: the intuitive and associative ("System 1") and the deliberative and logical ("System 2"). Neural sequen…
Flexible Compositional Learning of Structured Visual Concepts
Yanli Zhou, Brenden M. Lake
Humans are highly efficient learners, with the ability to grasp the meaning of a new concept from just a few examples. Unlike popular computer vision systems, humans can flexibly l…
Fast and flexible: Human program induction in abstract reasoning tasks
Aysja Johnson, Wai Keen Vong, Brenden M. Lake +1
The Abstraction and Reasoning Corpus (ARC) is a challenging program induction dataset that was recently proposed by Chollet (2019). Here, we report the first set of results collect…
Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others
Kanishk Gandhi, Gala Stojnic, Brenden M. Lake +1
To achieve human-like common sense about everyday life, machine learning systems must understand and reason about the goals, preferences, and actions of other agents in the environ…
CURI: A Benchmark for Productive Concept Learning Under Uncertainty
Ramakrishna Vedantam, Arthur Szlam, Maximilian Nickel +2
Humans can learn and reason under substantial uncertainty in a space of infinitely many concepts, including structured relational concepts ("a scene with objects that have the same…
Word meaning in minds and machines
Brenden M. Lake, Gregory L. Murphy
Machines have achieved a broad and growing set of linguistic competencies, thanks to recent progress in Natural Language Processing (NLP). Psychologists have shown increasing inter…
Self-supervised learning through the eyes of a child
A. Emin Orhan, Vaibhav V. Gupta, Brenden M. Lake
Within months of birth, children develop meaningful expectations about the world around them. How much of this early knowledge can be explained through generic learning mechanisms…