77 citations · 479 across the 36 of their papers we have counts for
9 papers · 1 filter
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…
Learning Task-General Representations with Generative Neuro-Symbolic Modeling
Reuben Feinman, Brenden M. Lake
People can learn rich, general-purpose conceptual representations from only raw perceptual inputs. Current machine learning approaches fall well short of these human standards, alt…
Learning word-referent mappings and concepts from raw inputs
Wai Keen Vong, Brenden M. Lake
How do children learn correspondences between the language and the world from noisy, ambiguous, naturalistic input? One hypothesis is via cross-situational learning: tracking words…
Generating new concepts with hybrid neuro-symbolic models
Reuben Feinman, Brenden M. Lake
Human conceptual knowledge supports the ability to generate novel yet highly structured concepts, and the form of this conceptual knowledge is of great interest to cognitive scient…
Learning Compositional Rules via Neural Program Synthesis
Maxwell I. Nye, Armando Solar-Lezama, Joshua B. Tenenbaum +1
Many aspects of human reasoning, including language, require learning rules from very little data. Humans can do this, often learning systematic rules from very few examples, and c…
A Benchmark for Systematic Generalization in Grounded Language Understanding
Laura Ruis, Jacob Andreas, Marco Baroni +2
Humans easily interpret expressions that describe unfamiliar situations composed from familiar parts ("greet the pink brontosaurus by the ferris wheel"). Modern neural networks, by…
Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning
Guy Davidson, Brenden M. Lake
We explore the benefits of augmenting state-of-the-art model-free deep reinforcement algorithms with simple object representations. Following the Frostbite challenge posited by Lak…