27 citations · 66 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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 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…
The Omniglot challenge: a 3-year progress report
Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum
Three years ago, we released the Omniglot dataset for one-shot learning, along with five challenge tasks and a computational model that addresses these tasks. The model was not mea…