3 citations · 3 across the 1 of their papers we have counts for
5 papers
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…
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…
A Linear Systems Theory of Normalizing Flows
Reuben Feinman, Nikhil Parthasarathy
Normalizing Flows are a promising new class of algorithms for unsupervised learning based on maximum likelihood optimization with change of variables. They offer to learn a factori…
Learning a smooth kernel regularizer for convolutional neural networks
Reuben Feinman, Brenden M. Lake
Modern deep neural networks require a tremendous amount of data to train, often needing hundreds or thousands of labeled examples to learn an effective representation. For these ne…
Learning Inductive Biases with Simple Neural Networks
Reuben Feinman, Brenden M. Lake
People use rich prior knowledge about the world in order to efficiently learn new concepts. These priors - also known as "inductive biases" - pertain to the space of internal model…