Quantitative Biology - Neurons and Cognition
Subcategories
Papers
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still a...
Learning high-level visual representations from a child's perspective without strong inductive biases
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 inductive biases? To inve...
Similarity of Neural Network Representations Revisited
Recent work has sought to understand the behavior of neural networks by comparing representations between layers and between different trained models. We examine methods for comparing neural network r...
A mathematical theory of semantic development in deep neural networks
An extensive body of empirical research has revealed remarkable regularities in the acquisition, organization, deployment, and neural representation of human semantic knowledge, thereby raising a fund...