Search Results for author:
Found 4 papers, 1 papers with code
Incorporating simulated spatial context information improves the effectiveness of contrastive learning models
The historical spatial context of the agent provides a similarity signal for self-supervised contrastive learning.
Using Navigational Information to Learn Visual Representations
Children learn to build a visual representation of the world from unsupervised exploration and we hypothesize that a key part of this learning ability is the use of self-generated navigational information as a similarity label to drive a learning objective for self-supervised learning.
Seeking the Building Blocks of Visual Imagery and Creativity in a Cognitively Inspired Neural Network
To answer this question, we need to explore the cognitive, computational and neural mechanisms underlying imagery and creativity.
Neuromatch Academy: Teaching Computational Neuroscience with global accessibility
Neuromatch Academy designed and ran a fully online 3-week Computational Neuroscience summer school for 1757 students with 191 teaching assistants working in virtual inverted (or flipped) classrooms and on small group projects.
