1 citations · 1 across the 4 of their papers we have counts for
4 papers
Klingen Vectors for Depth Zero Supercuspidals of
Jonathan Cohen
Let be a non-archimedean local field of characteristic zero and a depth zero, irreducible, supercuspidal representation of . We calculate the dimensions of…
Relational Constraints On Neural Networks Reproduce Human Biases towards Abstract Geometric Regularity
Declan Campbell, Sreejan Kumar, Tyler Giallanza +2
Uniquely among primates, humans possess a remarkable capacity to recognize and manipulate abstract structure in the service of task goals across a broad range of behaviors. One ill…
A Quantitative Approach to Predicting Representational Learning and Performance in Neural Networks
Ryan Pyle, Sebastian Musslick, Jonathan D. Cohen +1
A key property of neural networks (both biological and artificial) is how they learn to represent and manipulate input information in order to solve a task. Different types of repr…
Beyond Transformers for Function Learning
Simon Segert, Jonathan Cohen
The ability to learn and predict simple functions is a key aspect of human intelligence. Recent works have started to explore this ability using transformer architectures, however…