4 papers · 1 filter
A Group Theoretic Analysis of the Symmetries Underlying Base Addition and Their Learnability by Neural Networks
Cutter Dawes, Simon Segert, Kamesh Krishnamurthy +1
A major challenge in the use of neural networks both for modeling human cognitive function and for artificial intelligence is the design of systems with the capacity to efficiently…
Flat Minima in Linear Estimation and an Extended Gauss Markov Theorem
Simon Segert
We consider the problem of linear estimation, and establish an extension of the Gauss-Markov theorem, in which the bias operator is allowed to be non-zero but bounded with respect…
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…
A Self-Supervised Framework for Function Learning and Extrapolation
Simon N. Segert, Jonathan D. Cohen
Understanding how agents learn to generalize -- and, in particular, to extrapolate -- in high-dimensional, naturalistic environments remains a challenge for both machine learning a…