Sir Henry Dale Fellow
I study principles of learning in the brain and mind. The interactions of billions of neurons ultimately give rise to our thoughts and actions. Remarkably, much of our behavior is learned starting in infancy and continuing throughout our lifespan. Understanding how learning at the level of behavior is linked to changes at the level of neurons and synapses is a key challenge for theory.
My lab aims to develop a mathematical toolkit suitable for analyzing and describing learning in complex multi-regional brain networks. Our current focus is on the theory of deep learning, a class of artificial neural network models that take inspiration from the brain.
Alongside this theoretical work, we develop close collaborations with experimentalists to test principles of learning in biological organisms.
Qualitatively characterizing neural network optimization problems
SAXE A., (2020)
A Critique of Pure Hierarchy: Uncovering Cross-Cutting Structure in a Natural Dataset
SAXE A., (2020), Neurocomputational Models of Cognitive Development and Processing
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup
Goldt S. et al, (2020), ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), 32
Energy-entropy competition and the effectiveness of stochastic gradient
descent in machine learning
Zhang Y. et al, (2020), Molecular Physics: An International Journal at the Interface Between Chemistry and Physics
Hierarchical Subtask Discovery with Non-Negative Matrix Factorization
SAXE A., (2020)