Delves into associative memory, Hebbian learning, and hierarchical organization in neural networks.
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Offers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.
Explores the Stochastic Hopfield model, noisy neurons, firing probabilities, memory retrieval, and overlap equations in attractor networks.
Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.
Explores learning, memory, cognitive loads, metacognition, and problem-solving tasks.
