Learning from Probabilistic Models

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (28)

Page 1 of 3

State Space Models: Expressivity of Transformers

Covers state space models and the expressivity of transformers in sequence copying tasks.

Statistical Physics of Clusters

Explores the statistical physics of clusters, focusing on complexity and equilibrium behavior.

Elements of computational complexity

Covers classical and quantum computational complexity concepts and implications.

Graph Coloring: Random vs Symmetrical

Compares random and symmetrical graph coloring in terms of cluster colorability and equilibrium.

Distributed Intelligent Systems: Division of Labor and Multi-Robot Coordination

Explores division of labor in natural systems, multi-robot coordination, and the challenges of uncertainty in market-based algorithms.

Computation with Tensor Networks

Explores computation with tensor networks, covering joint probability distributions, statistical mechanics, and quantum computation applications.

Graph Coloring: Theory and Applications

Covers the theory and applications of graph coloring, focusing on disassortative stochastic block models and planted coloring.

Introduction to Information, Computation, and Communication

Introduces the fundamental principles of Information, Computation, and Communication theory, covering genomics, medical imaging, and assistive technology.

Graphical Models: Representing Probabilistic Distributions

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Generalization in Deep Learning

Explores generalization in deep learning, covering model complexity, implicit bias, and the double descent phenomenon.