Belief propagation simplification

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Page 2 of 4

Introduces computational complexity, decision problems, quantum complexity, and probabilistic algorithms, including NP-hard and NP-complete problems.

State Space Models: Expressivity of Transformers

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

The Wrong Method: Polynomial Interpolation

Explores the inefficiencies of the incorrect method for polynomial interpolation.

Subset Sum: LLL Algorithm

Covers the Subset Sum problem and the efficient LLL algorithm for finding solutions in lattice basis reduction.

Computational Complexity

Covers the basics of computational complexity, including big O notation and complexity classes.

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.

Naive Bayes: Gaussian Discriminant Analysis

Covers the Naive Bayes assumption, Gaussian Discriminant Analysis, ML estimates, and Kernel trick.

Generalization in Deep Learning

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

Direct Methods for Linear Equations

Covers direct methods for solving linear equations and computation complexity of substitution.

Subquadratic Attention Mechanisms: State Space Models Overview

Covers subquadratic attention mechanisms and state space models, focusing on their theoretical foundations and practical implementations in machine learning.