Subquadratic Attention Mechanisms: State Space Models Overview

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Covers state space models and their expressivity compared to transformers, focusing on attention mechanisms and computational efficiency.

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

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.

Covers classical and quantum computational complexity concepts and implications.

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

Explores the history and efficiency of the fast Fourier transform algorithm, demonstrating its practical applications and computational advantages.

Explores the Fast Fourier Transform algorithm for efficient signal processing applications, including dividing signals and analyzing complexity.

Explores dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.

Covers optimization algorithms, convergence properties, and time complexity of sequences and functions.

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.