Applications of GAMP

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Related lectures (28)

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Compression: Prefix-Free Codes

Explains prefix-free codes for efficient data compression and the significance of uniquely decodable codes.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Spike Wigner Model

Explores the Spike Wigner model, Bayesian denoising, state evolution, and spectral methods in matrix analysis.

Optimization Methods: Theory Discussion

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Neural Signal Compression

Explores analog-to-digital conversion, neural signal optimization, multichannel architectures, and on-chip compression techniques in neuroengineering.

Data Compression and Shannon-Fano Algorithm

Explores the Shannon-Fano algorithm for data compression and its efficiency in creating unique binary codes for letters.

Model Compression Techniques: Enhancing Neural Networks

Covers model compression techniques to enhance the efficiency of large language models in production settings.

Structures in Non-Convex Optimization

Covers non-convex optimization, deep learning training problems, stochastic gradient descent, adaptive methods, and neural network architectures.

Adaptive Gradient Methods: Theory and Applications

Explores adaptive gradient methods, their properties, convergence, and comparison with traditional optimization algorithms.