Compressive Sensing

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Related lectures (30)

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Provable Benefits of Overparameterization in Model Compression

Explores the provable benefits of overparameterization in model compression, emphasizing the efficiency of deep neural networks and the importance of retraining for improved performance.

Signals & Systems I: Sampling and Reconstruction

Explores ideal sampling, Fourier transformation, spectral repetition, and analog signal reconstruction.

Watermarking II

Covers advanced topics in watermarking, including resisting scaling and rotations, self-referenced watermarking, and types of attacks.

JPEG XS & JPEG XL: Next-Gen Image Compression

Explores the cutting-edge JPEG XS and JPEG XL image compression standards, emphasizing their efficiency and versatility in various applications.

Data Compression: Sparse Signals and Data Recording

Explores data compression through signal sparsity, questioning the need for recording vast amounts of data.

Signal Sampling: Sampling

Explains signal sampling, the need for discrete sampling, errors, quantization, and choosing the right sampling period.

Second-Order Model Compression

Explores second-order model compression for massive deep neural networks, showcasing compression techniques and their impact on model accuracy.

Analog-to-Digital Conversion

Explores analog-to-digital conversion principles, covering resolution, sampling rate, and conversion characteristics.

Compression: Prefix-Free Codes

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

Sampling: Signal Reconstruction and Aliasing

Covers the importance of sampling, signal reconstruction, and aliasing in digital representation.