Optical Computing: All-Analog Photoelectronic Chip for Vision Tasks

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Neural Signals and Signal Processing

Explores neuronal signals, brain organization, measurement techniques, and MRI principles.

Model Compression Techniques: Enhancing Neural Networks

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

Signals & Systems I: Introduction and Signal Processing

Covers introductory lessons on signals and systems, signal processing, and practical applications like image compression and multimedia.

Dimensionality Reduction: PCA and Autoencoders

Introduces artificial neural networks, CNNs, and dimensionality reduction using PCA and autoencoders.

Neural Networks: Two Layers Neural Network

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Neural Signals and Signal Processing: Hemodynamic Imaging

Explores hemodynamic imaging techniques to measure brain activity and understand brain structure and function.

Machine Learning for Solving PDEs: Random Feature Method

Explores the Random Feature Method for solving PDEs using machine learning algorithms to approximate high-dimensional functions efficiently.

Compressive Sensing of Neural Signals

Explores compressive sensing for neural signals, focusing on data reduction and efficient signal reconstruction.

Compressive Sensing

Explores compressive sensing theory and hardware implementations for signal reconstruction.

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.