Explores image compression through various approaches like pixel and block level compression, Discrete Cosine Transform, quantization, and entropy coding.
Explores MP3 encoding, emphasizing reducing bits through lossy compression and utilizing psycho-acoustic models for efficient filtering and quantization.
Explores the provable benefits of overparameterization in model compression, emphasizing the efficiency of deep neural networks and the importance of retraining for improved performance.
Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.
Delves into training and applications of Vision-Language-Action models, emphasizing large language models' role in robotic control and the transfer of web knowledge. Results from experiments and future research directions are highlighted.
Explores deep learning for NLP, covering word embeddings, context representations, learning techniques, and challenges like vanishing gradients and ethical considerations.
