Algorithms & Theory
Model compression refers to techniques used to reduce the size and complexity of machine learning models while maintaining their performance. This can involve methods such as pruning, quantization, and knowledge distillation, making models faster and more efficient for deployment in resource-constrained environments.
Efficient neural networks are designed to perform tasks with lower computational costs and faster inference times. Techniques such as pruning, quantization, and knowledge distillation are commonly used to enhance their efficiency.