- Pruning is a technique used to implement sparsity in machine learning models. - It involves reducing the number of parameters in existing networks, either individually or in groups. - Parameters refer to the weights and biases that are used to make predictions in a model. - By reducing the number of parameters, pruning helps to simplify the model and make it more efficient. - This can lead to a reduction in computational demand and memory footprint, which is important for models that need to run on resource-constrained devices. - Pruning can be done in different ways, such as by removing individual weights or entire neurons or layers. - The process of pruning is usually automated, using algorithms that identify which parameters can be safely removed without significantly affecting the model's performance. - Despite the reduction in parameters, pruned models can still retain their predictive power, as long as the pruning is done carefully. - Pruning is a popular technique for reducing the size and complexity of deep neural networks, which can have millions of parameters. - It is often used in combination with other techniques, such as [[Distillation]], to create smaller and more efficient models that can be deployed in real-world applications. Ref: 1. https://arxiv.org/abs//2306.11695