Neural network pruning with combinatorial optimization

Modern neural networks have achieved impressive performance across a variety of applications, such as language, mathematical reasoning, and vision. However, these networks often use large architectures that require lots of computational resources. This can make it impractical to serve such models to users, especially in resource-constrained environments like wearables and smartphones. A widely used approach to mitigate the inference costs of pre-trained networks …

applications architectures athena computational graduate language machine learning mathematical reasoning mit modern network networks neural network neural networks optimization performance pruning reasoning research research scientist resources serve team vision