Structured Neural Network Pruning

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תובנה - Structured Neural Network Pruning

SequentialAttention++ for Block Sparsification: Combining Differentiable Pruning and Combinatorial Optimization

Differentiable pruning combined with combinatorial optimization enhances structured neural network pruning.

Structurally Prune Any Neural Network Across Frameworks and Architectures

SPA is a versatile structured pruning framework that can prune neural networks with any architecture, from any framework, and at any stage of training, achieving state-of-the-art pruning results without the need for fine-tuning or calibration data.

Efficient Neural Network Pruning via Subspace Factorization and Variance-Based Importance Scoring

The authors propose a novel method for structured pruning of pre-trained deep neural networks that involves projecting unit activations to an orthogonal subspace, ranking units based on their non-redundant variance, and using a global variance-based cutoff to automatically determine layer-wise pruning ratios.

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