The content discusses the integration of differentiable pruning and combinatorial optimization techniques for structured neural network pruning. It explores theoretical foundations, empirical results, and comparisons with baseline algorithms across various datasets and sparsity levels.
Neural network pruning is crucial for developing large yet scalable models. The work unites differentiable pruning methods with combinatorial optimization to select important parameters efficiently. Various techniques like magnitude pruning, ℓ1 regularization, and greedy coordinate descent have been successful in practice.
Structured sparsity constraints lead to efficiency gains due to improved hardware utilization. The SequentialAttention++ algorithm combines Sequential Attention with ACDC for block-wise neural network pruning. Theoretical results show how differentiable pruning can be understood as nonconvex regularization for group sparse optimization.
Empirical evaluations on ImageNet and Criteo datasets demonstrate that SequentialAttention++ outperforms ACDC in block sparsification tasks. Results indicate that it is highly accurate for large block sizes and extreme sparsities. The study provides insights into the importance of combining different techniques for efficient neural network pruning.
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by Taisuke Yasu... om arxiv.org 02-29-2024
https://arxiv.org/pdf/2402.17902.pdfDiepere vragen