Sparse-IFT: Enhancing Training Efficiency with Sparse Iso-FLOP Transformations
Our approach, Sparse Iso-FLOP Transformations (Sparse-IFT), uses sparsity to improve accuracy while maintaining dense model FLOPs. By expanding the search space for optimal sparse masks and utilizing dynamic sparse training, our study reveals a robust correlation among mask topology, weights, and final performance.
Sparse-IFT: Sparse Iso-FLOP Transformations for Maximizing Training Efficiency
Sparsity in neural networks can improve accuracy without sacrificing training efficiency through Sparse Iso-FLOP Transformations (Sparse-IFT).
Sparse-IFT: Improving Training Efficiency with Sparse Iso-FLOP Transformations
Using Sparse Iso-FLOP Transformations (Sparse-IFT) enhances accuracy while maintaining dense model FLOPs, improving training efficiency.
Sparse-IFT: Sparse Iso-FLOP Transformations for Maximizing Training Efficiency
Sparse-IFT introduces non-linear sparse transformations to enhance model accuracy without increasing training and inference FLOPs compared to dense models.
