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Found 4 papers, 2 papers with code
DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward Propagation
In this paper, we propose Dropping Backward Propagation (DropBP), a novel approach designed to reduce computational costs while maintaining accuracy.
Activation Sharing with Asymmetric Paths Solves Weight Transport Problem without Bidirectional Connection
One of the reasons why it is difficult for the brain to perform backpropagation (BP) is the weight transport problem, which argues forward and feedback neurons cannot share the same synaptic weights during learning in biological neural networks.
Toward Efficient Low-Precision Training: Data Format Optimization and Hysteresis Quantization
In this paper, we propose a method to efficiently find an optimal format without actual training of deep neural networks.
Real-time Denoising and Dereverberation with Tiny Recurrent U-Net
Modern deep learning-based models have seen outstanding performance improvement with speech enhancement tasks.
