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Found 3 papers, 1 papers with code
FTL: A universal framework for training low-bit DNNs via Feature Transfer
Compared with low-bit models trained directly, the proposed framework brings 0. 5% to 3. 4% accuracy gains to three different quantization schemes.
FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours
In this work, we present FastFold, an efficient implementation of AlphaFold for both training and inference.
HMS-Net: Hierarchical Multi-scale Sparsity-invariant Network for Sparse Depth Completion
Dense depth cues are important and have wide applications in various computer vision tasks.
Ranked #9 on
Depth Completion
on KITTI Depth Completion
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