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Found 3 papers, 3 papers with code
Learning Label Encodings for Deep Regression
Underlying RLEL is our observation that the search space of label encodings can be constrained and efficiently explored by using a continuous search space of real-valued label encodings combined with a regularization function designed to encourage encodings with certain properties.
Label Encoding for Regression Networks
Prior work has shown that solving a regression problem with a set of binary classifiers can improve accuracy by utilizing well-studied binary classification algorithms.
Sparse Weight Activation Training
For ResNet-50 on ImageNet SWAT reduces total floating-point operations (FLOPS) during training by 80% resulting in a 3. 3$\times$ training speedup when run on a simulated sparse learning accelerator representative of emerging platforms while incurring only 1. 63% reduction in validation accuracy.
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