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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.
Analyzing Machine Learning Workloads Using a Detailed GPU Simulator
Most deep neural networks deployed today are trained using GPUs via high-level frameworks such as TensorFlow and PyTorch.
Distributed, Parallel, and Cluster Computing
