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Found 5 papers, 1 papers with code
Efficient Training Under Limited Resources
Training time budget and size of the dataset are among the factors affecting the performance of a Deep Neural Network (DNN).
Scaling Deep Networks with the Mesh Adaptive Direct Search algorithm
Deep neural networks are getting larger.
Rethinking Pareto Frontier for Performance Evaluation of Deep Neural Networks
We propose to use a multi-dimensional Pareto frontier to re-define the efficiency measure of candidate deep learning models, where several variables such as training cost, inference latency, and accuracy play a relative role in defining a dominant model.
Demystifying and Generalizing BinaryConnect
BinaryConnect (BC) and its many variations have become the de facto standard for neural network quantization.
Importance of Data Loading Pipeline in Training Deep Neural Networks
Training large-scale deep neural networks is a long, time-consuming operation, often requiring many GPUs to accelerate.
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