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Generating Efficient DNN-Ensembles with Evolutionary Computation
In this work, we leverage ensemble learning as a tool for the creation of faster, smaller, and more accurate deep learning models.
Low-Precision Floating-Point Schemes for Neural Network Training
The use of low-precision fixed-point arithmetic along with stochastic rounding has been proposed as a promising alternative to the commonly used 32-bit floating point arithmetic to enhance training neural networks training in terms of performance and energy efficiency.
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