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  • title: Divide and Conquer for Quantization: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks
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            Divide and Conquer for Quantization: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks
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            Divide and Conquer for Quantization: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks

            Jul 12, 2020

            Speakers

            ATE

            Ahmed T. Elthakeb

            Speaker · 0 followers

            PP

            Prannoy Pilligundla

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            FM

            FatemehSadat Mireshghallah

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            About

            The deep layers of modern neural networks extract a rather rich set of features as an input propagates through the network. This paper sets out to harvest these rich intermediate representations for quantization with minimal accuracy loss while significantly reducing the memory footprint and compute intensity of the DNN. This paper utilizes knowledge distillation through teacher-student paradigm (Hinton et al., 2015) in a novel setting that exploits the feature extraction capability of DNNs for…

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            The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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