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Compact neural networks are essential for affordable and power efficient deep learning solutions.
Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
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The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 2000
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Alexander Kraskov, Harald Stögbauer, and Peter Grassberger · 2004
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Elements of information theory
Thomas M Cover and Joy A Thomas · 2012
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Human-level control through deep reinforcement learning
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On the information bottleneck theory of deep learning
Andrew M Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, Brendan D Tracey, and David D Cox · 2019
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Latent weights do not exist: Rethinking binarized neural network optimization
Koen Helwegen, James Widdicombe, Lukas Geiger, Zechun Liu, Kwang-Ting Cheng, and Roeland Nusselder · 2019
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Scalable mutual information estimation using dependence graphs
Morteza Noshad, Yu Zeng, and Alfred O Hero · 2019
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Regularizing activation distribution for training binarized deep networks
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Alexander A Alemi and Ian Fischer · 2018
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Attacking binarized neural networks
Angus Galloway, Graham W. Taylor, and Medhat Moussa · 2018
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Binarized neural networks: Training deep neural networks with weights and activations constrained to
Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio
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Regularized binary network training
Sajad Darabi, Mouloud Belbahri, Matthieu Courbariaux, and Vahid Partovi Nia
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Ruizhou Ding, Ting-Wu Chin, Zeye Liu, and Diana Marculescu · 2019
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What is the state of neural network pruning?
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag · 2020
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Statistical mechanics of deep learning
Yasaman Bahri, Jonathan Kadmon, Jeffrey Pennington, Sam S Schoenholz, Jascha Sohl-Dickstein, and Surya Ganguli · 2020
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