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1512.03965
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The Power of Depth for Feedforward Neural Networks
12 December 2015
Ronen Eldan
Ohad Shamir
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Papers citing
"The Power of Depth for Feedforward Neural Networks"
50 / 367 papers shown
Title
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Limits on representing Boolean functions by linear combinations of simple functions: thresholds, ReLUs, and low-degree polynomials
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The Role of Information Complexity and Randomization in Representation Learning
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On Characterizing the Capacity of Neural Networks using Algebraic Topology
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Deep Learning Works in Practice. But Does it Work in Theory?
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A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading
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D. Puig
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The exploding gradient problem demystified - definition, prevalence, impact, origin, tradeoffs, and solutions
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D. Song
J. Carbonell
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15 Dec 2017
A trans-disciplinary review of deep learning research for water resources scientists
Chaopeng Shen
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Exploiting Nontrivial Connectivity for Automatic Speech Recognition
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Lars Maaløe
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28 Nov 2017
Lower bounds over Boolean inputs for deep neural networks with ReLU gates
Anirbit Mukherjee
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Bounding and Counting Linear Regions of Deep Neural Networks
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Christian Tjandraatmadja
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An efficient quantum algorithm for generative machine learning
Xun Gao
Zhengyu Zhang
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Expressive power of recurrent neural networks
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Optimization Landscape and Expressivity of Deep CNNs
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On the Long-Term Memory of Deep Recurrent Networks
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The Expressive Power of Neural Networks: A View from the Width
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Deep Learning Techniques for Music Generation -- A Survey
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Deep Radial Kernel Networks: Approximating Radially Symmetric Functions with Deep Networks
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Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks
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Mixing Complexity and its Applications to Neural Networks
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Depth Separation for Neural Networks
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Deep Stochastic Configuration Networks with Universal Approximation Property
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31 Jan 2017
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Reliably Learning the ReLU in Polynomial Time
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An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learning
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Moritz Hardt
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Understanding Deep Neural Networks with Rectified Linear Units
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Learning Identity Mappings with Residual Gates
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