In this paper, we characterize the learnability of fully-connected neural networks via both positive and negative results. We focus on ℓ1-regularized networks, ...
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In this paper, we characterize the learnability of fully- connected neural networks via both positive and negative results. We focus on l1-regularized networks, ...
Despite the empirical success of deep neural net- works, there is limited theoretical understand- ing of the learnability of these models with re-.
This paper characterize the learnability of fullyconnected neural networks via both positive and negative results, and establishes a hardness result showing ...
Abstract. Despite the empirical success of deep neural networks, there is limited theoretical understanding of the learnability of these models with respect to ...
Nov 20, 2019 · Practitioners know that if we increase the number of full-connected layers in Neural Network (NN), then at some points the NN performance starts ...
Dec 6, 2021 · Learn how to build a fully connected neural network from scratch in Python 3 (adapted from Michael Nielson's Neural Networks and Deep Learning ...
Missing: Learnability | Show results with:Learnability
More recently (2016) [14] and after the deep learning diffusion, authors proposed a small-world ANN that has better performance than traditional structure in ...
6 days ago · A Fully Connected layer is a type of neural network layer where every neuron in the layer is connected to every neuron in the previous and ...
Aug 29, 2019 · Abstract:Recent results in nonparametric regression show that deep learning, i.e., neural network estimates with many hidden layers, ...