Asymptotic properties of one-layer artificial neural networks with sparse connectivity
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Abstract (arXiv)
A law of large numbers for the empirical distribution of parameters of a one-layer artificial neural networks with sparse connectivity is derived for a simultaneously increasing number of both, neurons and training iterations of the stochastic gradient descent.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 19 references that are in the map. First posted on arXiv 1 Dec 2021.
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References · 6
- 2018A Mean Field View of the Landscape of Two-Layers Neural Networkscited by 466
- 2018On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transportcited by 426
- 2017Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Sciencecited by 328
- 2018Mean Field Analysis of Neural Networks: A Central Limit Theoremcited by 107
- 2018Pre-Defined Sparse Neural Networks with Hardware Accelerationcited by 9
- 2014Understanding brain networks and brain organizationcited by 6
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