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Provable Methods for Training Neural Networks with Sparse Connectivity

Sedghi, Hanie and Anandkumar, Anima (2014) Provable Methods for Training Neural Networks with Sparse Connectivity. In: 3rd International Conference on Learning Representations (ICLR 2015), 7-9 May 2015, San Diego, CA.

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We provide novel guaranteed approaches for training feedforward neural networks with sparse connectivity. We leverage on the techniques developed previously for learning linear networks and show that they can also be effectively adopted to learn non-linear networks. We operate on the moments involving label and the score function of the input, and show that their factorization provably yields the weight matrix of the first layer of a deep network under mild conditions. In practice, the output of our method can be employed as effective initializers for gradient descent.

Item Type:Conference or Workshop Item (Poster)
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URLURL TypeDescription Paper
Additional Information:A. Anandkumar is supported in part by Microsoft Faculty Fellowship, NSF Career award CCF-1254106, NSF Award CCF-1219234, ARO YIP Award W911NF-13-1-0084 and ONR Award N00014-14-1-0665. H. Sedghi is supported by ONR Award N00014-14-1-0665.
Funding AgencyGrant Number
Microsoft Faculty FellowshipUNSPECIFIED
Army Research Office (ARO)W911NF-13-1-0084
Office of Naval Research (ONR)N00014-14-1-0665
Subject Keywords:Deep feedforward networks, sparse connectivity, ℓ1-optimization, Stein’s lemma
Record Number:CaltechAUTHORS:20190401-162914714
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94343
Deposited By: George Porter
Deposited On:03 Apr 2019 14:39
Last Modified:02 Jun 2023 00:21

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