However, their training is a non convex optimization problem. Here we address this difficulty by turning to "improper learning" of neural nets. In other words, ...
Neural networks have recently re-emerged as a powerful hypothesis class, yielding impressive classification accuracy in multiple domains.
Finally, we evaluate our improper deep kernel on simulated data as well as object recognition benchmarks. Page 2. Improper Deep Kernels. 2 Parametric Improper ...
Improper Deep Kernels. Uri Heinemann, Roi Livni, Gal Elidan, Elad Eban, Amir Globerson. The Hebrew University of Jerusalem, Google, Tel Aviv University. Proof.
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This work learns a classifier that is not a neural net but is competitive with the best neural net model given a sufficient number of training examples, ...
Neural networks have recently re-emerged as a powerful hypothesis class, yielding impressive classification accuracy in multiple domains.
Dive into the research topics of 'Improper deep kernels'. Together they form a unique fingerprint. Sort by; Weight · Alphabetically. Mathematics.
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Uri Heinemann, Roi Livni, Elad Eban, Gal Elidan, Amir Globerson: Improper Deep Kernels. AISTATS 2016: 1159-1167. a service of Schloss Dagstuhl - Leibniz ...
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