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arxiv: 1806.05403 · v1 · pith:226DNITEnew · submitted 2018-06-14 · 💻 cs.LG · stat.ML

On the Perceptron's Compression

Shay Moran , Ido Nachum , Itai Panasoff , Amir Yehudayoff This is my paper
classification 💻 cs.LG stat.ML
keywords perceptroncompressionmodificationsthemealgorithmbetterconcernsconclusions
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We study and provide exposition to several phenomena that are related to the perceptron's compression. One theme concerns modifications of the perceptron algorithm that yield better guarantees on the margin of the hyperplane it outputs. These modifications can be useful in training neural networks as well, and we demonstrate them with some experimental data. In a second theme, we deduce conclusions from the perceptron's compression in various contexts.

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    For symmetric target functions, chosen initial conditions in one-hidden-layer networks enable SGD to produce generalization guarantees, unlike random initialization.