Size-Independent Sample Complexity of Neural Networks
نویسندگان
چکیده
We study the sample complexity of learning neural networks, by providing new bounds on their Rademacher complexity assuming norm constraints on the parameter matrix of each layer. Compared to previous work, these complexity bounds have improved dependence on the network depth, and under some additional assumptions, are fully independent of the network size (both depth and width). These results are derived using some novel techniques, which may be of independent interest.
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ورودعنوان ژورنال:
- CoRR
دوره abs/1712.06541 شماره
صفحات -
تاریخ انتشار 2017