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Learning a Single Neuron with Bias Using Gradient Descent
Gal Vardi · Gilad Yehudai · Ohad Shamir

Thu Dec 09 12:30 AM -- 02:00 AM (PST) @
We theoretically study the fundamental problem of learning a single neuron with a bias term ($\mathbf{x}\mapsto \sigma(\langle\mathbf{w},\mathbf{x}\rangle + b)$) in the realizable setting with the ReLU activation, using gradient descent. Perhaps surprisingly, we show that this is a significantly different and more challenging problem than the bias-less case (which was the focus of previous works on single neurons), both in terms of the optimization geometry as well as the ability of gradient methods to succeed in some scenarios. We provide a detailed study of this problem, characterizing the critical points of the objective, demonstrating failure cases, and providing positive convergence guarantees under different sets of assumptions. To prove our results, we develop some tools which may be of independent interest, and improve previous results on learning single neurons.

Author Information

Gal Vardi (Weizmann Institute of Science)
Gilad Yehudai (Weizmann Institute of Science)
Ohad Shamir (Weizmann Institute of Science)

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