Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data

• URL: http://arxiv.org/abs/2210.07082v1
• Date: Thu, 13 Oct 2022 15:09:54 GMT
• Title: Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data
• Authors: Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu
• Abstract summary: In this work, we investigate the implicit bias of gradient flow and gradient descent in two-layer fully-connected neural networks with ReLU activations. For gradient flow, we leverage recent work on the implicit bias for homogeneous neural networks to show that leakyally, gradient flow produces a neural network with rank at most two. For gradient descent, provided the random variance is small enough, we show that a single step of gradient descent suffices to drastically reduce the rank of the network, and that the rank remains small throughout training.
• Score: 63.34506218832164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The implicit biases of gradient-based optimization algorithms are conjectured to be a major factor in the success of modern deep learning. In this work, we investigate the implicit bias of gradient flow and gradient descent in two-layer fully-connected neural networks with leaky ReLU activations when the training data are nearly-orthogonal, a common property of high-dimensional data. For gradient flow, we leverage recent work on the implicit bias for homogeneous neural networks to show that asymptotically, gradient flow produces a neural network with rank at most two. Moreover, this network is an $\ell_2$-max-margin solution (in parameter space), and has a linear decision boundary that corresponds to an approximate-max-margin linear predictor. For gradient descent, provided the random initialization variance is small enough, we show that a single step of gradient descent suffices to drastically reduce the rank of the network, and that the rank remains small throughout training. We provide experiments which suggest that a small initialization scale is important for finding low-rank neural networks with gradient descent.

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