126,351 research outputs found
Foothill: A Quasiconvex Regularization for Edge Computing of Deep Neural Networks
Deep neural networks (DNNs) have demonstrated success for many supervised
learning tasks, ranging from voice recognition, object detection, to image
classification. However, their increasing complexity might yield poor
generalization error that make them hard to be deployed on edge devices.
Quantization is an effective approach to compress DNNs in order to meet these
constraints. Using a quasiconvex base function in order to construct a binary
quantizer helps training binary neural networks (BNNs) and adding noise to the
input data or using a concrete regularization function helps to improve
generalization error. Here we introduce foothill function, an infinitely
differentiable quasiconvex function. This regularizer is flexible enough to
deform towards and penalties. Foothill can be used as a binary
quantizer, as a regularizer, or as a loss. In particular, we show this
regularizer reduces the accuracy gap between BNNs and their full-precision
counterpart for image classification on ImageNet.Comment: Accepted in 16th International Conference of Image Analysis and
Recognition (ICIAR 2019
On the Analysis of Trajectories of Gradient Descent in the Optimization of Deep Neural Networks
Theoretical analysis of the error landscape of deep neural networks has
garnered significant interest in recent years. In this work, we theoretically
study the importance of noise in the trajectories of gradient descent towards
optimal solutions in multi-layer neural networks. We show that adding noise (in
different ways) to a neural network while training increases the rank of the
product of weight matrices of a multi-layer linear neural network. We thus
study how adding noise can assist reaching a global optimum when the product
matrix is full-rank (under certain conditions). We establish theoretical
foundations between the noise induced into the neural network - either to the
gradient, to the architecture, or to the input/output to a neural network - and
the rank of product of weight matrices. We corroborate our theoretical findings
with empirical results.Comment: 4 pages + 1 figure (main, excluding references), 5 pages + 4 figures
(appendix
- …