9,796 research outputs found
Orthogonal Recurrent Neural Networks and Batch Normalization in Deep Neural Networks
Despite the recent success of various machine learning techniques, there are still numerous obstacles that must be overcome. One obstacle is known as the vanishing/exploding gradient problem. This problem refers to gradients that either become zero or unbounded. This is a well known problem that commonly occurs in Recurrent Neural Networks (RNNs). In this work we describe how this problem can be mitigated, establish three different architectures that are designed to avoid this issue, and derive update schemes for each architecture. Another portion of this work focuses on the often used technique of batch normalization. Although found to be successful in decreasing training times and in preventing overfitting, it is still unknown why this technique works. In this paper we describe batch normalization and provide a potential alternative with the end goal of improving our understanding of how batch normalization works
Complex Unitary Recurrent Neural Networks using Scaled Cayley Transform
Recurrent neural networks (RNNs) have been successfully used on a wide range
of sequential data problems. A well known difficulty in using RNNs is the
\textit{vanishing or exploding gradient} problem. Recently, there have been
several different RNN architectures that try to mitigate this issue by
maintaining an orthogonal or unitary recurrent weight matrix. One such
architecture is the scaled Cayley orthogonal recurrent neural network (scoRNN)
which parameterizes the orthogonal recurrent weight matrix through a scaled
Cayley transform. This parametrization contains a diagonal scaling matrix
consisting of positive or negative one entries that can not be optimized by
gradient descent. Thus the scaling matrix is fixed before training and a
hyperparameter is introduced to tune the matrix for each particular task. In
this paper, we develop a unitary RNN architecture based on a complex scaled
Cayley transform. Unlike the real orthogonal case, the transformation uses a
diagonal scaling matrix consisting of entries on the complex unit circle which
can be optimized using gradient descent and no longer requires the tuning of a
hyperparameter. We also provide an analysis of a potential issue of the modReLU
activiation function which is used in our work and several other unitary RNNs.
In the experiments conducted, the scaled Cayley unitary recurrent neural
network (scuRNN) achieves comparable or better results than scoRNN and other
unitary RNNs without fixing the scaling matrix
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