3 research outputs found
Compact Autoregressive Network
Autoregressive networks can achieve promising performance in many sequence
modeling tasks with short-range dependence. However, when handling
high-dimensional inputs and outputs, the huge amount of parameters in the
network lead to expensive computational cost and low learning efficiency. The
problem can be alleviated slightly by introducing one more narrow hidden layer
to the network, but the sample size required to achieve a certain training
error is still large. To address this challenge, we rearrange the weight
matrices of a linear autoregressive network into a tensor form, and then make
use of Tucker decomposition to represent low-rank structures. This leads to a
novel compact autoregressive network, called Tucker AutoRegressive (TAR) net.
Interestingly, the TAR net can be applied to sequences with long-range
dependence since the dimension along the sequential order is reduced.
Theoretical studies show that the TAR net improves the learning efficiency, and
requires much fewer samples for model training. Experiments on synthetic and
real-world datasets demonstrate the promising performance of the proposed
compact network