20,514 research outputs found
Regression with Linear Factored Functions
Many applications that use empirically estimated functions face a curse of
dimensionality, because the integrals over most function classes must be
approximated by sampling. This paper introduces a novel regression-algorithm
that learns linear factored functions (LFF). This class of functions has
structural properties that allow to analytically solve certain integrals and to
calculate point-wise products. Applications like belief propagation and
reinforcement learning can exploit these properties to break the curse and
speed up computation. We derive a regularized greedy optimization scheme, that
learns factored basis functions during training. The novel regression algorithm
performs competitively to Gaussian processes on benchmark tasks, and the
learned LFF functions are with 4-9 factored basis functions on average very
compact.Comment: Under review as conference paper at ECML/PKDD 201
Predicting Parameters in Deep Learning
We demonstrate that there is significant redundancy in the parameterization
of several deep learning models. Given only a few weight values for each
feature it is possible to accurately predict the remaining values. Moreover, we
show that not only can the parameter values be predicted, but many of them need
not be learned at all. We train several different architectures by learning
only a small number of weights and predicting the rest. In the best case we are
able to predict more than 95% of the weights of a network without any drop in
accuracy
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