58 research outputs found
Deep Tree Transductions - A Short Survey
The paper surveys recent extensions of the Long-Short Term Memory networks to
handle tree structures from the perspective of learning non-trivial forms of
isomorph structured transductions. It provides a discussion of modern TreeLSTM
models, showing the effect of the bias induced by the direction of tree
processing. An empirical analysis is performed on real-world benchmarks,
highlighting how there is no single model adequate to effectively approach all
transduction problems.Comment: To appear in the Proceedings of the 2019 INNS Big Data and Deep
Learning (INNSBDDL 2019). arXiv admin note: text overlap with
arXiv:1809.0909
Learning Tree Distributions by Hidden Markov Models
Hidden tree Markov models allow learning distributions for tree structured
data while being interpretable as nondeterministic automata. We provide a
concise summary of the main approaches in literature, focusing in particular on
the causality assumptions introduced by the choice of a specific tree visit
direction. We will then sketch a novel non-parametric generalization of the
bottom-up hidden tree Markov model with its interpretation as a
nondeterministic tree automaton with infinite states.Comment: Accepted in LearnAut2018 worksho
Set Aggregation Network as a Trainable Pooling Layer
Global pooling, such as max- or sum-pooling, is one of the key ingredients in
deep neural networks used for processing images, texts, graphs and other types
of structured data. Based on the recent DeepSets architecture proposed by
Zaheer et al. (NIPS 2017), we introduce a Set Aggregation Network (SAN) as an
alternative global pooling layer. In contrast to typical pooling operators, SAN
allows to embed a given set of features to a vector representation of arbitrary
size. We show that by adjusting the size of embedding, SAN is capable of
preserving the whole information from the input. In experiments, we demonstrate
that replacing global pooling layer by SAN leads to the improvement of
classification accuracy. Moreover, it is less prone to overfitting and can be
used as a regularizer.Comment: ICONIP 201
Risk Assessment Algorithms Based On Recursive Neural Networks
The assessment of highly-risky situations at road intersections have been
recently revealed as an important research topic within the context of the
automotive industry. In this paper we shall introduce a novel approach to
compute risk functions by using a combination of a highly non-linear processing
model in conjunction with a powerful information encoding procedure.
Specifically, the elements of information either static or dynamic that appear
in a road intersection scene are encoded by using directed positional acyclic
labeled graphs. The risk assessment problem is then reformulated in terms of an
inductive learning task carried out by a recursive neural network. Recursive
neural networks are connectionist models capable of solving supervised and
non-supervised learning problems represented by directed ordered acyclic
graphs. The potential of this novel approach is demonstrated through well
predefined scenarios. The major difference of our approach compared to others
is expressed by the fact of learning the structure of the risk. Furthermore,
the combination of a rich information encoding procedure with a generalized
model of dynamical recurrent networks permit us, as we shall demonstrate, a
sophisticated processing of information that we believe as being a first step
for building future advanced intersection safety system
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