33,134 research outputs found
Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction
Labeled sequence transduction is a task of transforming one sequence into
another sequence that satisfies desiderata specified by a set of labels. In
this paper we propose multi-space variational encoder-decoders, a new model for
labeled sequence transduction with semi-supervised learning. The generative
model can use neural networks to handle both discrete and continuous latent
variables to exploit various features of data. Experiments show that our model
provides not only a powerful supervised framework but also can effectively take
advantage of the unlabeled data. On the SIGMORPHON morphological inflection
benchmark, our model outperforms single-model state-of-art results by a large
margin for the majority of languages.Comment: Accepted by ACL 201
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
Unsupervised Domain Adaptation using Graph Transduction Games
Unsupervised domain adaptation (UDA) amounts to assigning class labels to the
unlabeled instances of a dataset from a target domain, using labeled instances
of a dataset from a related source domain. In this paper, we propose to cast
this problem in a game-theoretic setting as a non-cooperative game and
introduce a fully automatized iterative algorithm for UDA based on graph
transduction games (GTG). The main advantages of this approach are its
principled foundation, guaranteed termination of the iterative algorithms to a
Nash equilibrium (which corresponds to a consistent labeling condition) and
soft labels quantifying the uncertainty of the label assignment process. We
also investigate the beneficial effect of using pseudo-labels from linear
classifiers to initialize the iterative process. The performance of the
resulting methods is assessed on publicly available object recognition
benchmark datasets involving both shallow and deep features. Results of
experiments demonstrate the suitability of the proposed game-theoretic approach
for solving UDA tasks.Comment: Oral IJCNN 201
Ancient Coin Classification Using Graph Transduction Games
Recognizing the type of an ancient coin requires theoretical expertise and
years of experience in the field of numismatics. Our goal in this work is
automatizing this time consuming and demanding task by a visual classification
framework. Specifically, we propose to model ancient coin image classification
using Graph Transduction Games (GTG). GTG casts the classification problem as a
non-cooperative game where the players (the coin images) decide their
strategies (class labels) according to the choices made by the others, which
results with a global consensus at the final labeling. Experiments are
conducted on the only publicly available dataset which is composed of 180
images of 60 types of Roman coins. We demonstrate that our approach outperforms
the literature work on the same dataset with the classification accuracy of
73.6% and 87.3% when there are one and two images per class in the training
set, respectively
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