101,218 research outputs found
A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning
and related fields. This review asks the question: how can a classifier learn
from a source domain and generalize to a target domain? We present a
categorization of approaches, divided into, what we refer to as, sample-based,
feature-based and inference-based methods. Sample-based methods focus on
weighting individual observations during training based on their importance to
the target domain. Feature-based methods revolve around on mapping, projecting
and representing features such that a source classifier performs well on the
target domain and inference-based methods incorporate adaptation into the
parameter estimation procedure, for instance through constraints on the
optimization procedure. Additionally, we review a number of conditions that
allow for formulating bounds on the cross-domain generalization error. Our
categorization highlights recurring ideas and raises questions important to
further research.Comment: 20 pages, 5 figure
Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data
It is an enduring question how to combine revealed preference (RP) and stated
preference (SP) data to analyze travel behavior. This study presents a
framework of multitask learning deep neural networks (MTLDNNs) for this
question, and demonstrates that MTLDNNs are more generic than the traditional
nested logit (NL) method, due to its capacity of automatic feature learning and
soft constraints. About 1,500 MTLDNN models are designed and applied to the
survey data that was collected in Singapore and focused on the RP of four
current travel modes and the SP with autonomous vehicles (AV) as the one new
travel mode in addition to those in RP. We found that MTLDNNs consistently
outperform six benchmark models and particularly the classical NL models by
about 5% prediction accuracy in both RP and SP datasets. This performance
improvement can be mainly attributed to the soft constraints specific to
MTLDNNs, including its innovative architectural design and regularization
methods, but not much to the generic capacity of automatic feature learning
endowed by a standard feedforward DNN architecture. Besides prediction, MTLDNNs
are also interpretable. The empirical results show that AV is mainly the
substitute of driving and AV alternative-specific variables are more important
than the socio-economic variables in determining AV adoption. Overall, this
study introduces a new MTLDNN framework to combine RP and SP, and demonstrates
its theoretical flexibility and empirical power for prediction and
interpretation. Future studies can design new MTLDNN architectures to reflect
the speciality of RP and SP and extend this work to other behavioral analysis
Semi-Supervised Single- and Multi-Domain Regression with Multi-Domain Training
We address the problems of multi-domain and single-domain regression based on
distinct and unpaired labeled training sets for each of the domains and a large
unlabeled training set from all domains. We formulate these problems as a
Bayesian estimation with partial knowledge of statistical relations. We propose
a worst-case design strategy and study the resulting estimators. Our analysis
explicitly accounts for the cardinality of the labeled sets and includes the
special cases in which one of the labeled sets is very large or, in the other
extreme, completely missing. We demonstrate our estimators in the context of
removing expressions from facial images and in the context of audio-visual word
recognition, and provide comparisons to several recently proposed multi-modal
learning algorithms.Comment: 24 pages, 6 figures, 2 table
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