6,253 research outputs found
On the discriminative power of Hyper-parameters in Cross-Validation and how to choose them
Hyper-parameters tuning is a crucial task to make a model perform at its
best. However, despite the well-established methodologies, some aspects of the
tuning remain unexplored. As an example, it may affect not just accuracy but
also novelty as well as it may depend on the adopted dataset. Moreover,
sometimes it could be sufficient to concentrate on a single parameter only (or
a few of them) instead of their overall set. In this paper we report on our
investigation on hyper-parameters tuning by performing an extensive 10-Folds
Cross-Validation on MovieLens and Amazon Movies for three well-known baselines:
User-kNN, Item-kNN, BPR-MF. We adopted a grid search strategy considering
approximately 15 values for each parameter, and we then evaluated each
combination of parameters in terms of accuracy and novelty. We investigated the
discriminative power of nDCG, Precision, Recall, MRR, EFD, EPC, and, finally,
we analyzed the role of parameters on model evaluation for Cross-Validation.Comment: 5 pages RecSys 201
Domain-Adversarial Training of Neural Networks
We introduce a new representation learning approach for domain adaptation, in
which data at training and test time come from similar but different
distributions. Our approach is directly inspired by the theory on domain
adaptation suggesting that, for effective domain transfer to be achieved,
predictions must be made based on features that cannot discriminate between the
training (source) and test (target) domains. The approach implements this idea
in the context of neural network architectures that are trained on labeled data
from the source domain and unlabeled data from the target domain (no labeled
target-domain data is necessary). As the training progresses, the approach
promotes the emergence of features that are (i) discriminative for the main
learning task on the source domain and (ii) indiscriminate with respect to the
shift between the domains. We show that this adaptation behaviour can be
achieved in almost any feed-forward model by augmenting it with few standard
layers and a new gradient reversal layer. The resulting augmented architecture
can be trained using standard backpropagation and stochastic gradient descent,
and can thus be implemented with little effort using any of the deep learning
packages. We demonstrate the success of our approach for two distinct
classification problems (document sentiment analysis and image classification),
where state-of-the-art domain adaptation performance on standard benchmarks is
achieved. We also validate the approach for descriptor learning task in the
context of person re-identification application.Comment: Published in JMLR: http://jmlr.org/papers/v17/15-239.htm
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