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Consistency and Variation in Kernel Neural Ranking Model
This paper studies the consistency of the kernel-based neural ranking model
K-NRM, a recent state-of-the-art neural IR model, which is important for
reproducible research and deployment in the industry. We find that K-NRM has
low variance on relevance-based metrics across experimental trials. In spite of
this low variance in overall performance, different trials produce different
document rankings for individual queries. The main source of variance in our
experiments was found to be different latent matching patterns captured by
K-NRM. In the IR-customized word embeddings learned by K-NRM, the
query-document word pairs follow two different matching patterns that are
equally effective, but align word pairs differently in the embedding space. The
different latent matching patterns enable a simple yet effective approach to
construct ensemble rankers, which improve K-NRM's effectiveness and
generalization abilities.Comment: 4 pages, 4 figures, 2 table
Learning to rank from medical imaging data
Medical images can be used to predict a clinical score coding for the
severity of a disease, a pain level or the complexity of a cognitive task. In
all these cases, the predicted variable has a natural order. While a standard
classifier discards this information, we would like to take it into account in
order to improve prediction performance. A standard linear regression does
model such information, however the linearity assumption is likely not be
satisfied when predicting from pixel intensities in an image. In this paper we
address these modeling challenges with a supervised learning procedure where
the model aims to order or rank images. We use a linear model for its
robustness in high dimension and its possible interpretation. We show on
simulations and two fMRI datasets that this approach is able to predict the
correct ordering on pairs of images, yielding higher prediction accuracy than
standard regression and multiclass classification techniques
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