42,840 research outputs found
Propagation Kernels
We introduce propagation kernels, a general graph-kernel framework for
efficiently measuring the similarity of structured data. Propagation kernels
are based on monitoring how information spreads through a set of given graphs.
They leverage early-stage distributions from propagation schemes such as random
walks to capture structural information encoded in node labels, attributes, and
edge information. This has two benefits. First, off-the-shelf propagation
schemes can be used to naturally construct kernels for many graph types,
including labeled, partially labeled, unlabeled, directed, and attributed
graphs. Second, by leveraging existing efficient and informative propagation
schemes, propagation kernels can be considerably faster than state-of-the-art
approaches without sacrificing predictive performance. We will also show that
if the graphs at hand have a regular structure, for instance when modeling
image or video data, one can exploit this regularity to scale the kernel
computation to large databases of graphs with thousands of nodes. We support
our contributions by exhaustive experiments on a number of real-world graphs
from a variety of application domains
End-to-End Neural Ad-hoc Ranking with Kernel Pooling
This paper proposes K-NRM, a kernel based neural model for document ranking.
Given a query and a set of documents, K-NRM uses a translation matrix that
models word-level similarities via word embeddings, a new kernel-pooling
technique that uses kernels to extract multi-level soft match features, and a
learning-to-rank layer that combines those features into the final ranking
score. The whole model is trained end-to-end. The ranking layer learns desired
feature patterns from the pairwise ranking loss. The kernels transfer the
feature patterns into soft-match targets at each similarity level and enforce
them on the translation matrix. The word embeddings are tuned accordingly so
that they can produce the desired soft matches. Experiments on a commercial
search engine's query log demonstrate the improvements of K-NRM over prior
feature-based and neural-based states-of-the-art, and explain the source of
K-NRM's advantage: Its kernel-guided embedding encodes a similarity metric
tailored for matching query words to document words, and provides effective
multi-level soft matches
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