8,941 research outputs found
Integrated Node Encoder for Labelled Textual Networks
Voluminous works have been implemented to exploit content-enhanced network
embedding models, with little focus on the labelled information of nodes.
Although TriDNR leverages node labels by treating them as node attributes, it
fails to enrich unlabelled node vectors with the labelled information, which
leads to the weaker classification result on the test set in comparison to
existing unsupervised textual network embedding models. In this study, we
design an integrated node encoder (INE) for textual networks which is jointly
trained on the structure-based and label-based objectives. As a result, the
node encoder preserves the integrated knowledge of not only the network text
and structure, but also the labelled information. Furthermore, INE allows the
creation of label-enhanced vectors for unlabelled nodes by entering their node
contents. Our node embedding achieves state-of-the-art performances in the
classification task on two public citation networks, namely Cora and DBLP,
pushing benchmarks up by 10.0\% and 12.1\%, respectively, with the 70\%
training ratio. Additionally, a feasible solution that generalizes our model
from textual networks to a broader range of networks is proposed.Comment: 7 page
Contextual Motifs: Increasing the Utility of Motifs using Contextual Data
Motifs are a powerful tool for analyzing physiological waveform data.
Standard motif methods, however, ignore important contextual information (e.g.,
what the patient was doing at the time the data were collected). We hypothesize
that these additional contextual data could increase the utility of motifs.
Thus, we propose an extension to motifs, contextual motifs, that incorporates
context. Recognizing that, oftentimes, context may be unobserved or
unavailable, we focus on methods to jointly infer motifs and context. Applied
to both simulated and real physiological data, our proposed approach improves
upon existing motif methods in terms of the discriminative utility of the
discovered motifs. In particular, we discovered contextual motifs in continuous
glucose monitor (CGM) data collected from patients with type 1 diabetes.
Compared to their contextless counterparts, these contextual motifs led to
better predictions of hypo- and hyperglycemic events. Our results suggest that
even when inferred, context is useful in both a long- and short-term prediction
horizon when processing and interpreting physiological waveform data.Comment: 10 pages, 7 figures, accepted for oral presentation at KDD '1
Optical tomography: Image improvement using mixed projection of parallel and fan beam modes
Mixed parallel and fan beam projection is a technique used to increase the quality images. This research focuses on enhancing the image quality in optical tomography. Image quality can be defined by measuring the Peak Signal to Noise Ratio (PSNR) and Normalized Mean Square Error (NMSE) parameters. The findings of this research prove that by combining parallel and fan beam projection, the image quality can be increased by more than 10%in terms of its PSNR value and more than 100% in terms of its NMSE value compared to a single parallel beam
Link Prediction with Mutual Attention for Text-Attributed Networks
In this extended abstract, we present an algorithm that learns a similarity
measure between documents from the network topology of a structured corpus. We
leverage the Scaled Dot-Product Attention, a recently proposed attention
mechanism, to design a mutual attention mechanism between pairs of documents.
To train its parameters, we use the network links as supervision. We provide
preliminary experiment results with a citation dataset on two prediction tasks,
demonstrating the capacity of our model to learn a meaningful textual
similarity.Comment: Added missing referenc
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
Over the past years, distributed semantic representations have proved to be
effective and flexible keepers of prior knowledge to be integrated into
downstream applications. This survey focuses on the representation of meaning.
We start from the theoretical background behind word vector space models and
highlight one of their major limitations: the meaning conflation deficiency,
which arises from representing a word with all its possible meanings as a
single vector. Then, we explain how this deficiency can be addressed through a
transition from the word level to the more fine-grained level of word senses
(in its broader acceptation) as a method for modelling unambiguous lexical
meaning. We present a comprehensive overview of the wide range of techniques in
the two main branches of sense representation, i.e., unsupervised and
knowledge-based. Finally, this survey covers the main evaluation procedures and
applications for this type of representation, and provides an analysis of four
of its important aspects: interpretability, sense granularity, adaptability to
different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence
Researc
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