7,427 research outputs found
An Overview of Classifier Fusion Methods
A number of classifier fusion methods have been
recently developed opening an alternative approach
leading to a potential improvement in the
classification performance. As there is little theory of
information fusion itself, currently we are faced with
different methods designed for different problems and
producing different results. This paper gives an
overview of classifier fusion methods and attempts to
identify new trends that may dominate this area of
research in future. A taxonomy of fusion methods
trying to bring some order into the existing “pudding
of diversities” is also provided
A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications
Graph is an important data representation which appears in a wide diversity
of real-world scenarios. Effective graph analytics provides users a deeper
understanding of what is behind the data, and thus can benefit a lot of useful
applications such as node classification, node recommendation, link prediction,
etc. However, most graph analytics methods suffer the high computation and
space cost. Graph embedding is an effective yet efficient way to solve the
graph analytics problem. It converts the graph data into a low dimensional
space in which the graph structural information and graph properties are
maximally preserved. In this survey, we conduct a comprehensive review of the
literature in graph embedding. We first introduce the formal definition of
graph embedding as well as the related concepts. After that, we propose two
taxonomies of graph embedding which correspond to what challenges exist in
different graph embedding problem settings and how the existing work address
these challenges in their solutions. Finally, we summarize the applications
that graph embedding enables and suggest four promising future research
directions in terms of computation efficiency, problem settings, techniques and
application scenarios.Comment: A 20-page comprehensive survey of graph/network embedding for over
150+ papers till year 2018. It provides systematic categorization of
problems, techniques and applications. Accepted by IEEE Transactions on
Knowledge and Data Engineering (TKDE). Comments and suggestions are welcomed
for continuously improving this surve
A Survey of Deep Learning Methods for Relation Extraction
Relation Extraction is an important sub-task of Information Extraction which
has the potential of employing deep learning (DL) models with the creation of
large datasets using distant supervision. In this review, we compare the
contributions and pitfalls of the various DL models that have been used for the
task, to help guide the path ahead
Graph-based Neural Multi-Document Summarization
We propose a neural multi-document summarization (MDS) system that
incorporates sentence relation graphs. We employ a Graph Convolutional Network
(GCN) on the relation graphs, with sentence embeddings obtained from Recurrent
Neural Networks as input node features. Through multiple layer-wise
propagation, the GCN generates high-level hidden sentence features for salience
estimation. We then use a greedy heuristic to extract salient sentences while
avoiding redundancy. In our experiments on DUC 2004, we consider three types of
sentence relation graphs and demonstrate the advantage of combining sentence
relations in graphs with the representation power of deep neural networks. Our
model improves upon traditional graph-based extractive approaches and the
vanilla GRU sequence model with no graph, and it achieves competitive results
against other state-of-the-art multi-document summarization systems.Comment: In CoNLL 201
Hyperspherical Prototype Networks
This paper introduces hyperspherical prototype networks, which unify
classification and regression with prototypes on hyperspherical output spaces.
For classification, a common approach is to define prototypes as the mean
output vector over training examples per class. Here, we propose to use
hyperspheres as output spaces, with class prototypes defined a priori with
large margin separation. We position prototypes through data-independent
optimization, with an extension to incorporate priors from class semantics. By
doing so, we do not require any prototype updating, we can handle any training
size, and the output dimensionality is no longer constrained to the number of
classes. Furthermore, we generalize to regression, by optimizing outputs as an
interpolation between two prototypes on the hypersphere. Since both tasks are
now defined by the same loss function, they can be jointly trained for
multi-task problems. Experimentally, we show the benefit of hyperspherical
prototype networks for classification, regression, and their combination over
other prototype methods, softmax cross-entropy, and mean squared error
approaches.Comment: NeurIPS 201
Collective Learning From Diverse Datasets for Entity Typing in the Wild
Entity typing (ET) is the problem of assigning labels to given entity
mentions in a sentence. Existing works for ET require knowledge about the
domain and target label set for a given test instance. ET in the absence of
such knowledge is a novel problem that we address as ET in the wild. We
hypothesize that the solution to this problem is to build supervised models
that generalize better on the ET task as a whole, rather than a specific
dataset. In this direction, we propose a Collective Learning Framework (CLF),
which enables learning from diverse datasets in a unified way. The CLF first
creates a unified hierarchical label set (UHLS) and a label mapping by
aggregating label information from all available datasets. Then it builds a
single neural network classifier using UHLS, label mapping, and a partial loss
function. The single classifier predicts the finest possible label across all
available domains even though these labels may not be present in any
domain-specific dataset. We also propose a set of evaluation schemes and
metrics to evaluate the performance of models in this novel problem. Extensive
experimentation on seven diverse real-world datasets demonstrates the efficacy
of our CLF.Comment: Accepted at EYRE'19 Workshop, CIKM 201
Large-Scale Cover Song Detection in Digital Music Libraries Using Metadata, Lyrics and Audio Features
Cover song detection is a very relevant task in Music Information Retrieval
(MIR) studies and has been mainly addressed using audio-based systems. Despite
its potential impact in industrial contexts, low performances and lack of
scalability have prevented such systems from being adopted in practice for
large applications. In this work, we investigate whether textual music
information (such as metadata and lyrics) can be used along with audio for
large-scale cover identification problem in a wide digital music library. We
benchmark this problem using standard text and state of the art audio
similarity measures. Our studies shows that these methods can significantly
increase the accuracy and scalability of cover detection systems on Million
Song Dataset (MSD) and Second Hand Song (SHS) datasets. By only leveraging
standard tf-idf based text similarity measures on song titles and lyrics, we
achieved 35.5% of absolute increase in mean average precision compared to the
current scalable audio content-based state of the art methods on MSD. These
experimental results suggests that new methodologies can be encouraged among
researchers to leverage and identify more sophisticated NLP-based techniques to
improve current cover song identification systems in digital music libraries
with metadata.Comment: Music Information Retrieval, Cover Song Identification, Million Song
Dataset, Natural Language Processin
SUBIC: A supervised, structured binary code for image search
For large-scale visual search, highly compressed yet meaningful
representations of images are essential. Structured vector quantizers based on
product quantization and its variants are usually employed to achieve such
compression while minimizing the loss of accuracy. Yet, unlike binary hashing
schemes, these unsupervised methods have not yet benefited from the
supervision, end-to-end learning and novel architectures ushered in by the deep
learning revolution. We hence propose herein a novel method to make deep
convolutional neural networks produce supervised, compact, structured binary
codes for visual search. Our method makes use of a novel block-softmax
non-linearity and of batch-based entropy losses that together induce structure
in the learned encodings. We show that our method outperforms state-of-the-art
compact representations based on deep hashing or structured quantization in
single and cross-domain category retrieval, instance retrieval and
classification. We make our code and models publicly available online.Comment: Accepted at ICCV 2017 (Spotlight
Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs
A visual-relational knowledge graph (KG) is a multi-relational graph whose
entities are associated with images. We explore novel machine learning
approaches for answering visual-relational queries in web-extracted knowledge
graphs. To this end, we have created ImageGraph, a KG with 1,330 relation
types, 14,870 entities, and 829,931 images crawled from the web. With
visual-relational KGs such as ImageGraph one can introduce novel probabilistic
query types in which images are treated as first-class citizens. Both the
prediction of relations between unseen images as well as multi-relational image
retrieval can be expressed with specific families of visual-relational queries.
We introduce novel combinations of convolutional networks and knowledge graph
embedding methods to answer such queries. We also explore a zero-shot learning
scenario where an image of an entirely new entity is linked with multiple
relations to entities of an existing KG. The resulting multi-relational
grounding of unseen entity images into a knowledge graph serves as a semantic
entity representation. We conduct experiments to demonstrate that the proposed
methods can answer these visual-relational queries efficiently and accurately
Corpus-level Fine-grained Entity Typing Using Contextual Information
This paper addresses the problem of corpus-level entity typing, i.e.,
inferring from a large corpus that an entity is a member of a class such as
"food" or "artist". The application of entity typing we are interested in is
knowledge base completion, specifically, to learn which classes an entity is a
member of. We propose FIGMENT to tackle this problem. FIGMENT is
embedding-based and combines (i) a global model that scores based on aggregated
contextual information of an entity and (ii) a context model that first scores
the individual occurrences of an entity and then aggregates the scores. In our
evaluation, FIGMENT strongly outperforms an approach to entity typing that
relies on relations obtained by an open information extraction system.Comment: Accepted at EMNLP2015, Proceedings of the 2015 Conference on
Empirical Methods in Natural Language Processin
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