3,521 research outputs found
graph2vec: Learning Distributed Representations of Graphs
Recent works on representation learning for graph structured data
predominantly focus on learning distributed representations of graph
substructures such as nodes and subgraphs. However, many graph analytics tasks
such as graph classification and clustering require representing entire graphs
as fixed length feature vectors. While the aforementioned approaches are
naturally unequipped to learn such representations, graph kernels remain as the
most effective way of obtaining them. However, these graph kernels use
handcrafted features (e.g., shortest paths, graphlets, etc.) and hence are
hampered by problems such as poor generalization. To address this limitation,
in this work, we propose a neural embedding framework named graph2vec to learn
data-driven distributed representations of arbitrary sized graphs. graph2vec's
embeddings are learnt in an unsupervised manner and are task agnostic. Hence,
they could be used for any downstream task such as graph classification,
clustering and even seeding supervised representation learning approaches. Our
experiments on several benchmark and large real-world datasets show that
graph2vec achieves significant improvements in classification and clustering
accuracies over substructure representation learning approaches and are
competitive with state-of-the-art graph kernels
A Semi-Supervised and Inductive Embedding Model for Churn Prediction of Large-Scale Mobile Games
Mobile gaming has emerged as a promising market with billion-dollar revenues.
A variety of mobile game platforms and services have been developed around the
world. One critical challenge for these platforms and services is to understand
user churn behavior in mobile games. Accurate churn prediction will benefit
many stakeholders such as game developers, advertisers, and platform operators.
In this paper, we present the first large-scale churn prediction solution for
mobile games. In view of the common limitations of the state-of-the-art methods
built upon traditional machine learning models, we devise a novel
semi-supervised and inductive embedding model that jointly learns the
prediction function and the embedding function for user-app relationships. We
model these two functions by deep neural networks with a unique edge embedding
technique that is able to capture both contextual information and relationship
dynamics. We also design a novel attributed random walk technique that takes
into consideration both topological adjacency and attribute similarities. To
evaluate the performance of our solution, we collect real-world data from the
Samsung Game Launcher platform that includes tens of thousands of games and
hundreds of millions of user-app interactions. The experimental results with
this data demonstrate the superiority of our proposed model against existing
state-of-the-art methods.Comment: to appear in ICDM 201
Exploring Models and Data for Remote Sensing Image Caption Generation
Inspired by recent development of artificial satellite, remote sensing images
have attracted extensive attention. Recently, noticeable progress has been made
in scene classification and target detection.However, it is still not clear how
to describe the remote sensing image content with accurate and concise
sentences. In this paper, we investigate to describe the remote sensing images
with accurate and flexible sentences. First, some annotated instructions are
presented to better describe the remote sensing images considering the special
characteristics of remote sensing images. Second, in order to exhaustively
exploit the contents of remote sensing images, a large-scale aerial image data
set is constructed for remote sensing image caption. Finally, a comprehensive
review is presented on the proposed data set to fully advance the task of
remote sensing caption. Extensive experiments on the proposed data set
demonstrate that the content of the remote sensing image can be completely
described by generating language descriptions. The data set is available at
https://github.com/201528014227051/RSICD_optimalComment: 14 pages, 8 figure
Deep Tree Learning for Zero-shot Face Anti-Spoofing
Face anti-spoofing is designed to keep face recognition systems from
recognizing fake faces as the genuine users. While advanced face anti-spoofing
methods are developed, new types of spoof attacks are also being created and
becoming a threat to all existing systems. We define the detection of unknown
spoof attacks as Zero-Shot Face Anti-spoofing (ZSFA). Previous works of ZSFA
only study 1-2 types of spoof attacks, such as print/replay attacks, which
limits the insight of this problem. In this work, we expand the ZSFA problem to
a wide range of 13 types of spoof attacks, including print attack, replay
attack, 3D mask attacks, and so on. A novel Deep Tree Network (DTN) is proposed
to tackle the ZSFA. The tree is learned to partition the spoof samples into
semantic sub-groups in an unsupervised fashion. When a data sample arrives,
being know or unknown attacks, DTN routes it to the most similar spoof cluster,
and make the binary decision. In addition, to enable the study of ZSFA, we
introduce the first face anti-spoofing database that contains diverse types of
spoof attacks. Experiments show that our proposed method achieves the state of
the art on multiple testing protocols of ZSFA.Comment: To appear at CVPR 2019 as an oral presentatio
SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring
Deep learning has demonstrated tremendous potential for Automatic Text
Scoring (ATS) tasks. In this paper, we describe a new neural architecture that
enhances vanilla neural network models with auxiliary neural coherence
features. Our new method proposes a new \textsc{SkipFlow} mechanism that models
relationships between snapshots of the hidden representations of a long
short-term memory (LSTM) network as it reads. Subsequently, the semantic
relationships between multiple snapshots are used as auxiliary features for
prediction. This has two main benefits. Firstly, essays are typically long
sequences and therefore the memorization capability of the LSTM network may be
insufficient. Implicit access to multiple snapshots can alleviate this problem
by acting as a protection against vanishing gradients. The parameters of the
\textsc{SkipFlow} mechanism also acts as an auxiliary memory. Secondly,
modeling relationships between multiple positions allows our model to learn
features that represent and approximate textual coherence. In our model, we
call this \textit{neural coherence} features. Overall, we present a unified
deep learning architecture that generates neural coherence features as it reads
in an end-to-end fashion. Our approach demonstrates state-of-the-art
performance on the benchmark ASAP dataset, outperforming not only feature
engineering baselines but also other deep learning models.Comment: Accepted to AAAI 201
From handcrafted to deep local features
This paper presents an overview of the evolution of local features from
handcrafted to deep-learning-based methods, followed by a discussion of several
benchmarks and papers evaluating such local features. Our investigations are
motivated by 3D reconstruction problems, where the precise location of the
features is important. As we describe these methods, we highlight and explain
the challenges of feature extraction and potential ways to overcome them. We
first present handcrafted methods, followed by methods based on classical
machine learning and finally we discuss methods based on deep-learning. This
largely chronologically-ordered presentation will help the reader to fully
understand the topic of image and region description in order to make best use
of it in modern computer vision applications. In particular, understanding
handcrafted methods and their motivation can help to understand modern
approaches and how machine learning is used to improve the results. We also
provide references to most of the relevant literature and code.Comment: Preprin
A Process for the Evaluation of Node Embedding Methods in the Context of Node Classification
Node embedding methods find latent lower-dimensional representations which
are used as features in machine learning models. In the last few years, these
methods have become extremely popular as a replacement for manual feature
engineering. Since authors use various approaches for the evaluation of node
embedding methods, existing studies can rarely be efficiently and accurately
compared. We address this issue by developing a process for a fair and
objective evaluation of node embedding procedures w.r.t. node classification.
This process supports researchers and practitioners to compare new and existing
methods in a reproducible way. We apply this process to four popular node
embedding methods and make valuable observations. With an appropriate
combination of hyperparameters, good performance can be achieved even with
embeddings of lower dimensions, which is positive for the run times of the
downstream machine learning task and the embedding algorithm. Multiple
hyperparameter combinations yield similar performance. Thus, no extensive,
time-consuming search is required to achieve reasonable performance in most
cases
Catching Attention with Automatic Pull Quote Selection
Pull quotes are an effective component of a captivating news article. These
spans of text are selected from an article and provided with more salient
presentation, with the aim of attracting readers with intriguing phrases and
making the article more visually interesting. In this paper, we introduce the
novel task of automatic pull quote selection, construct a dataset, and
benchmark the performance of a number of approaches ranging from hand-crafted
features to state-of-the-art sentence embeddings to cross-task models. We show
that pre-trained Sentence-BERT embeddings outperform all other approaches,
however the benefit over n-gram models is marginal. By closely examining the
results of simple models, we also uncover many unexpected properties of pull
quotes that should serve as inspiration for future approaches. We believe the
benefits of exploring this problem further are clear: pull quotes have been
found to increase enjoyment and readability, shape reader perceptions, and
facilitate learning.Comment: 14 pages (11 + 3 for refs), 3 figures, 6 table
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning
Motivation: State-of-the-art biomedical named entity recognition (BioNER)
systems often require handcrafted features specific to each entity type, such
as genes, chemicals and diseases. Although recent studies explored using neural
network models for BioNER to free experts from manual feature engineering, the
performance remains limited by the available training data for each entity
type. Results: We propose a multi-task learning framework for BioNER to
collectively use the training data of different types of entities and improve
the performance on each of them. In experiments on 15 benchmark BioNER
datasets, our multi-task model achieves substantially better performance
compared with state-of-the-art BioNER systems and baseline neural sequence
labeling models. Further analysis shows that the large performance gains come
from sharing character- and word-level information among relevant biomedical
entities across differently labeled corpora.Comment: 7 pages, 4 figure
Deep Steganalysis: End-to-End Learning with Supervisory Information beyond Class Labels
Recently, deep learning has shown its power in steganalysis. However, the
proposed deep models have been often learned from pre-calculated noise
residuals with fixed high-pass filters rather than from raw images. In this
paper, we propose a new end-to-end learning framework that can learn
steganalytic features directly from pixels. In the meantime, the high-pass
filters are also automatically learned. Besides class labels, we make use of
additional pixel level supervision of cover-stego image pair to jointly and
iteratively train the proposed network which consists of a residual calculation
network and a steganalysis network. The experimental results prove the
effectiveness of the proposed architecture
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