pwc-archive/papers-with-abstracts huggingface dataset:
The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
Authors: Patrick Glauner, Jorge Augusto Meira, Petko Valtchev, Radu State, Franck Bettinger
Date: 2016-06-02 00:00:00
Tasks: Electrical Engineering

Detection of non-technical losses (NTL) which include electricity theft,
faulty meters or billing errors has attracted increasing attention from
researchers in electrical engineering and computer science. NTLs cause
significant harm to the economy, as in some countries they may range up to 40%
of the total electricity distributed. The predominant research direction is
employing artificial intelligence to predict whether a customer causes NTL.
This paper first provides an overview of how NTLs are defined and their impact
on economies, which include loss of revenue and profit of electricity providers
and decrease of the stability and reliability of electrical power grids. It
then surveys the state-of-the-art research efforts in a up-to-date and
comprehensive review of algorithms, features and data sets used. It finally
identifies the key scientific and engineering challenges in NTL detection and
suggests how they could be addressed in the future.
Correction of "Cloud Removal By Fusing Multi-Source and Multi-Temporal Images"
Authors: Chengyue Zhang, Zhiwei Li, Qing Cheng, Xinghua Li, Huanfeng Shen
Date: 2017-07-25 00:00:00
Tasks: Cloud Removal

Remote sensing images often suffer from cloud cover. Cloud removal is
required in many applications of remote sensing images. Multitemporal-based
methods are popular and effective to cope with thick clouds. This paper
contributes to a summarization and experimental comparation of the existing
multitemporal-based methods. Furthermore, we propose a spatiotemporal-fusion
with poisson-adjustment method to fuse multi-sensor and multi-temporal images
for cloud removal. The experimental results show that the proposed method has
potential to address the problem of accuracy reduction of cloud removal in
multi-temporal images with significant changes.
Representation Learning on Large and Small Data
Authors: Chun-Nan Chou, Chuen-Kai Shie, Fu-Chieh Chang, Jocelyn Chang, Edward Y. Chang
Date: 2017-07-25 00:00:00
Tasks: Melanoma Diagnosis, Representation Learning, Small Data Image Classification

Deep learning owes its success to three key factors: scale of data, enhanced
models to learn representations from data, and scale of computation. This book
chapter presented the importance of the data-driven approach to learn good
representations from both big data and small data. In terms of big data, it has
been widely accepted in the research community that the more data the better
for both representation and classification improvement. The question is then
how to learn representations from big data, and how to perform representation
learning when data is scarce. We addressed the first question by presenting CNN
model enhancements in the aspects of representation, optimization, and
generalization. To address the small data challenge, we showed transfer
representation learning to be effective. Transfer representation learning
transfers the learned representation from a source domain where abundant
training data is available to a target domain where training data is scarce.
Transfer representation learning gave the OM and melanoma diagnosis modules of
our XPRIZE Tricorder device (which finished $2^{nd}$ out of $310$ competing
teams) a significant boost in diagnosis accuracy.
SAR Target Recognition Using the Multi-aspect-aware Bidirectional LSTM Recurrent Neural Networks
Authors: Fan Zhang, Chen Hu, Qiang Yin, Wei Li, Heng-Chao Li, Wen Hong
Date: 2017-07-25 00:00:00
Tasks: Deep Learning, Dimensionality Reduction

The outstanding pattern recognition performance of deep learning brings new
vitality to the synthetic aperture radar (SAR) automatic target recognition
(ATR). However, there is a limitation in current deep learning based ATR
solution that each learning process only handle one SAR image, namely learning
the static scattering information, while missing the space-varying information.
It is obvious that multi-aspect joint recognition introduced space-varying
scattering information should improve the classification accuracy and
robustness. In this paper, a novel multi-aspect-aware method is proposed to
achieve this idea through the bidirectional Long Short-Term Memory (LSTM)
recurrent neural networks based space-varying scattering information learning.
Specifically, we first select different aspect images to generate the
multi-aspect space-varying image sequences. Then, the Gabor filter and
three-patch local binary pattern (TPLBP) are progressively implemented to
extract a comprehensive spatial features, followed by dimensionality reduction
with the Multi-layer Perceptron (MLP) network. Finally, we design a
bidirectional LSTM recurrent neural network to learn the multi-aspect features
with further integrating the softmax classifier to achieve target recognition.
Experimental results demonstrate that the proposed method can achieve 99.9%
accuracy for 10-class recognition. Besides, its anti-noise and anti-confusion
performance are also better than the conventional deep learning based methods.
Stein Variational Adaptive Importance Sampling
Authors: Jun Han, Qiang Liu
Date: 2017-04-18 00:00:00
Tasks: Diagnostic

We propose a novel adaptive importance sampling algorithm which incorporates
Stein variational gradient decent algorithm (SVGD) with importance sampling
(IS). Our algorithm leverages the nonparametric transforms in SVGD to
iteratively decrease the KL divergence between our importance proposal and the
target distribution. The advantages of this algorithm are twofold: first, our
algorithm turns SVGD into a standard IS algorithm, allowing us to use standard
diagnostic and analytic tools of IS to evaluate and interpret the results;
second, we do not restrict the choice of our importance proposal to predefined
distribution families like traditional (adaptive) IS methods. Empirical
experiments demonstrate that our algorithm performs well on evaluating
partition functions of restricted Boltzmann machines and testing likelihood of
variational auto-encoders.
Exploring the Effectiveness of Convolutional Neural Networks for Answer Selection in End-to-End Question Answering
Authors: Royal Sequiera, Gaurav Baruah, Zhucheng Tu, Salman Mohammed, Jinfeng Rao, Haotian Zhang, Jimmy Lin
Date: 2017-07-25 00:00:00
Tasks: Answer Selection, Question Answering, Retrieval

Most work on natural language question answering today focuses on answer
selection: given a candidate list of sentences, determine which contains the
answer. Although important, answer selection is only one stage in a standard
end-to-end question answering pipeline. This paper explores the effectiveness
of convolutional neural networks (CNNs) for answer selection in an end-to-end
context using the standard TrecQA dataset. We observe that a simple
idf-weighted word overlap algorithm forms a very strong baseline, and that
despite substantial efforts by the community in applying deep learning to
tackle answer selection, the gains are modest at best on this dataset.
Furthermore, it is unclear if a CNN is more effective than the baseline in an
end-to-end context based on standard retrieval metrics. To further explore this
finding, we conducted a manual user evaluation, which confirms that answers
from the CNN are detectably better than those from idf-weighted word overlap.
This result suggests that users are sensitive to relatively small differences
in answer selection quality.
Unsupervised prototype learning in an associative-memory network
Authors: Hui-Ling Zhen, Shang-Nan Wang, Hai-Jun Zhou
Date: 2017-04-10 00:00:00

Unsupervised learning in a generalized Hopfield associative-memory network is
investigated in this work. First, we prove that the (generalized) Hopfield
model is equivalent to a semi-restricted Boltzmann machine with a layer of
visible neurons and another layer of hidden binary neurons, so it could serve
as the building block for a multilayered deep-learning system. We then
demonstrate that the Hopfield network can learn to form a faithful internal
representation of the observed samples, with the learned memory patterns being
prototypes of the input data. Furthermore, we propose a spectral method to
extract a small set of concepts (idealized prototypes) as the most concise
summary or abstraction of the empirical data.
Relational Learning and Feature Extraction by Querying over Heterogeneous Information Networks
Authors: Parisa Kordjamshidi, Sameer Singh, Daniel Khashabi, Christos Christodoulopoulos, Mark Summons, Saurabh Sinha, Dan Roth
Date: 2017-07-25 00:00:00
Tasks: BIG-bench Machine Learning, Knowledge Graphs, Relational Reasoning

Many real world systems need to operate on heterogeneous information networks
that consist of numerous interacting components of different types. Examples
include systems that perform data analysis on biological information networks;
social networks; and information extraction systems processing unstructured
data to convert raw text to knowledge graphs. Many previous works describe
specialized approaches to perform specific types of analysis, mining and
learning on such networks. In this work, we propose a unified framework
consisting of a data model -a graph with a first order schema along with a
declarative language for constructing, querying and manipulating such networks
in ways that facilitate relational and structured machine learning. In
particular, we provide an initial prototype for a relational and graph
traversal query language where queries are directly used as relational features
for structured machine learning models. Feature extraction is performed by
making declarative graph traversal queries. Learning and inference models can
directly operate on this relational representation and augment it with new data
and knowledge that, in turn, is integrated seamlessly into the relational
structure to support new predictions. We demonstrate this system's capabilities
by showcasing tasks in natural language processing and computational biology
domains.
Integrating Lexical and Temporal Signals in Neural Ranking Models for Searching Social Media Streams
Authors: Jinfeng Rao, Hua He, Haotian Zhang, Ferhan Ture, Royal Sequiera, Salman Mohammed, Jimmy Lin
Date: 2017-07-25 00:00:00
Tasks: Density Estimation, Document Ranking

Time is an important relevance signal when searching streams of social media
posts. The distribution of document timestamps from the results of an initial
query can be leveraged to infer the distribution of relevant documents, which
can then be used to rerank the initial results. Previous experiments have shown
that kernel density estimation is a simple yet effective implementation of this
idea. This paper explores an alternative approach to mining temporal signals
with recurrent neural networks. Our intuition is that neural networks provide a
more expressive framework to capture the temporal coherence of neighboring
documents in time. To our knowledge, we are the first to integrate lexical and
temporal signals in an end-to-end neural network architecture, in which
existing neural ranking models are used to generate query-document similarity
vectors that feed into a bidirectional LSTM layer for temporal modeling. Our
results are mixed: existing neural models for document ranking alone yield
limited improvements over simple baselines, but the integration of lexical and
temporal signals yield significant improvements over competitive temporal
baselines.
Deep Feature Learning via Structured Graph Laplacian Embedding for Person Re-Identification
Authors: De Cheng, Yihong Gong, Zhihui Li, Weiwei Shi, Alexander G. Hauptmann, Nanning Zheng
Date: 2017-07-25 00:00:00
Tasks: Person Re-Identification, Triplet

Learning the distance metric between pairs of examples is of great importance
for visual recognition, especially for person re-identification (Re-Id).
Recently, the contrastive and triplet loss are proposed to enhance the
discriminative power of the deeply learned features, and have achieved
remarkable success. As can be seen, either the contrastive or triplet loss is
just one special case of the Euclidean distance relationships among these
training samples. Therefore, we propose a structured graph Laplacian embedding
algorithm, which can formulate all these structured distance relationships into
the graph Laplacian form. The proposed method can take full advantages of the
structured distance relationships among these training samples, with the
constructed complete graph. Besides, this formulation makes our method
easy-to-implement and super-effective. When embedding the proposed algorithm
with the softmax loss for the CNN training, our method can obtain much more
robust and discriminative deep features with inter-personal dispersion and
intra-personal compactness, which is essential to person Re-Id. We illustrate
the effectiveness of our proposed method on top of three popular networks,
namely AlexNet, DGDNet and ResNet50, on recent four widely used Re-Id benchmark
datasets. Our proposed method achieves state-of-the-art performances.
Unite the People: Closing the Loop Between 3D and 2D Human Representations
Authors: Christoph Lassner, Javier Romero, Martin Kiefel, Federica Bogo, Michael J. Black, Peter V. Gehler
Date: 2017-01-10 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D human pose and shape estimation, 3D Human Pose Estimation, Monocular 3D Human Pose Estimation

3D models provide a common ground for different representations of human
bodies. In turn, robust 2D estimation has proven to be a powerful tool to
obtain 3D fits "in-the- wild". However, depending on the level of detail, it
can be hard to impossible to acquire labeled data for training 2D estimators on
large scale. We propose a hybrid approach to this problem: with an extended
version of the recently introduced SMPLify method, we obtain high quality 3D
body model fits for multiple human pose datasets. Human annotators solely sort
good and bad fits. This procedure leads to an initial dataset, UP-3D, with rich
annotations. With a comprehensive set of experiments, we show how this data can
be used to train discriminative models that produce results with an
unprecedented level of detail: our models predict 31 segments and 91 landmark
locations on the body. Using the 91 landmark pose estimator, we present
state-of-the art results for 3D human pose and shape estimation using an order
of magnitude less training data and without assumptions about gender or pose in
the fitting procedure. We show that UP-3D can be enhanced with these improved
fits to grow in quantity and quality, which makes the system deployable on
large scale. The data, code and models are available for research purposes.
Big Data Regression Using Tree Based Segmentation
Authors: Rajiv Sambasivan, Sourish Das
Date: 2017-07-24 00:00:00
Tasks: regression

Scaling regression to large datasets is a common problem in many application
areas. We propose a two step approach to scaling regression to large datasets.
Using a regression tree (CART) to segment the large dataset constitutes the
first step of this approach. The second step of this approach is to develop a
suitable regression model for each segment. Since segment sizes are not very
large, we have the ability to apply sophisticated regression techniques if
required. A nice feature of this two step approach is that it can yield models
that have good explanatory power as well as good predictive performance.
Ensemble methods like Gradient Boosted Trees can offer excellent predictive
performance but may not provide interpretable models. In the experiments
reported in this study, we found that the predictive performance of the
proposed approach matched the predictive performance of Gradient Boosted Trees.
Convolutional Sequence to Sequence Learning
Authors: Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin
Date: 2017-05-08 00:00:00
Proceeding: ICML 2017 8
Tasks: Bangla Spelling Error Correction, CPU, Decoder, GPU, Image Classification, Machine Translation, Translation

The prevalent approach to sequence to sequence learning maps an input
sequence to a variable length output sequence via recurrent neural networks. We
introduce an architecture based entirely on convolutional neural networks.
Compared to recurrent models, computations over all elements can be fully
parallelized during training and optimization is easier since the number of
non-linearities is fixed and independent of the input length. Our use of gated
linear units eases gradient propagation and we equip each decoder layer with a
separate attention module. We outperform the accuracy of the deep LSTM setup of
Wu et al. (2016) on both WMT'14 English-German and WMT'14 English-French
translation at an order of magnitude faster speed, both on GPU and CPU.
A Convolutional Encoder Model for Neural Machine Translation
Authors: Jonas Gehring, Michael Auli, David Grangier, Yann N. Dauphin
Date: 2016-11-07 00:00:00
Proceeding: ACL 2017 7
Tasks: CPU, Machine Translation, model, Sentence, Translation

The prevalent approach to neural machine translation relies on bi-directional
LSTMs to encode the source sentence. In this paper we present a faster and
simpler architecture based on a succession of convolutional layers. This allows
to encode the entire source sentence simultaneously compared to recurrent
networks for which computation is constrained by temporal dependencies. On
WMT'16 English-Romanian translation we achieve competitive accuracy to the
state-of-the-art and we outperform several recently published results on the
WMT'15 English-German task. Our models obtain almost the same accuracy as a
very deep LSTM setup on WMT'14 English-French translation. Our convolutional
encoder speeds up CPU decoding by more than two times at the same or higher
accuracy as a strong bi-directional LSTM baseline.
Comparing Aggregators for Relational Probabilistic Models
Authors: Seyed Mehran Kazemi, Bahare Fatemi, Alexandra Kim, Zilun Peng, Moumita Roy Tora, Xing Zeng, Matthew Dirks, David Poole
Date: 2017-07-25 00:00:00

Relational probabilistic models have the challenge of aggregation, where one
variable depends on a population of other variables. Consider the problem of
predicting gender from movie ratings; this is challenging because the number of
movies per user and users per movie can vary greatly. Surprisingly, aggregation
is not well understood. In this paper, we show that existing relational models
(implicitly or explicitly) either use simple numerical aggregators that lose
great amounts of information, or correspond to naive Bayes, logistic
regression, or noisy-OR that suffer from overconfidence. We propose new simple
aggregators and simple modifications of existing models that empirically
outperform the existing ones. The intuition we provide on different (existing
or new) models and their shortcomings plus our empirical findings promise to
form the foundation for future representations.
Iterative Hard Thresholding for Model Selection in Genome-Wide Association Studies
Authors: Kevin L. Keys, Gary K. Chen, Kenneth Lange
Date: 2016-08-04 00:00:00
Tasks: CPU, Model Selection, regression

A genome-wide association study (GWAS) correlates marker variation with trait
variation in a sample of individuals. Each study subject is genotyped at a
multitude of SNPs (single nucleotide polymorphisms) spanning the genome. Here
we assume that subjects are unrelated and collected at random and that trait
values are normally distributed or transformed to normality. Over the past
decade, researchers have been remarkably successful in applying GWAS analysis
to hundreds of traits. The massive amount of data produced in these studies
present unique computational challenges. Penalized regression with LASSO or MCP
penalties is capable of selecting a handful of associated SNPs from millions of
potential SNPs. Unfortunately, model selection can be corrupted by false
positives and false negatives, obscuring the genetic underpinning of a trait.
This paper introduces the iterative hard thresholding (IHT) algorithm to the
GWAS analysis of continuous traits. Our parallel implementation of IHT
accommodates SNP genotype compression and exploits multiple CPU cores and
graphics processing units (GPUs). This allows statistical geneticists to
leverage commodity desktop computers in GWAS analysis and to avoid
supercomputing. We evaluate IHT performance on both simulated and real GWAS
data and conclude that it reduces false positive and false negative rates while
remaining competitive in computational time with penalized regression. Source
code is freely available at https://github.com/klkeys/IHT.jl.
Clinical Patient Tracking in the Presence of Transient and Permanent Occlusions via Geodesic Feature
Authors: Kun Li, Joel W. Burdick
Date: 2017-07-22 00:00:00

This paper develops a method to use RGB-D cameras to track the motions of a
human spinal cord injury patient undergoing spinal stimulation and physical
rehabilitation. Because clinicians must remain close to the patient during
training sessions, the patient is usually under permanent and transient
occlusions due to the training equipment and the movements of the attending
clinicians. These occlusions can significantly degrade the accuracy of existing
human tracking methods. To improve the data association problem in these
circumstances, we present a new global feature based on the geodesic distances
of surface mesh points to a set of anchor points. Transient occlusions are
handled via a multi-hypothesis tracking framework. To evaluate the method, we
simulated different occlusion sizes on a data set captured from a human in
varying movement patterns, and compared the proposed feature with other
tracking methods. The results show that the proposed method achieves robustness
to both surface deformations and transient occlusions.
An Ensemble Framework for Detecting Community Changes in Dynamic Networks
Authors: Timothy La Fond, Geoffrey Sanders, Christine Klymko, Van Emden Henson
Date: 2017-07-24 00:00:00
Tasks: Clustering, Stochastic Block Model

Dynamic networks, especially those representing social networks, undergo
constant evolution of their community structure over time. Nodes can migrate
between different communities, communities can split into multiple new
communities, communities can merge together, etc. In order to represent dynamic
networks with evolving communities it is essential to use a dynamic model
rather than a static one. Here we use a dynamic stochastic block model where
the underlying block model is different at different times. In order to
represent the structural changes expressed by this dynamic model the network
will be split into discrete time segments and a clustering algorithm will
assign block memberships for each segment. In this paper we show that using an
ensemble of clustering assignments accommodates for the variance in scalable
clustering algorithms and produces superior results in terms of
pairwise-precision and pairwise-recall. We also demonstrate that the dynamic
clustering produced by the ensemble can be visualized as a flowchart which
encapsulates the community evolution succinctly.