pwc-archive/papers-with-abstracts huggingface dataset:
Fast Randomized Semi-Supervised Clustering
Authors: Alaa Saade, Florent Krzakala, Marc Lelarge, Lenka Zdeborová
Date: 2016-05-20 00:00:00
Tasks: Clustering, General Classification

We consider the problem of clustering partially labeled data from a minimal
number of randomly chosen pairwise comparisons between the items. We introduce
an efficient local algorithm based on a power iteration of the non-backtracking
operator and study its performance on a simple model. For the case of two
clusters, we give bounds on the classification error and show that a small
error can be achieved from $O(n)$ randomly chosen measurements, where $n$ is
the number of items in the dataset. Our algorithm is therefore efficient both
in terms of time and space complexities. We also investigate numerically the
performance of the algorithm on synthetic and real world data.
Neural Headline Generation with Sentence-wise Optimization
Authors: Ayana, Shiqi Shen, Yu Zhao, Zhiyuan Liu, Maosong Sun
Date: 2016-04-07 00:00:00
Tasks: Headline Generation, Sentence

Recently, neural models have been proposed for headline generation by
learning to map documents to headlines with recurrent neural networks.
Nevertheless, as traditional neural network utilizes maximum likelihood
estimation for parameter optimization, it essentially constrains the expected
training objective within word level rather than sentence level. Moreover, the
performance of model prediction significantly relies on training data
distribution. To overcome these drawbacks, we employ minimum risk training
strategy in this paper, which directly optimizes model parameters in sentence
level with respect to evaluation metrics and leads to significant improvements
for headline generation. Experiment results show that our models outperforms
state-of-the-art systems on both English and Chinese headline generation tasks.
Spatial Relationship Based Features for Indian Sign Language Recognition
Authors: B. M. Chethana Kumara, H. S. Nagendraswamy, R. Lekha Chinmayi
Date: 2016-10-09 00:00:00
Tasks: Sentence, Sign Language Recognition

In this paper, the task of recognizing signs made by hearing impaired people
at sentence level has been addressed. A novel method of extracting spatial
features to capture hand movements of a signer has been proposed. Frames of a
given video of a sign are preprocessed to extract face and hand components of a
signer. The local centroids of the extracted components along with the global
centroid are exploited to extract spatial features. The concept of interval
valued type symbolic data has been explored to capture variations in the same
sign made by the different signers at different instances of time. A suitable
symbolic similarity measure is studied to establish matching between test and
reference signs and a simple nearest neighbour classifier is used to recognize
an unknown sign as one among the known signs by specifying a desired level of
threshold. An extensive experimentation is conducted on a considerably large
database of signs created by us during the course of research work in order to
evaluate the performance of the proposed system
On Deductive Systems of AC Semantics for Rough Sets
Authors: A. Mani
Date: 2016-10-09 00:00:00

Antichain based semantics for general rough sets were introduced recently by
the present author. In her paper two different semantics, one for general rough
sets and another for general approximation spaces over quasi-equivalence
relations, were developed. These semantics are improved and studied further
from a lateral algebraic logic perspective in this research. The main results
concern the structure of the algebras and deductive systems in the context.
Enabling Medical Translation for Low-Resource Languages
Authors: Ahmad Musleh, Nadir Durrani, Irina Temnikova, Preslav Nakov, Stephan Vogel, Osama Alsaad
Date: 2016-10-09 00:00:00
Tasks: Machine Translation, Translation

We present research towards bridging the language gap between migrant workers
in Qatar and medical staff. In particular, we present the first steps towards
the development of a real-world Hindi-English machine translation system for
doctor-patient communication. As this is a low-resource language pair,
especially for speech and for the medical domain, our initial focus has been on
gathering suitable training data from various sources. We applied a variety of
methods ranging from fully automatic extraction from the Web to manual
annotation of test data. Moreover, we developed a method for automatically
augmenting the training data with synthetically generated variants, which
yielded a very sizable improvement of more than 3 BLEU points absolute.
An image compression and encryption scheme based on deep learning
Authors: Fei Hu, Changjiu Pu, Haowei Gao, Mengzi Tang, Li Li
Date: 2016-08-16 00:00:00
Tasks: Deep Learning, Image Compression

Stacked Auto-Encoder (SAE) is a kind of deep learning algorithm for
unsupervised learning. Which has multi layers that project the vector
representation of input data into a lower vector space. These projection
vectors are dense representations of the input data. As a result, SAE can be
used for image compression. Using chaotic logistic map, the compression ones
can further be encrypted. In this study, an application of image compression
and encryption is suggested using SAE and chaotic logistic map. Experiments
show that this application is feasible and effective. It can be used for image
transmission and image protection on internet simultaneously.
Learning Bayesian Networks with Incomplete Data by Augmentation
Authors: Tameem Adel, Cassio P. de Campos
Date: 2016-08-27 00:00:00
Tasks: Data Augmentation, Missing Values

We present new algorithms for learning Bayesian networks from data with
missing values using a data augmentation approach. An exact Bayesian network
learning algorithm is obtained by recasting the problem into a standard
Bayesian network learning problem without missing data. To the best of our
knowledge, this is the first exact algorithm for this problem. As expected, the
exact algorithm does not scale to large domains. We build on the exact method
to create an approximate algorithm using a hill-climbing technique. This
algorithm scales to large domains so long as a suitable standard structure
learning method for complete data is available. We perform a wide range of
experiments to demonstrate the benefits of learning Bayesian networks with such
new approach.
Cornell SPF: Cornell Semantic Parsing Framework
Authors: Yoav Artzi
Date: 2013-11-13 00:00:00
Tasks: Semantic Parsing

The Cornell Semantic Parsing Framework (SPF) is a learning and inference
framework for mapping natural language to formal representation of its meaning.
Crafting GBD-Net for Object Detection
Authors: Xingyu Zeng, Wanli Ouyang, Junjie Yan, Hongsheng Li, Tong Xiao, Kun Wang, Yu Liu, Yucong Zhou, Bin Yang, Zhe Wang, Hui Zhou, Xiaogang Wang
Date: 2016-10-08 00:00:00
Tasks: Object, object-detection, Object Detection

The visual cues from multiple support regions of different sizes and
resolutions are complementary in classifying a candidate box in object
detection. Effective integration of local and contextual visual cues from these
regions has become a fundamental problem in object detection.
  In this paper, we propose a gated bi-directional CNN (GBD-Net) to pass
messages among features from different support regions during both feature
learning and feature extraction. Such message passing can be implemented
through convolution between neighboring support regions in two directions and
can be conducted in various layers. Therefore, local and contextual visual
patterns can validate the existence of each other by learning their nonlinear
relationships and their close interactions are modeled in a more complex way.
It is also shown that message passing is not always helpful but dependent on
individual samples. Gated functions are therefore needed to control message
transmission, whose on-or-offs are controlled by extra visual evidence from the
input sample. The effectiveness of GBD-Net is shown through experiments on
three object detection datasets, ImageNet, Pascal VOC2007 and Microsoft COCO.
This paper also shows the details of our approach in wining the ImageNet object
detection challenge of 2016, with source code provided on
\url{https://github.com/craftGBD/craftGBD}.
A Photometrically Calibrated Benchmark For Monocular Visual Odometry
Authors: Jakob Engel, Vladyslav Usenko, Daniel Cremers
Date: 2016-07-09 00:00:00
Tasks: Monocular Visual Odometry, Visual Odometry

We present a dataset for evaluating the tracking accuracy of monocular visual
odometry and SLAM methods. It contains 50 real-world sequences comprising more
than 100 minutes of video, recorded across dozens of different environments --
ranging from narrow indoor corridors to wide outdoor scenes. All sequences
contain mostly exploring camera motion, starting and ending at the same
position. This allows to evaluate tracking accuracy via the accumulated drift
from start to end, without requiring ground truth for the full sequence. In
contrast to existing datasets, all sequences are photometrically calibrated. We
provide exposure times for each frame as reported by the sensor, the camera
response function, and dense lens attenuation factors. We also propose a novel,
simple approach to non-parametric vignette calibration, which requires minimal
set-up and is easy to reproduce. Finally, we thoroughly evaluate two existing
methods (ORB-SLAM and DSO) on the dataset, including an analysis of the effect
of image resolution, camera field of view, and the camera motion direction.
Mining the Web for Pharmacovigilance: the Case Study of Duloxetine and Venlafaxine
Authors: Abbas Chokor, Abeed Sarker, Graciela Gonzalez
Date: 2016-10-08 00:00:00
Tasks: Pharmacovigilance

Adverse reactions caused by drugs following their release into the market are
among the leading causes of death in many countries. The rapid growth of
electronically available health related information, and the ability to process
large volumes of them automatically, using natural language processing (NLP)
and machine learning algorithms, have opened new opportunities for
pharmacovigilance. Survey found that more than 70% of US Internet users consult
the Internet when they require medical information. In recent years, research
in this area has addressed for Adverse Drug Reaction (ADR) pharmacovigilance
using social media, mainly Twitter and medical forums and websites. This paper
will show the information which can be collected from a variety of Internet
data sources and search engines, mainly Google Trends and Google Correlate.
While considering the case study of two popular Major depressive Disorder (MDD)
drugs, Duloxetine and Venlafaxine, we will provide a comparative analysis for
their reactions using publicly-available alternative data sources.
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Authors: Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey Dean
Date: 2016-09-26 00:00:00
Tasks: Decoder, Machine Translation, NMT, Sentence, Translation

Neural Machine Translation (NMT) is an end-to-end learning approach for
automated translation, with the potential to overcome many of the weaknesses of
conventional phrase-based translation systems. Unfortunately, NMT systems are
known to be computationally expensive both in training and in translation
inference. Also, most NMT systems have difficulty with rare words. These issues
have hindered NMT's use in practical deployments and services, where both
accuracy and speed are essential. In this work, we present GNMT, Google's
Neural Machine Translation system, which attempts to address many of these
issues. Our model consists of a deep LSTM network with 8 encoder and 8 decoder
layers using attention and residual connections. To improve parallelism and
therefore decrease training time, our attention mechanism connects the bottom
layer of the decoder to the top layer of the encoder. To accelerate the final
translation speed, we employ low-precision arithmetic during inference
computations. To improve handling of rare words, we divide words into a limited
set of common sub-word units ("wordpieces") for both input and output. This
method provides a good balance between the flexibility of "character"-delimited
models and the efficiency of "word"-delimited models, naturally handles
translation of rare words, and ultimately improves the overall accuracy of the
system. Our beam search technique employs a length-normalization procedure and
uses a coverage penalty, which encourages generation of an output sentence that
is most likely to cover all the words in the source sentence. On the WMT'14
English-to-French and English-to-German benchmarks, GNMT achieves competitive
results to state-of-the-art. Using a human side-by-side evaluation on a set of
isolated simple sentences, it reduces translation errors by an average of 60%
compared to Google's phrase-based production system.
Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring
Authors: Konstantinos Georgatzis, Christopher K. I. Williams, Christopher Hawthorne
Date: 2016-07-31 00:00:00

We present a non-linear dynamical system for modelling the effect of drug
infusions on the vital signs of patients admitted in Intensive Care Units
(ICUs). More specifically we are interested in modelling the effect of a widely
used anaesthetic drug (Propofol) on a patient's monitored depth of anaesthesia
and haemodynamics. We compare our approach with one from the
Pharmacokinetics/Pharmacodynamics (PK/PD) literature and show that we can
provide significant improvements in performance without requiring the
incorporation of expert physiological knowledge in our system.
Computational linking theory
Authors: Aaron Steven White, Drew Reisinger, Rachel Rudinger, Kyle Rawlins, Benjamin Van Durme
Date: 2016-10-08 00:00:00
Tasks: Semantic Parsing, Semantic Role Labeling

A linking theory explains how verbs' semantic arguments are mapped to their
syntactic arguments---the inverse of the Semantic Role Labeling task from the
shallow semantic parsing literature. In this paper, we develop the
Computational Linking Theory framework as a method for implementing and testing
linking theories proposed in the theoretical literature. We deploy this
framework to assess two cross-cutting types of linking theory: local v. global
models and categorical v. featural models. To further investigate the behavior
of these models, we develop a measurement model in the spirit of previous work
in semantic role induction: the Semantic Proto-Role Linking Model. We use this
model, which implements a generalization of Dowty's seminal Proto-Role Theory,
to induce semantic proto-roles, which we compare to those Dowty proposes.
Empirical Evaluation of RNN Architectures on Sentence Classification Task
Authors: Lei Shen, Junlin Zhang
Date: 2016-09-29 00:00:00
Tasks: Classification, General Classification, Sentence, Sentence Classification

Recurrent Neural Networks have achieved state-of-the-art results for many
problems in NLP and two most popular RNN architectures are Tail Model and
Pooling Model. In this paper, a hybrid architecture is proposed and we present
the first empirical study using LSTMs to compare performance of the three RNN
structures on sentence classification task. Experimental results show that the
Max Pooling Model or Hybrid Max Pooling Model achieves the best performance on
most datasets, while Tail Model does not outperform other models.
Federated Optimization: Distributed Machine Learning for On-Device Intelligence
Authors: Jakub Konečný, H. Brendan McMahan, Daniel Ramage, Peter Richtárik
Date: 2016-10-08 00:00:00
Tasks: BIG-bench Machine Learning, Distributed Optimization

We introduce a new and increasingly relevant setting for distributed
optimization in machine learning, where the data defining the optimization are
unevenly distributed over an extremely large number of nodes. The goal is to
train a high-quality centralized model. We refer to this setting as Federated
Optimization. In this setting, communication efficiency is of the utmost
importance and minimizing the number of rounds of communication is the
principal goal.
  A motivating example arises when we keep the training data locally on users'
mobile devices instead of logging it to a data center for training. In
federated optimziation, the devices are used as compute nodes performing
computation on their local data in order to update a global model. We suppose
that we have extremely large number of devices in the network --- as many as
the number of users of a given service, each of which has only a tiny fraction
of the total data available. In particular, we expect the number of data points
available locally to be much smaller than the number of devices. Additionally,
since different users generate data with different patterns, it is reasonable
to assume that no device has a representative sample of the overall
distribution.
  We show that existing algorithms are not suitable for this setting, and
propose a new algorithm which shows encouraging experimental results for sparse
convex problems. This work also sets a path for future research needed in the
context of \federated optimization.
Dimension Projection among Languages based on Pseudo-relevant Documents for Query Translation
Authors: Javid Dadashkarimi, Mahsa S. Shahshahani, Amirhossein Tebbifakhr, Heshaam Faili, Azadeh Shakery
Date: 2016-05-25 00:00:00
Tasks: Information Retrieval, Machine Translation, Retrieval, Translation

Using top-ranked documents in response to a query has been shown to be an
effective approach to improve the quality of query translation in
dictionary-based cross-language information retrieval. In this paper, we
propose a new method for dictionary-based query translation based on dimension
projection of embedded vectors from the pseudo-relevant documents in the source
language to their equivalents in the target language. To this end, first we
learn low-dimensional vectors of the words in the pseudo-relevant collections
separately and then aim to find a query-dependent transformation matrix between
the vectors of translation pairs appeared in the collections. At the next step,
representation of each query term is projected to the target language and then,
after using a softmax function, a query-dependent translation model is built.
Finally, the model is used for query translation. Our experiments on four CLEF
collections in French, Spanish, German, and Italian demonstrate that the
proposed method outperforms a word embedding baseline based on bilingual
shuffling and a further number of competitive baselines. The proposed method
reaches up to 87% performance of machine translation (MT) in short queries and
considerable improvements in verbose queries.
Content-Based Image Retrieval Using Multiresolution Analysis Of Shape-Based Classified Images
Authors: I. M. El-Henawy, Kareem Ahmed
Date: 2016-10-08 00:00:00
Tasks: Content-Based Image Retrieval, Descriptive, Image Retrieval, Retrieval

Content-Based Image Retrieval (CBIR) systems have been widely used for a wide
range of applications such as Art collections, Crime prevention and
Intellectual property. In this paper, a novel CBIR system, which utilizes
visual contents (color, texture and shape) of an image to retrieve images, is
proposed. The proposed system builds three feature vectors and stores them into
MySQL database. The first feature vector uses descriptive statistics to
describe the distribution of data in each channel of RGB channels of the image.
The second feature vector describes the texture using eigenvalues of the 39
sub-bands that are generated after applying four levels 2D DWT in each channel
(red, green and blue channels) of the image. These wavelets sub-bands perfectly
describes the horizontal, vertical and diagonal edges that exist in the
multi-resolution analysis of the image. The third feature vector describes the
basic shapes that exist in the skeletonization version of the black and white
representation of the image. Experimental results on a private MYSQL database
that consists of 10000 images, using color, texture, shape and stored relevance
feedbacks, showed 96.4% average correct retrieval rate in an efficient recovery
time.
Revisiting Multiple Instance Neural Networks
Authors: Xinggang Wang, Yongluan Yan, Peng Tang, Xiang Bai, Wenyu Liu
Date: 2016-10-08 00:00:00
Tasks: CPU, Multiple Instance Learning, Weakly-supervised Learning

Recently neural networks and multiple instance learning are both attractive
topics in Artificial Intelligence related research fields. Deep neural networks
have achieved great success in supervised learning problems, and multiple
instance learning as a typical weakly-supervised learning method is effective
for many applications in computer vision, biometrics, nature language
processing, etc. In this paper, we revisit the problem of solving multiple
instance learning problems using neural networks. Neural networks are appealing
for solving multiple instance learning problem. The multiple instance neural
networks perform multiple instance learning in an end-to-end way, which take a
bag with various number of instances as input and directly output bag label.
All of the parameters in a multiple instance network are able to be optimized
via back-propagation. We propose a new multiple instance neural network to
learn bag representations, which is different from the existing multiple
instance neural networks that focus on estimating instance label. In addition,
recent tricks developed in deep learning have been studied in multiple instance
networks, we find deep supervision is effective for boosting bag classification
accuracy. In the experiments, the proposed multiple instance networks achieve
state-of-the-art or competitive performance on several MIL benchmarks.
Moreover, it is extremely fast for both testing and training, e.g., it takes
only 0.0003 second to predict a bag and a few seconds to train on a MIL
datasets on a moderate CPU.
Attribute2Image: Conditional Image Generation from Visual Attributes
Authors: Xinchen Yan, Jimei Yang, Kihyuk Sohn, Honglak Lee
Date: 2015-12-02 00:00:00
Tasks: Attribute, Conditional Image Generation, Image Generation, Image Reconstruction

This paper investigates a novel problem of generating images from visual
attributes. We model the image as a composite of foreground and background and
develop a layered generative model with disentangled latent variables that can
be learned end-to-end using a variational auto-encoder. We experiment with
natural images of faces and birds and demonstrate that the proposed models are
capable of generating realistic and diverse samples with disentangled latent
representations. We use a general energy minimization algorithm for posterior
inference of latent variables given novel images. Therefore, the learned
generative models show excellent quantitative and visual results in the tasks
of attribute-conditioned image reconstruction and completion.
A Semantic Analyzer for the Comprehension of the Spontaneous Arabic Speech
Authors: Mourad Mars, Mounir Zrigui, Mohamed Belgacem, Anis Zouaghi
Date: 2016-10-08 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Integration, Decoder, speech-recognition, Speech Recognition, Translation

This work is part of a large research project entitled "Or\'eodule" aimed at
developing tools for automatic speech recognition, translation, and synthesis
for Arabic language. Our attention has mainly been focused on an attempt to
improve the probabilistic model on which our semantic decoder is based. To
achieve this goal, we have decided to test the influence of the pertinent
context use, and of the contextual data integration of different types, on the
effectiveness of the semantic decoder. The findings are quite satisfactory.