pwc-archive/papers-with-abstracts huggingface dataset:
Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog
Authors: Satwik Kottur, José M. F. Moura, Stefan Lee, Dhruv Batra
Date: 2017-06-26 00:00:00

A number of recent works have proposed techniques for end-to-end learning of
communication protocols among cooperative multi-agent populations, and have
simultaneously found the emergence of grounded human-interpretable language in
the protocols developed by the agents, all learned without any human
supervision!
  In this paper, using a Task and Tell reference game between two agents as a
testbed, we present a sequence of 'negative' results culminating in a
'positive' one -- showing that while most agent-invented languages are
effective (i.e. achieve near-perfect task rewards), they are decidedly not
interpretable or compositional.
  In essence, we find that natural language does not emerge 'naturally',
despite the semblance of ease of natural-language-emergence that one may gather
from recent literature. We discuss how it is possible to coax the invented
languages to become more and more human-like and compositional by increasing
restrictions on how two agents may communicate.
Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method
Authors: Haoyuan Hu, Xiaodong Zhang, Xiaowei Yan, Longfei Wang, Yinghui Xu
Date: 2017-08-20 00:00:00
Tasks: 3D Bin Packing, Combinatorial Optimization, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, a new type of 3D bin packing problem (BPP) is proposed, in
which a number of cuboid-shaped items must be put into a bin one by one
orthogonally. The objective is to find a way to place these items that can
minimize the surface area of the bin. This problem is based on the fact that
there is no fixed-sized bin in many real business scenarios and the cost of a
bin is proportional to its surface area. Our research shows that this problem
is NP-hard. Based on previous research on 3D BPP, the surface area is
determined by the sequence, spatial locations and orientations of items. Among
these factors, the sequence of items plays a key role in minimizing the surface
area. Inspired by recent achievements of deep reinforcement learning (DRL)
techniques, especially Pointer Network, on combinatorial optimization problems
such as TSP, a DRL-based method is applied to optimize the sequence of items to
be packed into the bin. Numerical results show that the method proposed in this
paper achieve about 5% improvement than heuristic method.
Dual-fisheye lens stitching for 360-degree imaging
Authors: Tuan Ho, Madhukar Budagavi
Date: 2017-08-20 00:00:00

Dual-fisheye lens cameras have been increasingly used for 360-degree
immersive imaging. However, the limited overlapping field of views and
misalignment between the two lenses give rise to visible discontinuities in the
stitching boundaries. This paper introduces a novel method for dual-fisheye
camera stitching that adaptively minimizes the discontinuities in the
overlapping regions to generate full spherical 360-degree images. Results show
that this approach can produce good quality stitched images for Samsung Gear
360 -- a dual-fisheye camera, even with hard-to-stitch objects in the stitching
borders.
Neural Paraphrase Identification of Questions with Noisy Pretraining
Authors: Gaurav Singh Tomar, Thyago Duque, Oscar Täckström, Jakob Uszkoreit, Dipanjan Das
Date: 2017-04-15 00:00:00
Proceeding: WS 2017 9
Tasks: Paraphrase Identification

We present a solution to the problem of paraphrase identification of
questions. We focus on a recent dataset of question pairs annotated with binary
paraphrase labels and show that a variant of the decomposable attention model
(Parikh et al., 2016) results in accurate performance on this task, while being
far simpler than many competing neural architectures. Furthermore, when the
model is pretrained on a noisy dataset of automatically collected question
paraphrases, it obtains the best reported performance on the dataset.
Electricity Theft Detection using Machine Learning
Authors: Niklas Dahringer
Date: 2017-08-19 00:00:00
Tasks: BIG-bench Machine Learning

Non-technical losses (NTL) in electric power grids arise through electricity
theft, broken electric meters or billing errors. They can harm the power
supplier as well as the whole economy of a country through losses of up to 40%
of the total power distribution. For NTL detection, researchers use artificial
intelligence to analyse data. This work is about improving the extraction of
more meaningful features from a data set. With these features, the prediction
quality will increase.
Action Recognition with Dynamic Image Networks
Authors: Hakan Bilen, Basura Fernando, Efstratios Gavves, Andrea Vedaldi
Date: 2016-12-02 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization

We introduce the concept of "dynamic image", a novel compact representation
of videos useful for video analysis, particularly in combination with
convolutional neural networks (CNNs). A dynamic image encodes temporal data
such as RGB or optical flow videos by using the concept of `rank pooling'. The
idea is to learn a ranking machine that captures the temporal evolution of the
data and to use the parameters of the latter as a representation. When a linear
ranking machine is used, the resulting representation is in the form of an
image, which we call dynamic because it summarizes the video dynamics in
addition of appearance. This is a powerful idea because it allows to convert
any video to an image so that existing CNN models pre-trained for the analysis
of still images can be immediately extended to videos. We also present an
efficient and effective approximate rank pooling operator, accelerating
standard rank pooling algorithms by orders of magnitude, and formulate that as
a CNN layer. This new layer allows generalizing dynamic images to dynamic
feature maps. We demonstrate the power of the new representations on standard
benchmarks in action recognition achieving state-of-the-art performance.
Using Global Constraints and Reranking to Improve Cognates Detection
Authors: Michael Bloodgood, Benjamin Strauss
Date: 2017-04-24 00:00:00
Proceeding: ACL 2017 7
Tasks: Reranking

Global constraints and reranking have not been used in cognates detection
research to date. We propose methods for using global constraints by performing
rescoring of the score matrices produced by state of the art cognates detection
systems. Using global constraints to perform rescoring is complementary to
state of the art methods for performing cognates detection and results in
significant performance improvements beyond current state of the art
performance on publicly available datasets with different language pairs and
various conditions such as different levels of baseline state of the art
performance and different data size conditions, including with more realistic
large data size conditions than have been evaluated with in the past.
Magnetic Hamiltonian Monte Carlo
Authors: Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard Turner
Date: 2016-07-10 00:00:00
Proceeding: ICML 2017 8

Hamiltonian Monte Carlo (HMC) exploits Hamiltonian dynamics to construct
efficient proposals for Markov chain Monte Carlo (MCMC). In this paper, we
present a generalization of HMC which exploits \textit{non-canonical}
Hamiltonian dynamics. We refer to this algorithm as magnetic HMC, since in 3
dimensions a subset of the dynamics map onto the mechanics of a charged
particle coupled to a magnetic field. We establish a theoretical basis for the
use of non-canonical Hamiltonian dynamics in MCMC, and construct a symplectic,
leapfrog-like integrator allowing for the implementation of magnetic HMC.
Finally, we exhibit several examples where these non-canonical dynamics can
lead to improved mixing of magnetic HMC relative to ordinary HMC.
An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection
Authors: Joseph Futoma, Sanjay Hariharan, Mark Sendak, Nathan Brajer, Meredith Clement, Armando Bedoya, Cara O'Brien, Katherine Heller
Date: 2017-08-19 00:00:00
Tasks: Gaussian Processes, Missing Values, Time Series Analysis

Sepsis is a poorly understood and potentially life-threatening complication
that can occur as a result of infection. Early detection and treatment improves
patient outcomes, and as such it poses an important challenge in medicine. In
this work, we develop a flexible classifier that leverages streaming lab
results, vitals, and medications to predict sepsis before it occurs. We model
patient clinical time series with multi-output Gaussian processes, maintaining
uncertainty about the physiological state of a patient while also imputing
missing values. The mean function takes into account the effects of medications
administered on the trajectories of the physiological variables. Latent
function values from the Gaussian process are then fed into a deep recurrent
neural network to classify patient encounters as septic or not, and the overall
model is trained end-to-end using back-propagation. We train and validate our
model on a large dataset of 18 months of heterogeneous inpatient stays from the
Duke University Health System, and develop a new "real-time" validation scheme
for simulating the performance of our model as it will actually be used. Our
proposed method substantially outperforms clinical baselines, and improves on a
previous related model for detecting sepsis. Our model's predictions will be
displayed in a real-time analytics dashboard to be used by a sepsis rapid
response team to help detect and improve treatment of sepsis.
Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVM
Authors: Mohamed Eldesouki, Younes Samih, Ahmed Abdelali, Mohammed Attia, Hamdy Mubarak, Kareem Darwish, Kallmeyer Laura
Date: 2017-08-19 00:00:00
Tasks: Domain Adaptation, Information Retrieval, Machine Translation, Retrieval, Segmentation, Sentiment Analysis, Translation

Arabic word segmentation is essential for a variety of NLP applications such
as machine translation and information retrieval. Segmentation entails breaking
words into their constituent stems, affixes and clitics. In this paper, we
compare two approaches for segmenting four major Arabic dialects using only
several thousand training examples for each dialect. The two approaches involve
posing the problem as a ranking problem, where an SVM ranker picks the best
segmentation, and as a sequence labeling problem, where a bi-LSTM RNN coupled
with CRF determines where best to segment words. We are able to achieve solid
segmentation results for all dialects using rather limited training data. We
also show that employing Modern Standard Arabic data for domain adaptation and
assuming context independence improve overall results.
A rule based algorithm for detecting negative words in Persian
Authors: Reza Takhshid, Adel Rahimi
Date: 2017-08-19 00:00:00

In this paper, we present a novel method for detecting negative words in
Persian. We first used an algorithm to an exceptions list which was later
modified by hand. We then used the mentioned lists and a Persian polarity
corpus in our rule based algorithm to detect negative words.
Efficient and Adaptive Linear Regression in Semi-Supervised Settings
Authors: Abhishek Chakrabortty, Tianxi Cai
Date: 2017-01-17 00:00:00
Tasks: Imputation, regression

We consider the linear regression problem under semi-supervised settings
wherein the available data typically consists of: (i) a small or moderate sized
'labeled' data, and (ii) a much larger sized 'unlabeled' data. Such data arises
naturally from settings where the outcome, unlike the covariates, is expensive
to obtain, a frequent scenario in modern studies involving large databases like
electronic medical records (EMR). Supervised estimators like the ordinary least
squares (OLS) estimator utilize only the labeled data. It is often of interest
to investigate if and when the unlabeled data can be exploited to improve
estimation of the regression parameter in the adopted linear model.
  In this paper, we propose a class of 'Efficient and Adaptive Semi-Supervised
Estimators' (EASE) to improve estimation efficiency. The EASE are two-step
estimators adaptive to model mis-specification, leading to improved (optimal in
some cases) efficiency under model mis-specification, and equal (optimal)
efficiency under a linear model. This adaptive property, often unaddressed in
the existing literature, is crucial for advocating 'safe' use of the unlabeled
data. The construction of EASE primarily involves a flexible
'semi-non-parametric' imputation, including a smoothing step that works well
even when the number of covariates is not small; and a follow up 'refitting'
step along with a cross-validation (CV) strategy both of which have useful
practical as well as theoretical implications towards addressing two important
issues: under-smoothing and over-fitting. We establish asymptotic results
including consistency, asymptotic normality and the adaptive properties of
EASE. We also provide influence function expansions and a 'double' CV strategy
for inference. The results are further validated through extensive simulations,
followed by application to an EMR study on auto-immunity.
Surrogate Aided Unsupervised Recovery of Sparse Signals in Single Index Models for Binary Outcomes
Authors: Abhishek Chakrabortty, Matey Neykov, Raymond Carroll, Tianxi Cai
Date: 2017-01-18 00:00:00
Tasks: Diagnostic

We consider the recovery of regression coefficients, denoted by
$\boldsymbol{\beta}_0$, for a single index model (SIM) relating a binary
outcome $Y$ to a set of possibly high dimensional covariates $\boldsymbol{X}$,
based on a large but 'unlabeled' dataset $\mathcal{U}$, with $Y$ never
observed. On $\mathcal{U}$, we fully observe $\boldsymbol{X}$ and additionally,
a surrogate $S$ which, while not being strongly predictive of $Y$ throughout
the entirety of its support, can forecast it with high accuracy when it assumes
extreme values. Such datasets arise naturally in modern studies involving large
databases such as electronic medical records (EMR) where $Y$, unlike
$(\boldsymbol{X}, S)$, is difficult and/or expensive to obtain. In EMR studies,
an example of $Y$ and $S$ would be the true disease phenotype and the count of
the associated diagnostic codes respectively. Assuming another SIM for $S$
given $\boldsymbol{X}$, we show that under sparsity assumptions, we can recover
$\boldsymbol{\beta}_0$ proportionally by simply fitting a least squares LASSO
estimator to the subset of the observed data on $(\boldsymbol{X}, S)$
restricted to the extreme sets of $S$, with $Y$ imputed using the surrogacy of
$S$. We obtain sharp finite sample performance bounds for our estimator,
including deterministic deviation bounds and probabilistic guarantees. We
demonstrate the effectiveness of our approach through multiple simulation
studies, as well as by application to real data from an EMR study conducted at
the Partners HealthCare Systems.
A novel agent-based simulation framework for sensing in complex adaptive environments
Authors: Muaz A. Niazi, Amir Hussain
Date: 2017-08-19 00:00:00

In this paper we present a novel Formal Agent-Based Simulation framework
(FABS). FABS uses formal specification as a means of clear description of
wireless sensor networks (WSN) sensing a Complex Adaptive Environment. This
specification model is then used to develop an agent-based model of both the
wireless sensor network as well as the environment. As proof of concept, we
demonstrate the application of FABS to a boids model of self-organized flocking
of animals monitored by a random deployment of proximity sensors.
What Drives the International Development Agenda? An NLP Analysis of the United Nations General Debate 1970-2016
Authors: Alexander Baturo, Niheer Dasandi, Slava J. Mikhaylov
Date: 2017-08-19 00:00:00

There is surprisingly little known about agenda setting for international
development in the United Nations (UN) despite it having a significant
influence on the process and outcomes of development efforts. This paper
addresses this shortcoming using a novel approach that applies natural language
processing techniques to countries' annual statements in the UN General Debate.
Every year UN member states deliver statements during the General Debate on
their governments' perspective on major issues in world politics. These
speeches provide invaluable information on state preferences on a wide range of
issues, including international development, but have largely been overlooked
in the study of global politics. This paper identifies the main international
development topics that states raise in these speeches between 1970 and 2016,
and examine the country-specific drivers of international development rhetoric.
Agent-based computing from multi-agent systems to agent-based Models: a visual survey
Authors: Muaz A. Niazi, Amir Hussain
Date: 2017-08-19 00:00:00
Tasks: Articles

Agent-Based Computing is a diverse research domain concerned with the
building of intelligent software based on the concept of "agents". In this
paper, we use Scientometric analysis to analyze all sub-domains of agent-based
computing. Our data consists of 1,064 journal articles indexed in the ISI web
of knowledge published during a twenty year period: 1990-2010. These were
retrieved using a topic search with various keywords commonly used in
sub-domains of agent-based computing. In our proposed approach, we have
employed a combination of two applications for analysis, namely Network
Workbench and CiteSpace - wherein Network Workbench allowed for the analysis of
complex network aspects of the domain, detailed visualization-based analysis of
the bibliographic data was performed using CiteSpace. Our results include the
identification of the largest cluster based on keywords, the timeline of
publication of index terms, the core journals and key subject categories. We
also identify the core authors, top countries of origin of the manuscripts
along with core research institutes. Finally, our results have interestingly
revealed the strong presence of agent-based computing in a number of
non-computing related scientific domains including Life Sciences, Ecological
Sciences and Social Sciences.
The CLaC Discourse Parser at CoNLL-2015
Authors: Majid Laali, Elnaz Davoodi, Leila Kosseim
Date: 2017-08-19 00:00:00
Proceeding: CONLL 2015 7
Tasks: BIG-bench Machine Learning, Discourse Parsing

This paper describes our submission (kosseim15) to the CoNLL-2015 shared task
on shallow discourse parsing. We used the UIMA framework to develop our parser
and used ClearTK to add machine learning functionality to the UIMA framework.
Overall, our parser achieves a result of 17.3 F1 on the identification of
discourse relations on the blind CoNLL-2015 test set, ranking in sixth place.
Image2song: Song Retrieval via Bridging Image Content and Lyric Words
Authors: Xuelong. Li, Di Hu, Xiaoqiang Lu
Date: 2017-08-19 00:00:00
Proceeding: ICCV 2017 10
Tasks: Retrieval, TAG

Image is usually taken for expressing some kinds of emotions or purposes,
such as love, celebrating Christmas. There is another better way that combines
the image and relevant song to amplify the expression, which has drawn much
attention in the social network recently. Hence, the automatic selection of
songs should be expected. In this paper, we propose to retrieve semantic
relevant songs just by an image query, which is named as the image2song
problem. Motivated by the requirements of establishing correlation in
semantic/content, we build a semantic-based song retrieval framework, which
learns the correlation between image content and lyric words. This model uses a
convolutional neural network to generate rich tags from image regions, a
recurrent neural network to model lyric, and then establishes correlation via a
multi-layer perceptron. To reduce the content gap between image and lyric, we
propose to make the lyric modeling focus on the main image content via a tag
attention. We collect a dataset from the social-sharing multimodal data to
study the proposed problem, which consists of (image, music clip, lyric)
triplets. We demonstrate that our proposed model shows noticeable results in
the image2song retrieval task and provides suitable songs. Besides, the
song2image task is also performed.
A Data and Model-Parallel, Distributed and Scalable Framework for Training of Deep Networks in Apache Spark
Authors: Disha Shrivastava, Santanu Chaudhury, Dr. Jayadeva
Date: 2017-08-19 00:00:00

Training deep networks is expensive and time-consuming with the training
period increasing with data size and growth in model parameters. In this paper,
we provide a framework for distributed training of deep networks over a cluster
of CPUs in Apache Spark. The framework implements both Data Parallelism and
Model Parallelism making it suitable to use for deep networks which require
huge training data and model parameters which are too big to fit into the
memory of a single machine. It can be scaled easily over a cluster of cheap
commodity hardware to attain significant speedup and obtain better results
making it quite economical as compared to farm of GPUs and supercomputers. We
have proposed a new algorithm for training of deep networks for the case when
the network is partitioned across the machines (Model Parallelism) along with
detailed cost analysis and proof of convergence of the same. We have developed
implementations for Fully-Connected Feedforward Networks, Convolutional Neural
Networks, Recurrent Neural Networks and Long Short-Term Memory architectures.
We present the results of extensive simulations demonstrating the speedup and
accuracy obtained by our framework for different sizes of the data and model
parameters with variation in the number of worker cores/partitions; thereby
showing that our proposed framework can achieve significant speedup (upto 11X
for CNN) and is also quite scalable.
Adaptive Learning Rule for Hardware-based Deep Neural Networks Using Electronic Synapse Devices
Authors: Suhwan Lim, Jong-Ho Bae, Jai-Ho Eum, Sungtae Lee, Chul-Heung Kim, Dongseok Kwon, Byung-Gook Park, Jong-Ho Lee
Date: 2017-07-20 00:00:00

In this paper, we propose a learning rule based on a back-propagation (BP)
algorithm that can be applied to a hardware-based deep neural network (HW-DNN)
using electronic devices that exhibit discrete and limited conductance
characteristics. This adaptive learning rule, which enables forward, backward
propagation, as well as weight updates in hardware, is helpful during the
implementation of power-efficient and high-speed deep neural networks. In
simulations using a three-layer perceptron network, we evaluate the learning
performance according to various conductance responses of electronic synapse
devices and weight-updating methods. It is shown that the learning accuracy is
comparable to that obtained when using a software-based BP algorithm when the
electronic synapse device has a linear conductance response with a high dynamic
range. Furthermore, the proposed unidirectional weight-updating method is
suitable for electronic synapse devices which have nonlinear and finite
conductance responses. Because this weight-updating method can compensate the
demerit of asymmetric weight updates, we can obtain better accuracy compared to
other methods. This adaptive learning rule, which can be applied to full
hardware implementation, can also compensate the degradation of learning
accuracy due to the probable device-to-device variation in an actual electronic
synapse device.