pwc-archive/papers-with-abstracts huggingface dataset:
Text2Action: Generative Adversarial Synthesis from Language to Action
Authors: Hyemin Ahn, Timothy Ha, Yunho Choi, Hwiyeon Yoo, Songhwai Oh
Date: 2017-10-15 00:00:00
Tasks: Decoder, Generative Adversarial Network, Sentence

In this paper, we propose a generative model which learns the relationship
between language and human action in order to generate a human action sequence
given a sentence describing human behavior. The proposed generative model is a
generative adversarial network (GAN), which is based on the sequence to
sequence (SEQ2SEQ) model. Using the proposed generative network, we can
synthesize various actions for a robot or a virtual agent using a text encoder
recurrent neural network (RNN) and an action decoder RNN. The proposed
generative network is trained from 29,770 pairs of actions and sentence
annotations extracted from MSR-Video-to-Text (MSR-VTT), a large-scale video
dataset. We demonstrate that the network can generate human-like actions which
can be transferred to a Baxter robot, such that the robot performs an action
based on a provided sentence. Results show that the proposed generative network
correctly models the relationship between language and action and can generate
a diverse set of actions from the same sentence.
Interactive Tools and Tasks for the Hebrew Bible
Authors: Nicolai Winther-Nielsen
Date: 2016-03-14 00:00:00
Tasks: Text Categorization

This contribution to a special issue on "Computer-aided processing of
intertextuality" in ancient texts will illustrate how using digital tools to
interact with the Hebrew Bible offers new promising perspectives for
visualizing the texts and for performing tasks in education and research. This
contribution explores how the corpus of the Hebrew Bible created and maintained
by the Eep Talstra Centre for Bible and Computer can support new methods for
modern knowledge workers within the field of digital humanities and theology be
applied to ancient texts, and how this can be envisioned as a new field of
digital intertextuality. The article first describes how the corpus was used to
develop the Bible Online Learner as a persuasive technology to enhance language
learning with, in, and around a database that acts as the engine driving
interactive tasks for learners. Intertextuality in this case is a matter of
active exploration and ongoing practice. Furthermore, interactive
corpus-technology has an important bearing on the task of textual criticism as
a specialized area of research that depends increasingly on the availability of
digital resources. Commercial solutions developed by software companies like
Logos and Accordance offer a market-based intertextuality defined by the
production of advanced digital resources for scholars and students as useful
alternatives to often inaccessible and expensive printed versions. It is
reasonable to expect that in the future interactive corpus technology will
allow scholars to do innovative academic tasks in textual criticism and
interpretation. We have already seen the emergence of promising tools for text
categorization, analysis of translation shifts, and interpretation. Broadly
speaking, interactive tools and tasks within the three areas of language
learning, textual criticism, and Biblical studies illustrate a new kind of
intertextuality emerging within digital humanities.
Stochastic L-BFGS: Improved Convergence Rates and Practical Acceleration Strategies
Authors: Renbo Zhao, William B. Haskell, Vincent Y. F. Tan
Date: 2017-04-01 00:00:00
Tasks: regression

We revisit the stochastic limited-memory BFGS (L-BFGS) algorithm. By
proposing a new framework for the convergence analysis, we prove improved
convergence rates and computational complexities of the stochastic L-BFGS
algorithms compared to previous works. In addition, we propose several
practical acceleration strategies to speed up the empirical performance of such
algorithms. We also provide theoretical analyses for most of the strategies.
Experiments on large-scale logistic and ridge regression problems demonstrate
that our proposed strategies yield significant improvements vis-\`a-vis
competing state-of-the-art algorithms.
Cross-Lingual Sentiment Analysis Without (Good) Translation
Authors: Mohamed Abdalla, Graeme Hirst
Date: 2017-07-06 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Machine Translation, Sentiment Analysis, Translation

Current approaches to cross-lingual sentiment analysis try to leverage the
wealth of labeled English data using bilingual lexicons, bilingual vector space
embeddings, or machine translation systems. Here we show that it is possible to
use a single linear transformation, with as few as 2000 word pairs, to capture
fine-grained sentiment relationships between words in a cross-lingual setting.
We apply these cross-lingual sentiment models to a diverse set of tasks to
demonstrate their functionality in a non-English context. By effectively
leveraging English sentiment knowledge without the need for accurate
translation, we can analyze and extract features from other languages with
scarce data at a very low cost, thus making sentiment and related analyses for
many languages inexpensive.
Max-Margin Invariant Features from Transformed Unlabeled Data
Authors: Dipan K. Pal, Ashwin A. Kannan, Gautam Arakalgud, Marios Savvides
Date: 2017-10-24 00:00:00
Tasks: Face Recognition, One-Shot Learning

The study of representations invariant to common transformations of the data
is important to learning. Most techniques have focused on local approximate
invariance implemented within expensive optimization frameworks lacking
explicit theoretical guarantees. In this paper, we study kernels that are
invariant to a unitary group while having theoretical guarantees in addressing
the important practical issue of unavailability of transformed versions of
labelled data. A problem we call the Unlabeled Transformation Problem which is
a special form of semi-supervised learning and one-shot learning. We present a
theoretically motivated alternate approach to the invariant kernel SVM based on
which we propose Max-Margin Invariant Features (MMIF) to solve this problem. As
an illustration, we design an framework for face recognition and demonstrate
the efficacy of our approach on a large scale semi-synthetic dataset with
153,000 images and a new challenging protocol on Labelled Faces in the Wild
(LFW) while out-performing strong baselines.
Display advertising: Estimating conversion probability efficiently
Authors: Abdollah Safari, Rachel MacKay Altman, Thomas M. Loughin
Date: 2017-10-24 00:00:00
Tasks: Marketing

The goal of online display advertising is to entice users to "convert" (i.e.,
take a pre-defined action such as making a purchase) after clicking on the ad.
An important measure of the value of an ad is the probability of conversion.
The focus of this paper is the development of a computationally efficient,
accurate, and precise estimator of conversion probability. The challenges
associated with this estimation problem are the delays in observing conversions
and the size of the data set (both number of observations and number of
predictors). Two models have previously been considered as a basis for
estimation: A logistic regression model and a joint model for observed
conversion statuses and delay times. Fitting the former is simple, but ignoring
the delays in conversion leads to an under-estimate of conversion probability.
On the other hand, the latter is less biased but computationally expensive to
fit. Our proposed estimator is a compromise between these two estimators. We
apply our results to a data set from Criteo, a commerce marketing company that
personalizes online display advertisements for users.
Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search
Authors: Chaitanya Mitash, Abdeslam Boularias, Kostas E. Bekris
Date: 2017-10-24 00:00:00
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGB, global-optimization, Object, object-detection, Object Detection, Point Cloud Registration, Pose Estimation

This work proposes a process for efficiently searching over combinations of
individual object 6D pose hypotheses in cluttered scenes, especially in cases
involving occlusions and objects resting on each other. The initial set of
candidate object poses is generated from state-of-the-art object detection and
global point cloud registration techniques. The best-scored pose per object by
using these techniques may not be accurate due to overlaps and occlusions.
Nevertheless, experimental indications provided in this work show that object
poses with lower ranks may be closer to the real poses than ones with high
ranks according to registration techniques. This motivates a global
optimization process for improving these poses by taking into account
scene-level physical interactions between objects. It also implies that the
Cartesian product of candidate poses for interacting objects must be searched
so as to identify the best scene-level hypothesis. To perform the search
efficiently, the candidate poses for each object are clustered so as to reduce
their number but still keep a sufficient diversity. Then, searching over the
combinations of candidate object poses is performed through a Monte Carlo Tree
Search (MCTS) process that uses the similarity between the observed depth image
of the scene and a rendering of the scene given the hypothesized pose as a
score that guides the search procedure. MCTS handles in a principled way the
tradeoff between fine-tuning the most promising poses and exploring new ones,
by using the Upper Confidence Bound (UCB) technique. Experimental results
indicate that this process is able to quickly identify in cluttered scenes
physically-consistent object poses that are significantly closer to ground
truth compared to poses found by point cloud registration methods.
Optimal Resource Allocation in Distributed Broadband Wireless Communication Systems
Authors: Yao Yao, Mustafa Mehmet Ali, Shahin Vakilinia
Date: 2017-10-24 00:00:00

This paper is concerned with optimization of distributed broadband wireless
communication (BWC) systems. BWC systems contain a distributed antenna system
(DAS) connected to a base station with optical fiber. Distributed BWC systems
have been proposed as a solution to the power constraint problem in traditional
cellular networks. So far, the research on BWC systems have advanced on two
separate tracks, the design of the system to meet the quality of service
requirements (QoS) and optimization of the location of the DAS. In this paper,
we consider a combined optimization of BWC systems. We consider uplink
communications in distributed BWC systems with multiple levels of priority
traffic with arrivals and departures forming renewal processes. We develop an
analysis that determines packet delay violation probability for each priority
level as a function of the outage probability of the DAS through the
application of results from renewal theory. Then, we determine the optimal
locations of the antennas that minimize the antenna outage probability. We also
study the tradeoff between the packet delay violation probability and packet
loss probability. This work will be helpful in the designing of the distributed
BWC systems.
Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets
Authors: Sanjay Purushotham, Chuizheng Meng, Zhengping Che, Yan Liu
Date: 2017-10-23 00:00:00
Tasks: Benchmarking, BIG-bench Machine Learning, Deep Learning, Length-of-Stay prediction, Mortality Prediction, Prediction, speech-recognition, Speech Recognition, Time Series, Time Series Analysis

Deep learning models (aka Deep Neural Networks) have revolutionized many
fields including computer vision, natural language processing, speech
recognition, and is being increasingly used in clinical healthcare
applications. However, few works exist which have benchmarked the performance
of the deep learning models with respect to the state-of-the-art machine
learning models and prognostic scoring systems on publicly available healthcare
datasets. In this paper, we present the benchmarking results for several
clinical prediction tasks such as mortality prediction, length of stay
prediction, and ICD-9 code group prediction using Deep Learning models,
ensemble of machine learning models (Super Learner algorithm), SAPS II and SOFA
scores. We used the Medical Information Mart for Intensive Care III (MIMIC-III)
(v1.4) publicly available dataset, which includes all patients admitted to an
ICU at the Beth Israel Deaconess Medical Center from 2001 to 2012, for the
benchmarking tasks. Our results show that deep learning models consistently
outperform all the other approaches especially when the `raw' clinical time
series data is used as input features to the models.
Stability Analysis of Optimal Adaptive Control using Value Iteration with Approximation Errors
Authors: Ali Heydari
Date: 2017-10-23 00:00:00
Tasks: valid

Adaptive optimal control using value iteration initiated from a stabilizing
control policy is theoretically analyzed in terms of stability of the system
during the learning stage without ignoring the effects of approximation errors.
This analysis includes the system operated using any single/constant resulting
control policy and also using an evolving/time-varying control policy. A
feature of the presented results is providing estimations of the \textit{region
of attraction} so that if the initial condition is within the region, the whole
trajectory will remain inside it and hence, the function approximation results
remain valid.
A Two-Level Classification Approach for Detecting Clickbait Posts using Text-Based Features
Authors: Olga Papadopoulou, Markos Zampoglou, Symeon Papadopoulos, Ioannis Kompatsiaris
Date: 2017-10-23 00:00:00
Tasks: Clickbait Detection, Fake News Detection, feature selection, General Classification

The emergence of social media as news sources has led to the rise of
clickbait posts attempting to attract users to click on article links without
informing them on the actual article content. This paper presents our efforts
to create a clickbait detector inspired by fake news detection algorithms, and
our submission to the Clickbait Challenge 2017. The detector is based almost
exclusively on text-based features taken from previous work on clickbait
detection, our own work on fake post detection, and features we designed
specifically for the challenge. We use a two-level classification approach,
combining the outputs of 65 first-level classifiers in a second-level feature
vector. We present our exploratory results with individual features and their
combinations, taken from the post text and the target article title, as well as
feature selection. While our own blind tests with the dataset led to an F-score
of 0.63, our final evaluation in the Challenge only achieved an F-score of
0.43. We explore the possible causes of this, and lay out potential future
steps to achieve more successful results.
A Unified Framework for Long Range and Cold Start Forecasting of Seasonal Profiles in Time Series
Authors: Christopher Xie, Alex Tank, Alec Greaves-Tunnell, Emily Fox
Date: 2017-10-23 00:00:00
Tasks: Recommendation Systems, Time Series, Time Series Analysis

Providing long-range forecasts is a fundamental challenge in time series
modeling, which is only compounded by the challenge of having to form such
forecasts when a time series has never previously been observed. The latter
challenge is the time series version of the cold-start problem seen in
recommender systems which, to our knowledge, has not been addressed in previous
work. A similar problem occurs when a long range forecast is required after
only observing a small number of time points --- a warm start forecast. With
these aims in mind, we focus on forecasting seasonal profiles---or baseline
demand---for periods on the order of a year in three cases: the long range case
with multiple previously observed seasonal profiles, the cold start case with
no previous observed seasonal profiles, and the warm start case with only a
single partially observed profile. Classical time series approaches that
perform iterated step-ahead forecasts based on previous observations struggle
to provide accurate long range predictions; in settings with little to no
observed data, such approaches are simply not applicable. Instead, we present a
straightforward framework which combines ideas from high-dimensional regression
and matrix factorization on a carefully constructed data matrix. Key to our
formulation and resulting performance is leveraging (1) repeated patterns over
fixed periods of time and across series, and (2) metadata associated with the
individual series; without this additional data, the cold-start/warm-start
problems are nearly impossible to solve. We demonstrate that our framework can
accurately forecast an array of seasonal profiles on multiple large scale
datasets.
Exploring Content-based Artwork Recommendation with Metadata and Visual Features
Authors: Pablo Messina, Vicente Dominguez, Denis Parra, Christoph Trattner, Alvaro Soto
Date: 2017-06-19 00:00:00

Compared to other areas, artwork recommendation has received little
attention, despite the continuous growth of the artwork market. Previous
research has relied on ratings and metadata to make artwork recommendations, as
well as visual features extracted with deep neural networks (DNN). However,
these features have no direct interpretation to explicit visual features (e.g.
brightness, texture) which might hinder explainability and user-acceptance. In
this work, we study the impact of artwork metadata as well as visual features
(DNN-based and attractiveness-based) for physical artwork recommendation, using
images and transaction data from the UGallery online artwork store.
  Our results indicate that: (i) visual features perform better than manually
curated data, (ii) DNN-based visual features perform better than
attractiveness-based ones, and (iii) a hybrid approach improves the performance
further. Our research can inform the development of new artwork recommenders
relying on diverse content data.
NileTMRG at SemEval-2017 Task 4: Arabic Sentiment Analysis
Authors: Samhaa R. El-Beltagy, Mona El Kalamawy, Abu Bakr Soliman
Date: 2017-10-23 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Arabic Sentiment Analysis, General Classification, Sentiment Analysis, Word Embeddings

This paper describes two systems that were used by the authors for addressing
Arabic Sentiment Analysis as part of SemEval-2017, task 4. The authors
participated in three Arabic related subtasks which are: Subtask A (Message
Polarity Classification), Sub-task B (Topic-Based Message Polarity
classification) and Subtask D (Tweet quantification) using the team name of
NileTMRG. For subtask A, we made use of our previously developed sentiment
analyzer which we augmented with a scored lexicon. For subtasks B and D, we
used an ensemble of three different classifiers. The first classifier was a
convolutional neural network for which we trained (word2vec) word embeddings.
The second classifier consisted of a MultiLayer Perceptron, while the third
classifier was a Logistic regression model that takes the same input as the
second classifier. Voting between the three classifiers was used to determine
the final outcome. The output from task B, was quantified to produce the
results for task D. In all three Arabic related tasks in which NileTMRG
participated, the team ranked at number one.
Combining Lexical Features and a Supervised Learning Approach for Arabic Sentiment Analysis
Authors: Samhaa R. El-Beltagy, Talaat Khalil, Amal Halaby, Muhammad Hammad
Date: 2017-10-23 00:00:00
Tasks: Arabic Sentiment Analysis, Sentiment Analysis

The importance of building sentiment analysis tools for Arabic social media
has been recognized during the past couple of years, especially with the rapid
increase in the number of Arabic social media users. One of the main
difficulties in tackling this problem is that text within social media is
mostly colloquial, with many dialects being used within social media platforms.
In this paper, we present a set of features that were integrated with a machine
learning based sentiment analysis model and applied on Egyptian, Saudi,
Levantine, and MSA Arabic social media datasets. Many of the proposed features
were derived through the use of an Arabic Sentiment Lexicon. The model also
presents emoticon based features, as well as input text related features such
as the number of segments within the text, the length of the text, whether the
text ends with a question mark or not, etc. We show that the presented features
have resulted in an increased accuracy across six of the seven datasets we've
experimented with and which are all benchmarked. Since the developed model
out-performs all existing Arabic sentiment analysis systems that have publicly
available datasets, we can state that this model presents state-of-the-art in
Arabic sentiment analysis.
Convolutional Neural Networks: Ensemble Modeling, Fine-Tuning and Unsupervised Semantic Localization for Intraoperative CLE Images
Authors: Mohammadhassan Izadyyazdanabadi, Evgenii Belykh, Michael Mooney, Nikolay Martirosyan, Jennifer Eschbacher, Peter Nakaji, Mark C. Preul, Yezhou Yang
Date: 2017-09-10 00:00:00
Tasks: Diagnostic

Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence
technology undergoing assessment for applications in brain tumor surgery.
Despite its promising potential, interpreting the unfamiliar gray tone images
of fluorescent stains can be difficult. Many of the CLE images can be distorted
by motion, extremely low or high fluorescence signal, or obscured by red blood
cell accumulation, and these can be interpreted as nondiagnostic. However, just
one neat CLE image might suffice for intraoperative diagnosis of the tumor.
While manual examination of thousands of nondiagnostic images during surgery
would be impractical, this creates an opportunity for a model to select
diagnostic images for the pathologists or surgeon's review. In this study, we
sought to develop a deep learning model to automatically detect the diagnostic
images using a manually annotated dataset, and we employed a patient-based
nested cross-validation approach to explore generalizability of the model. We
explored various training regimes: deep training, shallow fine-tuning, and deep
fine-tuning. Further, we investigated the effect of ensemble modeling by
combining the top-5 single models crafted in the development phase. We
localized histological features from diagnostic CLE images by visualization of
shallow and deep neural activations. Our inter-rater experiment results
confirmed that our ensemble of deeply fine-tuned models achieved higher
agreement with the ground truth than the other observers. With the speed and
precision of the proposed method (110 images/second; 85% on the gold standard
test subset), it has potential to be integrated into the operative workflow in
the brain tumor surgery.
Stability and Generalization of Learning Algorithms that Converge to Global Optima
Authors: Zachary Charles, Dimitris Papailiopoulos
Date: 2017-10-23 00:00:00
Proceeding: ICML 2018 7
Tasks: Generalization Bounds

We establish novel generalization bounds for learning algorithms that
converge to global minima. We do so by deriving black-box stability results
that only depend on the convergence of a learning algorithm and the geometry
around the minimizers of the loss function. The results are shown for nonconvex
loss functions satisfying the Polyak-{\L}ojasiewicz (PL) and the quadratic
growth (QG) conditions. We further show that these conditions arise for some
neural networks with linear activations. We use our black-box results to
establish the stability of optimization algorithms such as stochastic gradient
descent (SGD), gradient descent (GD), randomized coordinate descent (RCD), and
the stochastic variance reduced gradient method (SVRG), in both the PL and the
strongly convex setting. Our results match or improve state-of-the-art
generalization bounds and can easily be extended to similar optimization
algorithms. Finally, we show that although our results imply comparable
stability for SGD and GD in the PL setting, there exist simple neural networks
with multiple local minima where SGD is stable but GD is not.