pwc-archive/papers-with-abstracts huggingface dataset:
Paradigm Completion for Derivational Morphology
Authors: Ryan Cotterell, Ekaterina Vylomova, Huda Khayrallah, Christo Kirov, David Yarowsky
Date: 2017-08-30 00:00:00
Proceeding: EMNLP 2017 9

The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the theoretical motivation for a paradigmatic treatment of derivational morphology, and introduce the task of derivational paradigm completion as a parallel to inflectional paradigm completion. State-of-the-art neural models, adapted from the inflection task, are able to learn a range of derivation patterns, and outperform a non-neural baseline by 16.4%. However, due to semantic, historical, and lexical considerations involved in derivational morphology, future work will be needed to achieve performance parity with inflection-generating systems.
Photorealistic Facial Expression Synthesis by the Conditional Difference Adversarial Autoencoder
Authors: Yuqian Zhou, Bertram Emil Shi
Date: 2017-08-30 00:00:00
Tasks: Data Augmentation, Decoder, Emotion Recognition, Face Recognition

Photorealistic facial expression synthesis from single face image can be
widely applied to face recognition, data augmentation for emotion recognition
or entertainment. This problem is challenging, in part due to a paucity of
labeled facial expression data, making it difficult for algorithms to
disambiguate changes due to identity and changes due to expression. In this
paper, we propose the conditional difference adversarial autoencoder, CDAAE,
for facial expression synthesis. The CDAAE takes a facial image of a previously
unseen person and generates an image of that human face with a target emotion
or facial action unit label. The CDAAE adds a feedforward path to an
autoencoder structure connecting low level features at the encoder to features
at the corresponding level at the decoder. It handles the problem of
disambiguating changes due to identity and changes due to facial expression by
learning to generate the difference between low-level features of images of the
same person but with different facial expressions. The CDAAE structure can be
used to generate novel expressions by combining and interpolating between
facial expressions/action units within the training set. Our experimental
results demonstrate that the CDAAE can preserve identity information when
generating facial expression for unseen subjects more faithfully than previous
approaches. This is especially advantageous when training with small databases.
End-to-end Learning for Short Text Expansion
Authors: Jian Tang, Yue Wang, Kai Zheng, Qiaozhu Mei
Date: 2017-08-30 00:00:00
Tasks: Recommendation Systems, text-classification, Text Classification

Effectively making sense of short texts is a critical task for many real
world applications such as search engines, social media services, and
recommender systems. The task is particularly challenging as a short text
contains very sparse information, often too sparse for a machine learning
algorithm to pick up useful signals. A common practice for analyzing short text
is to first expand it with external information, which is usually harvested
from a large collection of longer texts. In literature, short text expansion
has been done with all kinds of heuristics. We propose an end-to-end solution
that automatically learns how to expand short text to optimize a given learning
task. A novel deep memory network is proposed to automatically find relevant
information from a collection of longer documents and reformulate the short
text through a gating mechanism. Using short text classification as a
demonstrating task, we show that the deep memory network significantly
outperforms classical text expansion methods with comprehensive experiments on
real world data sets.
Automating Direct Speech Variations in Stories and Games
Authors: Stephanie M. Lukin, James O. Ryan, Marilyn A. Walker
Date: 2017-08-30 00:00:00
Tasks: Text Generation, Translation

Dialogue authoring in large games requires not only content creation but the
subtlety of its delivery, which can vary from character to character. Manually
authoring this dialogue can be tedious, time-consuming, or even altogether
infeasible. This paper utilizes a rich narrative representation for modeling
dialogue and an expressive natural language generation engine for realizing it,
and expands upon a translation tool that bridges the two. We add functionality
to the translator to allow direct speech to be modeled by the narrative
representation, whereas the original translator supports only narratives told
by a third person narrator. We show that we can perform character substitution
in dialogues. We implement and evaluate a potential application to dialogue
implementation: generating dialogue for games with big, dynamic, or
procedurally-generated open worlds. We present a pilot study on human
perceptions of the personalities of characters using direct speech, assuming
unknown personality types at the time of authoring.
A Deep Learning Approach for Population Estimation from Satellite Imagery
Authors: Caleb Robinson, Fred Hohman, Bistra Dilkina
Date: 2017-08-30 00:00:00
Tasks: Decision Making, Deep Learning, Management

Knowing where people live is a fundamental component of many decision making
processes such as urban development, infectious disease containment, evacuation
planning, risk management, conservation planning, and more. While bottom-up,
survey driven censuses can provide a comprehensive view into the population
landscape of a country, they are expensive to realize, are infrequently
performed, and only provide population counts over broad areas. Population
disaggregation techniques and population projection methods individually
address these shortcomings, but also have shortcomings of their own. To jointly
answer the questions of "where do people live" and "how many people live
there," we propose a deep learning model for creating high-resolution
population estimations from satellite imagery. Specifically, we train
convolutional neural networks to predict population in the USA at a
$0.01^{\circ} \times 0.01^{\circ}$ resolution grid from 1-year composite
Landsat imagery. We validate these models in two ways: quantitatively, by
comparing our model's grid cell estimates aggregated at a county-level to
several US Census county-level population projections, and qualitatively, by
directly interpreting the model's predictions in terms of the satellite image
inputs. We find that aggregating our model's estimates gives comparable results
to the Census county-level population projections and that the predictions made
by our model can be directly interpreted, which give it advantages over
traditional population disaggregation methods. In general, our model is an
example of how machine learning techniques can be an effective tool for
extracting information from inherently unstructured, remotely sensed data to
provide effective solutions to social problems.
Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion
Authors: Stephanie M. Lukin, Pranav Anand, Marilyn Walker, Steve Whittaker
Date: 2017-08-30 00:00:00
Proceeding: EACL 2017 4
Tasks: Persuasiveness

Americans spend about a third of their time online, with many participating
in online conversations on social and political issues. We hypothesize that
social media arguments on such issues may be more engaging and persuasive than
traditional media summaries, and that particular types of people may be more or
less convinced by particular styles of argument, e.g. emotional arguments may
resonate with some personalities while factual arguments resonate with others.
We report a set of experiments testing at large scale how audience variables
interact with argument style to affect the persuasiveness of an argument, an
under-researched topic within natural language processing. We show that belief
change is affected by personality factors, with conscientious, open and
agreeable people being more convinced by emotional arguments.
Adaptive SVM+: Learning with Privileged Information for Domain Adaptation
Authors: Nikolaos Sarafianos, Michalis Vrigkas, Ioannis A. Kakadiaris
Date: 2017-08-30 00:00:00
Tasks: Domain Adaptation

Incorporating additional knowledge in the learning process can be beneficial
for several computer vision and machine learning tasks. Whether privileged
information originates from a source domain that is adapted to a target domain,
or as additional features available at training time only, using such
privileged (i.e., auxiliary) information is of high importance as it improves
the recognition performance and generalization. However, both primary and
privileged information are rarely derived from the same distribution, which
poses an additional challenge to the recognition task. To address these
challenges, we present a novel learning paradigm that leverages privileged
information in a domain adaptation setup to perform visual recognition tasks.
The proposed framework, named Adaptive SVM+, combines the advantages of both
the learning using privileged information (LUPI) paradigm and the domain
adaptation framework, which are naturally embedded in the objective function of
a regular SVM. We demonstrate the effectiveness of our approach on the publicly
available Animals with Attributes and INTERACT datasets and report
state-of-the-art results in both of them.
PersonaBank: A Corpus of Personal Narratives and Their Story Intention Graphs
Authors: Stephanie M. Lukin, Kevin Bowden, Casey Barackman, Marilyn A. Walker
Date: 2017-08-30 00:00:00
Proceeding: LREC 2016 5

We present a new corpus, PersonaBank, consisting of 108 personal stories from
weblogs that have been annotated with their Story Intention Graphs, a deep
representation of the fabula of a story. We describe the topics of the stories
and the basis of the Story Intention Graph representation, as well as the
process of annotating the stories to produce the Story Intention Graphs and the
challenges of adapting the tool to this new personal narrative domain We also
discuss how the corpus can be used in applications that retell the story using
different styles of tellings, co-tellings, or as a content planner.
Continual One-Shot Learning of Hidden Spike-Patterns with Neural Network Simulation Expansion and STDP Convergence Predictions
Authors: Toby Lightheart, Steven Grainger, Tien-Fu Lu
Date: 2017-08-30 00:00:00
Tasks: Continual Learning, Neural Network simulation, One-Shot Learning

This paper presents a constructive algorithm that achieves successful
one-shot learning of hidden spike-patterns in a competitive detection task. It
has previously been shown (Masquelier et al., 2008) that spike-timing-dependent
plasticity (STDP) and lateral inhibition can result in neurons competitively
tuned to repeating spike-patterns concealed in high rates of overall
presynaptic activity. One-shot construction of neurons with synapse weights
calculated as estimates of converged STDP outcomes results in immediate
selective detection of hidden spike-patterns. The capability of continual
learning is demonstrated through the successful one-shot detection of new sets
of spike-patterns introduced after long intervals in the simulation time.
Simulation expansion (Lightheart et al., 2013) has been proposed as an approach
to the development of constructive algorithms that are compatible with
simulations of biological neural networks. A simulation of a biological neural
network may have orders of magnitude fewer neurons and connections than the
related biological neural systems; therefore, simulated neural networks can be
assumed to be a subset of a larger neural system. The constructive algorithm is
developed using simulation expansion concepts to perform an operation
equivalent to the exchange of neurons between the simulation and the larger
hypothetical neural system. The dynamic selection of neurons to simulate within
a larger neural system (hypothetical or stored in memory) may be a starting
point for a wide range of developments and applications in machine learning and
the simulation of biology.
Block-Simultaneous Direction Method of Multipliers: A proximal primal-dual splitting algorithm for nonconvex problems with multiple constraints
Authors: Fred Moolekamp, Peter Melchior
Date: 2017-08-30 00:00:00
Tasks: Hyperspectral Unmixing

We introduce a generalization of the linearized Alternating Direction Method
of Multipliers to optimize a real-valued function $f$ of multiple arguments
with potentially multiple constraints $g_\circ$ on each of them. The function
$f$ may be nonconvex as long as it is convex in every argument, while the
constraints $g_\circ$ need to be convex but not smooth. If $f$ is smooth, the
proposed Block-Simultaneous Direction Method of Multipliers (bSDMM) can be
interpreted as a proximal analog to inexact coordinate descent methods under
constraints. Unlike alternative approaches for joint solvers of
multiple-constraint problems, we do not require linear operators $L$ of a
constraint function $g(L\ \cdot)$ to be invertible or linked between each
other. bSDMM is well-suited for a range of optimization problems, in particular
for data analysis, where $f$ is the likelihood function of a model and $L$
could be a transformation matrix describing e.g. finite differences or basis
transforms. We apply bSDMM to the Non-negative Matrix Factorization task of a
hyperspectral unmixing problem and demonstrate convergence and effectiveness of
multiple constraints on both matrix factors. The algorithms are implemented in
python and released as an open-source package.
Practical Attacks Against Graph-based Clustering
Authors: Yizheng Chen, Yacin Nadji, Athanasios Kountouras, Fabian Monrose, Roberto Perdisci, Manos Antonakakis, Nikolaos Vasiloglou
Date: 2017-08-29 00:00:00
Tasks: Clustering, Graph Clustering

Graph modeling allows numerous security problems to be tackled in a general
way, however, little work has been done to understand their ability to
withstand adversarial attacks. We design and evaluate two novel graph attacks
against a state-of-the-art network-level, graph-based detection system. Our
work highlights areas in adversarial machine learning that have not yet been
addressed, specifically: graph-based clustering techniques, and a global
feature space where realistic attackers without perfect knowledge must be
accounted for (by the defenders) in order to be practical. Even though less
informed attackers can evade graph clustering with low cost, we show that some
practical defenses are possible.
Modelling Protagonist Goals and Desires in First-Person Narrative
Authors: Elahe Rahimtoroghi, Jiaqi Wu, Ruimin Wang, Pranav Anand, Marilyn A. Walker
Date: 2017-08-29 00:00:00
Proceeding: WS 2017 8

Many genres of natural language text are narratively structured, a testament
to our predilection for organizing our experiences as narratives. There is
broad consensus that understanding a narrative requires identifying and
tracking the goals and desires of the characters and their narrative outcomes.
However, to date, there has been limited work on computational models for this
problem. We introduce a new dataset, DesireDB, which includes gold-standard
labels for identifying statements of desire, textual evidence for desire
fulfillment, and annotations for whether the stated desire is fulfilled given
the evidence in the narrative context. We report experiments on tracking desire
fulfillment using different methods, and show that LSTM Skip-Thought model
achieves F-measure of 0.7 on our corpus.
Convolutional Sparse Coding with Overlapping Group Norms
Authors: Brendt Wohlberg
Date: 2017-08-29 00:00:00
Tasks: Denoising

The most widely used form of convolutional sparse coding uses an $\ell_1$
regularization term. While this approach has been successful in a variety of
applications, a limitation of the $\ell_1$ penalty is that it is homogeneous
across the spatial and filter index dimensions of the sparse representation
array, so that sparsity cannot be separately controlled across these
dimensions. The present paper considers the consequences of replacing the
$\ell_1$ penalty with a mixed group norm, motivated by recent theoretical
results for convolutional sparse representations. Algorithms are developed for
solving the resulting problems, which are quite challenging, and the impact on
the performance of the denoising problem is evaluated. The mixed group norms
are found to perform very poorly in this application. While their performance
is greatly improved by introducing a weighting strategy, such a strategy also
improves the performance obtained from the much simpler and computationally
cheaper $\ell_1$ norm.
Unsupervised Terminological Ontology Learning based on Hierarchical Topic Modeling
Authors: Xiaofeng Zhu, Diego Klabjan, Patrick Bless
Date: 2017-08-29 00:00:00
Tasks: Topic Models

In this paper, we present hierarchical relationbased latent Dirichlet
allocation (hrLDA), a data-driven hierarchical topic model for extracting
terminological ontologies from a large number of heterogeneous documents. In
contrast to traditional topic models, hrLDA relies on noun phrases instead of
unigrams, considers syntax and document structures, and enriches topic
hierarchies with topic relations. Through a series of experiments, we
demonstrate the superiority of hrLDA over existing topic models, especially for
building hierarchies. Furthermore, we illustrate the robustness of hrLDA in the
settings of noisy data sets, which are likely to occur in many practical
scenarios. Our ontology evaluation results show that ontologies extracted from
hrLDA are very competitive with the ontologies created by domain experts.
Sparse-then-Dense Alignment based 3D Map Reconstruction Method for Endoscopic Capsule Robots
Authors: Mehmet Turan, Yusuf Yigit Pilavci, Ipek Ganiyusufoglu, Helder Araujo, Ender Konukoglu, Metin Sitti
Date: 2017-08-29 00:00:00
Tasks: 3D Reconstruction

Since the development of capsule endoscopcy technology, substantial progress
were made in converting passive capsule endoscopes to robotic active capsule
endoscopes which can be controlled by the doctor. However, robotic capsule
endoscopy still has some challenges. In particular, the use of such devices to
generate a precise and globally consistent three-dimensional (3D) map of the
entire inner organ remains an unsolved problem. Such global 3D maps of inner
organs would help doctors to detect the location and size of diseased areas
more accurately, precisely, and intuitively, thus permitting more accurate and
intuitive diagnoses. The proposed 3D reconstruction system is built in a
modular fashion including preprocessing, frame stitching, and shading-based 3D
reconstruction modules. We propose an efficient scheme to automatically select
the key frames out of the huge quantity of raw endoscopic images. Together with
a bundle fusion approach that aligns all the selected key frames jointly in a
globally consistent way, a significant improvement of the mosaic and 3D map
accuracy was reached. To the best of our knowledge, this framework is the first
complete pipeline for an endoscopic capsule robot based 3D map reconstruction
containing all of the necessary steps for a reliable and accurate endoscopic 3D
map. For the qualitative evaluations, a real pig stomach is employed. Moreover,
for the first time in literature, a detailed and comprehensive quantitative
analysis of each proposed pipeline modules is performed using a non-rigid
esophagus gastro duodenoscopy simulator, four different endoscopic cameras, a
magnetically activated soft capsule robot (MASCE), a sub-millimeter precise
optical motion tracker and a fine-scale 3D optical scanner.
A Connectedness Constraint for Learning Sparse Graphs
Authors: Martin Sundin, Arun Venkitaraman, Magnus Jansson, Saikat Chatterjee
Date: 2017-08-29 00:00:00

Graphs are naturally sparse objects that are used to study many problems
involving networks, for example, distributed learning and graph signal
processing. In some cases, the graph is not given, but must be learned from the
problem and available data. Often it is desirable to learn sparse graphs.
However, making a graph highly sparse can split the graph into several
disconnected components, leading to several separate networks. The main
difficulty is that connectedness is often treated as a combinatorial property,
making it hard to enforce in e.g. convex optimization problems. In this
article, we show how connectedness of undirected graphs can be formulated as an
analytical property and can be enforced as a convex constraint. We especially
show how the constraint relates to the distributed consensus problem and graph
Laplacian learning. Using simulated and real data, we perform experiments to
learn sparse and connected graphs from data.
Learning to Price with Reference Effects
Authors: Abbas Kazerouni, Benjamin Van Roy
Date: 2017-08-29 00:00:00
Tasks: Reinforcement Learning, Thompson Sampling

As a firm varies the price of a product, consumers exhibit reference effects,
making purchase decisions based not only on the prevailing price but also the
product's price history. We consider the problem of learning such behavioral
patterns as a monopolist releases, markets, and prices products. This context
calls for pricing decisions that intelligently trade off between maximizing
revenue generated by a current product and probing to gain information for
future benefit. Due to dependence on price history, realized demand can reflect
delayed consequences of earlier pricing decisions. As such, inference entails
attribution of outcomes to prior decisions and effective exploration requires
planning price sequences that yield informative future outcomes. Despite the
considerable complexity of this problem, we offer a tractable systematic
approach. In particular, we frame the problem as one of reinforcement learning
and leverage Thompson sampling. We also establish a regret bound that provides
graceful guarantees on how performance improves as data is gathered and how
this depends on the complexity of the demand model. We illustrate merits of the
approach through simulations.
Pix2face: Direct 3D Face Model Estimation
Authors: Daniel Crispell, Maxim Bazik
Date: 2017-08-29 00:00:00
Tasks: Face Model, model, Pose Estimation

An efficient, fully automatic method for 3D face shape and pose estimation in
unconstrained 2D imagery is presented. The proposed method jointly estimates a
dense set of 3D landmarks and facial geometry using a single pass of a modified
version of the popular "U-Net" neural network architecture. Additionally, we
propose a method for directly estimating a set of 3D Morphable Model (3DMM)
parameters, using the estimated 3D landmarks and geometry as constraints in a
simple linear system. Qualitative modeling results are presented, as well as
quantitative evaluation of predicted 3D face landmarks in unconstrained video
sequences.
Clustering Patients with Tensor Decomposition
Authors: Matteo Ruffini, Ricard Gavaldà, Esther Limón
Date: 2017-08-29 00:00:00
Tasks: Clustering, Tensor Decomposition

In this paper we present a method for the unsupervised clustering of
high-dimensional binary data, with a special focus on electronic healthcare
records. We present a robust and efficient heuristic to face this problem using
tensor decomposition. We present the reasons why this approach is preferable
for tasks such as clustering patient records, to more commonly used
distance-based methods. We run the algorithm on two datasets of healthcare
records, obtaining clinically meaningful results.