pwc-archive/papers-with-abstracts huggingface dataset:
Learning Convolutional Text Representations for Visual Question Answering
Authors: Zhengyang Wang, Shuiwang Ji
Date: 2017-05-18 00:00:00
Tasks: General Classification, image-classification, text-classification, Visual Question Answering, Visual Question Answering (VQA)

Visual question answering is a recently proposed artificial intelligence task
that requires a deep understanding of both images and texts. In deep learning,
images are typically modeled through convolutional neural networks, and texts
are typically modeled through recurrent neural networks. While the requirement
for modeling images is similar to traditional computer vision tasks, such as
object recognition and image classification, visual question answering raises a
different need for textual representation as compared to other natural language
processing tasks. In this work, we perform a detailed analysis on natural
language questions in visual question answering. Based on the analysis, we
propose to rely on convolutional neural networks for learning textual
representations. By exploring the various properties of convolutional neural
networks specialized for text data, such as width and depth, we present our
"CNN Inception + Gate" model. We show that our model improves question
representations and thus the overall accuracy of visual question answering
models. We also show that the text representation requirement in visual
question answering is more complicated and comprehensive than that in
conventional natural language processing tasks, making it a better task to
evaluate textual representation methods. Shallow models like fastText, which
can obtain comparable results with deep learning models in tasks like text
classification, are not suitable in visual question answering.
Automated diagnosis of pneumothorax using an ensemble of convolutional neural networks with multi-sized chest radiography images
Authors: Tae Joon Jun, Dohyeun Kim, Daeyoung Kim
Date: 2018-04-18 00:00:00
Tasks: Pneumothorax Detection

Pneumothorax is a relatively common disease, but in some cases, it may be
difficult to find with chest radiography. In this paper, we propose a novel
method of detecting pneumothorax in chest radiography. We propose an ensemble
model of identical convolutional neural networks (CNN) with three different
sizes of radiography images. Conventional methods may not properly characterize
lost features while resizing large size images into 256 x 256 or 224 x 224
sizes. Our model is evaluated with ChestX-ray dataset which contains over
100,000 chest radiography images. As a result of the experiment, the proposed
model showed AUC 0.911, which is the state of the art result in pneumothorax
detection. Our method is expected to be effective when applying CNN to large
size medical images.
Active choice of teachers, learning strategies and goals for a socially guided intrinsic motivation learner
Authors: Sao Mai Nguyen, Pierre-Yves Oudeyer
Date: 2018-04-18 00:00:00
Tasks: Active Learning

We present an active learning architecture that allows a robot to actively
learn which data collection strategy is most efficient for acquiring motor
skills to achieve multiple outcomes, and generalise over its experience to
achieve new outcomes. The robot explores its environment both via interactive
learning and goal-babbling. It learns at the same time when, who and what to
actively imitate from several available teachers, and learns when not to use
social guidance but use active goal-oriented self-exploration. This is
formalised in the framework of life-long strategic learning. The proposed
architecture, called Socially Guided Intrinsic Motivation with Active Choice of
Teacher and Strategy (SGIM-ACTS), relies on hierarchical active decisions of
what and how to learn driven by empirical evaluation of learning progress for
each learning strategy. We illustrate with an experiment where a simulated
robot learns to control its arm for realising two kinds of different outcomes.
It has to choose actively and hierarchically at each learning episode: 1) what
to learn: which outcome is most interesting to select as a goal to focus on for
goal-directed exploration; 2) how to learn: which data collection strategy to
use among self-exploration, mimicry and emulation; 3) once he has decided when
and what to imitate by choosing mimicry or emulation, then he has to choose who
to imitate, from a set of different teachers. We show that SGIM-ACTS learns
significantly more efficiently than using single learning strategies, and
coherently selects the best strategy with respect to the chosen outcome, taking
advantage of the available teachers (with different levels of skills).
Automated detection of vulnerable plaque in intravascular ultrasound images
Authors: Tae Joon Jun, Soo-Jin Kang, June-Goo Lee, Jihoon Kweon, Wonjun Na, Daeyoun Kang, Dohyeun Kim, Daeyoung Kim, Young-Hak Kim
Date: 2018-04-18 00:00:00
Tasks: Diagnostic

Acute Coronary Syndrome (ACS) is a syndrome caused by a decrease in blood
flow in the coronary arteries. The ACS is usually related to coronary
thrombosis and is primarily caused by plaque rupture followed by plaque erosion
and calcified nodule. Thin-cap fibroatheroma (TCFA) is known to be the most
similar lesion morphologically to a plaque rupture. In this paper, we propose
methods to classify TCFA using various machine learning classifiers including
Feed-forward Neural Network (FNN), K-Nearest Neighbor (KNN), Random Forest (RF)
and Convolutional Neural Network (CNN) to figure out a classifier that shows
optimal TCFA classification accuracy. In addition, we suggest pixel range based
feature extraction method to extract the ratio of pixels in the different
region of interests to reflect the physician's TCFA discrimination criteria. A
total of 12,325 IVUS images were labeled with corresponding OCT images to train
and evaluate the classifiers. We achieved 0.884, 0.890, 0.878 and 0.933 Area
Under the ROC Curve (AUC) in the order of using FNN, KNN, RF and CNN
classifier. As a result, the CNN classifier performed best and the top 10
features of the feature-based classifiers (FNN, KNN, RF) were found to be
similar to the physician's TCFA diagnostic criteria.
Bootstrapping Generators from Noisy Data
Authors: Laura Perez-Beltrachini, Mirella Lapata
Date: 2018-04-17 00:00:00
Proceeding: NAACL 2018 6
Tasks: Data-to-Text Generation, Decoder, Text Generation

A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e.g., facts in a database) and associated texts. In this paper we aim to bootstrap generators from large scale datasets where the data (e.g., DBPedia facts) and related texts (e.g., Wikipedia abstracts) are loosely aligned. We tackle this challenging task by introducing a special-purpose content selection mechanism. We use multi-instance learning to automatically discover correspondences between data and text pairs and show how these can be used to enhance the content signal while training an encoder-decoder architecture. Experimental results demonstrate that models trained with content-specific objectives improve upon a vanilla encoder-decoder which solely relies on soft attention.
ECG arrhythmia classification using a 2-D convolutional neural network
Authors: Tae Joon Jun, Hoang Minh Nguyen, Daeyoun Kang, Dohyeun Kim, Daeyoung Kim, Young-Hak Kim
Date: 2018-04-18 00:00:00
Tasks: Arrhythmia Detection, Data Augmentation, Electrocardiography (ECG), General Classification

In this paper, we propose an effective electrocardiogram (ECG) arrhythmia
classification method using a deep two-dimensional convolutional neural network
(CNN) which recently shows outstanding performance in the field of pattern
recognition. Every ECG beat was transformed into a two-dimensional grayscale
image as an input data for the CNN classifier. Optimization of the proposed CNN
classifier includes various deep learning techniques such as batch
normalization, data augmentation, Xavier initialization, and dropout. In
addition, we compared our proposed classifier with two well-known CNN models;
AlexNet and VGGNet. ECG recordings from the MIT-BIH arrhythmia database were
used for the evaluation of the classifier. As a result, our classifier achieved
99.05% average accuracy with 97.85% average sensitivity. To precisely validate
our CNN classifier, 10-fold cross-validation was performed at the evaluation
which involves every ECG recording as a test data. Our experimental results
have successfully validated that the proposed CNN classifier with the
transformed ECG images can achieve excellent classification accuracy without
any manual pre-processing of the ECG signals such as noise filtering, feature
extraction, and feature reduction.
Solving the Exponential Growth of Symbolic Regression Trees in Geometric Semantic Genetic Programming
Authors: Joao Francisco B. S. Martins, Luiz Otavio V. B. Oliveira, Luis F. Miranda, Felipe Casadei, Gisele L. Pappa
Date: 2018-04-18 00:00:00
Tasks: regression, Symbolic Regression

Advances in Geometric Semantic Genetic Programming (GSGP) have shown that
this variant of Genetic Programming (GP) reaches better results than its
predecessor for supervised machine learning problems, particularly in the task
of symbolic regression. However, by construction, the geometric semantic
crossover operator generates individuals that grow exponentially with the
number of generations, resulting in solutions with limited use. This paper
presents a new method for individual simplification named GSGP with Reduced
trees (GSGP-Red). GSGP-Red works by expanding the functions generated by the
geometric semantic operators. The resulting expanded function is guaranteed to
be a linear combination that, in a second step, has its repeated structures and
respective coefficients aggregated. Experiments in 12 real-world datasets show
that it is not only possible to create smaller and completely equivalent
individuals in competitive computational time, but also to reduce the number of
nodes composing them by 58 orders of magnitude, on average.
Entropic Spectral Learning for Large-Scale Graphs
Authors: Diego Granziol, Binxin Ru, Stefan Zohren, Xiaowen Dong, Michael Osborne, Stephen Roberts
Date: 2018-04-18 00:00:00
Tasks: Community Detection

Graph spectra have been successfully used to classify network types, compute
the similarity between graphs, and determine the number of communities in a
network. For large graphs, where an eigen-decomposition is infeasible,
iterative moment matched approximations to the spectra and kernel smoothing are
typically used. We show that the underlying moment information is lost when
using kernel smoothing. We further propose a spectral density approximation
based on the method of Maximum Entropy, for which we develop a new algorithm.
This method matches moments exactly and is everywhere positive. We demonstrate
its effectiveness and superiority over existing approaches in learning graph
spectra, via experiments on both synthetic networks, such as the
Erd\H{o}s-R\'{e}nyi and Barab\'{a}si-Albert random graphs, and real-world
networks, such as the social networks for Orkut, YouTube, and Amazon from the
SNAP dataset.
Gaussian Process Landmarking on Manifolds
Authors: Tingran Gao, Shahar Z. Kovalsky, Ingrid Daubechies
Date: 2018-02-09 00:00:00
Tasks: Experimental Design

As a means of improving analysis of biological shapes, we propose an
algorithm for sampling a Riemannian manifold by sequentially selecting points
with maximum uncertainty under a Gaussian process model. This greedy strategy
is known to be near-optimal in the experimental design literature, and appears
to outperform the use of user-placed landmarks in representing the geometry of
biological objects in our application. In the noiseless regime, we establish an
upper bound for the mean squared prediction error (MSPE) in terms of the number
of samples and geometric quantities of the manifold, demonstrating that the
MSPE for our proposed sequential design decays at a rate comparable to the
oracle rate achievable by any sequential or non-sequential optimal design; to
our knowledge this is the first result of this type for sequential experimental
design. The key is to link the greedy algorithm to reduced basis methods in the
context of model reduction for partial differential equations. We expect this
approach will find additional applications in other fields of research.
Attentive Interaction Model: Modeling Changes in View in Argumentation
Authors: Yohan Jo, Shivani Poddar, Byungsoo Jeon, Qinlan Shen, Carolyn P. Rose, Graham Neubig
Date: 2018-03-30 00:00:00
Proceeding: NAACL 2018 6

We present a neural architecture for modeling argumentative dialogue that
explicitly models the interplay between an Opinion Holder's (OH's) reasoning
and a challenger's argument, with the goal of predicting if the argument
successfully changes the OH's view. The model has two components: (1)
vulnerable region detection, an attention model that identifies parts of the
OH's reasoning that are amenable to change, and (2) interaction encoding, which
identifies the relationship between the content of the OH's reasoning and that
of the challenger's argument. Based on evaluation on discussions from the
Change My View forum on Reddit, the two components work together to predict an
OH's change in view, outperforming several baselines. A posthoc analysis
suggests that sentences picked out by the attention model are addressed more
frequently by successful arguments than by unsuccessful ones.
Warped Convolutions: Efficient Invariance to Spatial Transformations
Authors: João F. Henriques, Andrea Vedaldi
Date: 2016-09-14 00:00:00
Proceeding: ICML 2017 8
Tasks: Translation

Convolutional Neural Networks (CNNs) are extremely efficient, since they exploit the inherent translation-invariance of natural images. However, translation is just one of a myriad of useful spatial transformations. Can the same efficiency be attained when considering other spatial invariances? Such generalized convolutions have been considered in the past, but at a high computational cost. We present a construction that is simple and exact, yet has the same computational complexity that standard convolutions enjoy. It consists of a constant image warp followed by a simple convolution, which are standard blocks in deep learning toolboxes. With a carefully crafted warp, the resulting architecture can be made equivariant to a wide range of two-parameter spatial transformations. We show encouraging results in realistic scenarios, including the estimation of vehicle poses in the Google Earth dataset (rotation and scale), and face poses in Annotated Facial Landmarks in the Wild (3D rotations under perspective).
Improving Long-Horizon Forecasts with Expectation-Biased LSTM Networks
Authors: Aya Abdelsalam Ismail, Timothy Wood, Héctor Corrada Bravo
Date: 2018-04-18 00:00:00
Tasks: Management

State-of-the-art forecasting methods using Recurrent Neural Net- works (RNN)
based on Long-Short Term Memory (LSTM) cells have shown exceptional performance
targeting short-horizon forecasts, e.g given a set of predictor features,
forecast a target value for the next few time steps in the future. However, in
many applica- tions, the performance of these methods decays as the forecasting
horizon extends beyond these few time steps. This paper aims to explore the
challenges of long-horizon forecasting using LSTM networks. Here, we illustrate
the long-horizon forecasting problem in datasets from neuroscience and energy
supply management. We then propose expectation-biasing, an approach motivated
by the literature of Dynamic Belief Networks, as a solution to improve
long-horizon forecasting using LSTMs. We propose two LSTM ar- chitectures along
with two methods for expectation biasing that significantly outperforms
standard practice.
Unspeech: Unsupervised Speech Context Embeddings
Authors: Benjamin Milde, Chris Biemann
Date: 2018-04-18 00:00:00
Tasks: Clustering

We introduce "Unspeech" embeddings, which are based on unsupervised learning
of context feature representations for spoken language. The embeddings were
trained on up to 9500 hours of crawled English speech data without
transcriptions or speaker information, by using a straightforward learning
objective based on context and non-context discrimination with negative
sampling. We use a Siamese convolutional neural network architecture to train
Unspeech embeddings and evaluate them on speaker comparison, utterance
clustering and as a context feature in TDNN-HMM acoustic models trained on
TED-LIUM, comparing it to i-vector baselines. Particularly decoding
out-of-domain speech data from the recently released Common Voice corpus shows
consistent WER reductions. We release our source code and pre-trained Unspeech
models under a permissive open source license.
Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems
Authors: Sumit Sidana, Mikhail Trofimov, Oleg Horodnitskii, Charlotte Laclau, Yury Maximov, Massih-Reza Amini
Date: 2017-04-29 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems, Representation Learning

In this paper, we propose a novel ranking framework for collaborative
filtering with the overall aim of learning user preferences over items by
minimizing a pairwise ranking loss. We show the minimization problem involves
dependent random variables and provide a theoretical analysis by proving the
consistency of the empirical risk minimization in the worst case where all
users choose a minimal number of positive and negative items. We further derive
a Neural-Network model that jointly learns a new representation of users and
items in an embedded space as well as the preference relation of users over the
pairs of items. The learning objective is based on three scenarios of ranking
losses that control the ability of the model to maintain the ordering over the
items induced from the users' preferences, as well as, the capacity of the
dot-product defined in the learned embedded space to produce the ordering. The
proposed model is by nature suitable for implicit feedback and involves the
estimation of only very few parameters. Through extensive experiments on
several real-world benchmarks on implicit data, we show the interest of
learning the preference and the embedding simultaneously when compared to
learning those separately. We also demonstrate that our approach is very
competitive with the best state-of-the-art collaborative filtering techniques
proposed for implicit feedback.
A Parallel/Distributed Algorithmic Framework for Mining All Quantitative Association Rules
Authors: Ioannis T. Christou, Emmanouil Amolochitis, Zheng-Hua Tan
Date: 2018-04-18 00:00:00
Tasks: All, Attribute, Marketing

We present QARMA, an efficient novel parallel algorithm for mining all
Quantitative Association Rules in large multidimensional datasets where items
are required to have at least a single common attribute to be specified in the
rules single consequent item. Given a minimum support level and a set of
threshold criteria of interestingness measures such as confidence, conviction
etc. our algorithm guarantees the generation of all non-dominated Quantitative
Association Rules that meet the minimum support and interestingness
requirements. Such rules can be of great importance to marketing departments
seeking to optimize targeted campaigns, or general market segmentation. They
can also be of value in medical applications, financial as well as predictive
maintenance domains. We provide computational results showing the scalability
of our algorithm, and its capability to produce all rules to be found in large
scale synthetic and real world datasets such as Movie Lens, within a few
seconds or minutes of computational time on commodity hardware.
A General Account of Argumentation with Preferences
Authors: Sanjay Modgil, Henry Prakken
Date: 2018-04-18 00:00:00

This paper builds on the recent ASPIC+ formalism, to develop a general
framework for argumentation with preferences. We motivate a revised definition
of conflict free sets of arguments, adapt ASPIC+ to accommodate a broader range
of instantiating logics, and show that under some assumptions, the resulting
framework satisfies key properties and rationality postulates. We then show
that the generalised framework accommodates Tarskian logic instantiations
extended with preferences, and then study instantiations of the framework by
classical logic approaches to argumentation. We conclude by arguing that
ASPIC+'s modelling of defeasible inference rules further testifies to the
generality of the framework, and then examine and counter recent critiques of
Dung's framework and its extensions to accommodate preferences.
Simulation-based Adversarial Test Generation for Autonomous Vehicles with Machine Learning Components
Authors: Cumhur Erkan Tuncali, Georgios Fainekos, Hisahiro Ito, James Kapinski
Date: 2018-04-18 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, BIG-bench Machine Learning

Many organizations are developing autonomous driving systems, which are
expected to be deployed at a large scale in the near future. Despite this,
there is a lack of agreement on appropriate methods to test, debug, and certify
the performance of these systems. One of the main challenges is that many
autonomous driving systems have machine learning components, such as deep
neural networks, for which formal properties are difficult to characterize. We
present a testing framework that is compatible with test case generation and
automatic falsification methods, which are used to evaluate cyber-physical
systems. We demonstrate how the framework can be used to evaluate closed-loop
properties of an autonomous driving system model that includes the ML
components, all within a virtual environment. We demonstrate how to use test
case generation methods, such as covering arrays, as well as requirement
falsification methods to automatically identify problematic test scenarios. The
resulting framework can be used to increase the reliability of autonomous
driving systems.
Forecasting the presence and intensity of hostility on Instagram using linguistic and social features
Authors: Ping Liu, Joshua Guberman, Libby Hemphill, Aron Culotta
Date: 2018-04-18 00:00:00
Tasks: Task 2

Online antisocial behavior, such as cyberbullying, harassment, and trolling,
is a widespread problem that threatens free discussion and has negative
physical and mental health consequences for victims and communities. While
prior work has proposed automated methods to identify hostile comments in
online discussions, these methods work retrospectively on comments that have
already been posted, making it difficult to intervene before an interaction
escalates. In this paper we instead consider the problem of forecasting future
hostilities in online discussions, which we decompose into two tasks: (1) given
an initial sequence of non-hostile comments in a discussion, predict whether
some future comment will contain hostility; and (2) given the first hostile
comment in a discussion, predict whether this will lead to an escalation of
hostility in subsequent comments. Thus, we aim to forecast both the presence
and intensity of hostile comments based on linguistic and social features from
earlier comments. To evaluate our approach, we introduce a corpus of over 30K
annotated Instagram comments from over 1,100 posts. Our approach is able to
predict the appearance of a hostile comment on an Instagram post ten or more
hours in the future with an AUC of .82 (task 1), and can furthermore
distinguish between high and low levels of future hostility with an AUC of .91
(task 2).