pwc-archive/papers-with-abstracts huggingface dataset:
Exploring Latent Semantic Factors to Find Useful Product Reviews
Authors: Subhabrata Mukherjee, Kashyap Popat, Gerhard Weikum
Date: 2017-05-06 00:00:00

Online reviews provided by consumers are a valuable asset for e-Commerce
platforms, influencing potential consumers in making purchasing decisions.
However, these reviews are of varying quality, with the useful ones buried deep
within a heap of non-informative reviews. In this work, we attempt to
automatically identify review quality in terms of its helpfulness to the end
consumers. In contrast to previous works in this domain exploiting a variety of
syntactic and community-level features, we delve deep into the semantics of
reviews as to what makes them useful, providing interpretable explanation for
the same. We identify a set of consistency and semantic factors, all from the
text, ratings, and timestamps of user-generated reviews, making our approach
generalizable across all communities and domains. We explore review semantics
in terms of several latent factors like the expertise of its author, his
judgment about the fine-grained facets of the underlying product, and his
writing style. These are cast into a Hidden Markov Model -- Latent Dirichlet
Allocation (HMM-LDA) based model to jointly infer: (i) reviewer expertise, (ii)
item facets, and (iii) review helpfulness. Large-scale experiments on five
real-world datasets from Amazon show significant improvement over
state-of-the-art baselines in predicting and ranking useful reviews.
A neural network system for transformation of regional cuisine style
Authors: Masahiro Kazama, Minami Sugimoto, Chizuru Hosokawa, Keisuke Matsushima, Lav R. Varshney, Yoshiki Ishikawa
Date: 2017-05-06 00:00:00

We propose a novel system which can transform a recipe into any selected
regional style (e.g., Japanese, Mediterranean, or Italian). This system has two
characteristics. First the system can identify the degree of regional cuisine
style mixture of any selected recipe and visualize such regional cuisine style
mixtures using barycentric Newton diagrams. Second, the system can suggest
ingredient substitutions through an extended word2vec model, such that a recipe
becomes more authentic for any selected regional cuisine style. Drawing on a
large number of recipes from Yummly, an example shows how the proposed system
can transform a traditional Japanese recipe, Sukiyaki, into French style.
ApproxDBN: Approximate Computing for Discriminative Deep Belief Networks
Authors: Xiaojing Xu, Srinjoy Das, Ken Kreutz-Delgado
Date: 2017-04-13 00:00:00
Tasks: General Classification, image-classification, Image Classification, speech-recognition, Speech Recognition

Probabilistic generative neural networks are useful for many applications,
such as image classification, speech recognition and occlusion removal.
However, the power budget for hardware implementations of neural networks can
be extremely tight. To address this challenge we describe a design methodology
for using approximate computing methods to implement Approximate Deep Belief
Networks (ApproxDBNs) by systematically exploring the use of (1) limited
precision of variables; (2) criticality analysis to identify the nodes in the
network which can operate with such limited precision while allowing the
network to maintain target accuracy levels; and (3) a greedy search methodology
with incremental retraining to determine the optimal reduction in precision to
enable maximize power savings under user-specified accuracy constraints.
Experimental results show that significant bit-length reduction can be achieved
by our ApproxDBN with constrained accuracy loss.
DeepDeath: Learning to Predict the Underlying Cause of Death with Big Data
Authors: Hamid Reza Hassanzadeh, Ying Sha, May D. Wang
Date: 2017-05-06 00:00:00

Multiple cause-of-death data provides a valuable source of information that
can be used to enhance health standards by predicting health related
trajectories in societies with large populations. These data are often
available in large quantities across U.S. states and require Big Data
techniques to uncover complex hidden patterns. We design two different classes
of models suitable for large-scale analysis of mortality data, a Hadoop-based
ensemble of random forests trained over N-grams, and the DeepDeath, a deep
classifier based on the recurrent neural network (RNN). We apply both classes
to the mortality data provided by the National Center for Health Statistics and
show that while both perform significantly better than the random classifier,
the deep model that utilizes long short-term memory networks (LSTMs), surpasses
the N-gram based models and is capable of learning the temporal aspect of the
data without a need for building ad-hoc, expert-driven features.
Context-Aware Trajectory Prediction
Authors: Federico Bartoli, Giuseppe Lisanti, Lamberto Ballan, Alberto del Bimbo
Date: 2017-05-06 00:00:00
Tasks: Navigate, Prediction, Trajectory Prediction

Human motion and behaviour in crowded spaces is influenced by several
factors, such as the dynamics of other moving agents in the scene, as well as
the static elements that might be perceived as points of attraction or
obstacles. In this work, we present a new model for human trajectory prediction
which is able to take advantage of both human-human and human-space
interactions. The future trajectory of humans, are generated by observing their
past positions and interactions with the surroundings. To this end, we propose
a "context-aware" recurrent neural network LSTM model, which can learn and
predict human motion in crowded spaces such as a sidewalk, a museum or a
shopping mall. We evaluate our model on a public pedestrian datasets, and we
contribute a new challenging dataset that collects videos of humans that
navigate in a (real) crowded space such as a big museum. Results show that our
approach can predict human trajectories better when compared to previous
state-of-the-art forecasting models.
A Study and Comparison of Human and Deep Learning Recognition Performance Under Visual Distortions
Authors: Samuel Dodge, Lina Karam
Date: 2017-05-06 00:00:00
Tasks: General Classification

Deep neural networks (DNNs) achieve excellent performance on standard
classification tasks. However, under image quality distortions such as blur and
noise, classification accuracy becomes poor. In this work, we compare the
performance of DNNs with human subjects on distorted images. We show that,
although DNNs perform better than or on par with humans on good quality images,
DNN performance is still much lower than human performance on distorted images.
We additionally find that there is little correlation in errors between DNNs
and human subjects. This could be an indication that the internal
representation of images are different between DNNs and the human visual
system. These comparisons with human performance could be used to guide future
development of more robust DNNs.
Learning Character-level Compositionality with Visual Features
Authors: Frederick Liu, Han Lu, Chieh Lo, Graham Neubig
Date: 2017-04-17 00:00:00
Proceeding: ACL 2017 7
Tasks: text-classification, Text Classification

Previous work has modeled the compositionality of words by creating
character-level models of meaning, reducing problems of sparsity for rare
words. However, in many writing systems compositionality has an effect even on
the character-level: the meaning of a character is derived by the sum of its
parts. In this paper, we model this effect by creating embeddings for
characters based on their visual characteristics, creating an image for the
character and running it through a convolutional neural network to produce a
visual character embedding. Experiments on a text classification task
demonstrate that such model allows for better processing of instances with rare
characters in languages such as Chinese, Japanese, and Korean. Additionally,
qualitative analyses demonstrate that our proposed model learns to focus on the
parts of characters that carry semantic content, resulting in embeddings that
are coherent in visual space.
Metacognitive Learning Approach for Online Tool Condition Monitoring
Authors: Mahardhika Pratama, Eric Dimla, Chow Yin Lai, Edwin Lughofer
Date: 2017-05-06 00:00:00

As manufacturing processes become increasingly automated, so should tool
condition monitoring (TCM) as it is impractical to have human workers monitor
the state of the tools continuously. Tool condition is crucial to ensure the
good quality of products: Worn tools affect not only the surface quality but
also the dimensional accuracy, which means higher reject rate of the products.
Therefore, there is an urgent need to identify tool failures before it occurs
on the fly. While various versions of intelligent tool condition monitoring
have been proposed, most of them suffer from a cognitive nature of traditional
machine learning algorithms. They focus on the how to learn process without
paying attention to other two crucial issues: what to learn, and when to learn.
The what to learn and the when to learn provide self regulating mechanisms to
select the training samples and to determine time instants to train a model. A
novel tool condition monitoring approach based on a psychologically plausible
concept, namely the metacognitive scaffolding theory, is proposed and built
upon a recently published algorithm, recurrent classifier (rClass). The
learning process consists of three phases: what to learn, how to learn, when to
learn and makes use of a generalized recurrent network structure as a cognitive
component. Experimental studies with real-world manufacturing data streams were
conducted where rClass demonstrated the highest accuracy while retaining the
lowest complexity over its counterparts.
PANFIS++: A Generalized Approach to Evolving Learning
Authors: Mahardhika Pratama
Date: 2017-05-06 00:00:00
Tasks: Active Learning

The concept of evolving intelligent system (EIS) provides an effective avenue
for data stream mining because it is capable of coping with two prominent
issues: online learning and rapidly changing environments. We note at least
three uncharted territories of existing EISs: data uncertainty, temporal system
dynamic, redundant data streams. This book chapter aims at delivering a
concrete solution of this problem with the algorithmic development of a novel
learning algorithm, namely PANFIS++. PANFIS++ is a generalized version of the
PANFIS by putting forward three important components: 1) An online active
learning scenario is developed to overcome redundant data streams. This module
allows to actively select data streams for the training process, thereby
expediting execution time and enhancing generalization performance, 2) PANFIS++
is built upon an interval type-2 fuzzy system environment, which incorporates
the so-called footprint of uncertainty. This component provides a degree of
tolerance for data uncertainty. 3) PANFIS++ is structured under a recurrent
network architecture with a self-feedback loop. This is meant to tackle the
temporal system dynamic. The efficacy of the PANFIS++ has been numerically
validated through numerous real-world and synthetic case studies, where it
delivers the highest predictive accuracy while retaining the lowest complexity.
Parsimonious Random Vector Functional Link Network for Data Streams
Authors: Mahardhika Pratama, Plamen P. Angelov, Edwin Lughofer
Date: 2017-04-10 00:00:00
Tasks: Active Learning

The theory of random vector functional link network (RVFLN) has provided a
breakthrough in the design of neural networks (NNs) since it conveys solid
theoretical justification of randomized learning. Existing works in RVFLN are
hardly scalable for data stream analytics because they are inherent to the
issue of complexity as a result of the absence of structural learning
scenarios. A novel class of RVLFN, namely parsimonious random vector functional
link network (pRVFLN), is proposed in this paper. pRVFLN features an open
structure paradigm where its network structure can be built from scratch and
can be automatically generated in accordance with degree of nonlinearity and
time-varying property of system being modelled. pRVFLN is equipped with
complexity reduction scenarios where inconsequential hidden nodes can be pruned
and input features can be dynamically selected. pRVFLN puts into perspective an
online active learning mechanism which expedites the training process and
relieves operator labelling efforts. In addition, pRVFLN introduces a
non-parametric type of hidden node, developed using an interval-valued data
cloud. The hidden node completely reflects the real data distribution and is
not constrained by a specific shape of the cluster. All learning procedures of
pRVFLN follow a strictly single-pass learning mode, which is applicable for an
online real-time deployment. The efficacy of pRVFLN was rigorously validated
through numerous simulations and comparisons with state-of-the art algorithms
where it produced the most encouraging numerical results. Furthermore, the
robustness of pRVFLN was investigated and a new conclusion is made to the scope
of random parameters where it plays vital role to the success of randomized
learning.
Low-Autocorrelation Binary Sequences: On Improved Merit Factors and Runtime Predictions to Achieve Them
Authors: Borko Bošković, Franc Brglez, Janez Brest
Date: 2014-06-20 00:00:00

The search for binary sequences with a high figure of merit, known as the low
autocorrelation binary sequence ($labs$}) problem, represents a formidable
computational challenge. To mitigate the computational constraints of the
problem, we consider solvers that accept odd values of sequence length $L$ and
return solutions for skew-symmetric binary sequences only -- with the
consequence that not all best solutions under this constraint will be optimal
for each $L$. In order to improve both, the search for best merit factor $and$
the asymptotic runtime performance, we instrumented three stochastic solvers,
the first two are state-of-the-art solvers that rely on variants of memetic and
tabu search ($lssMAts$ and $lssRRts$), the third solver ($lssOrel$) organizes
the search as a sequence of independent contiguous self-avoiding walk segments.
By adapting a rigorous statistical methodology to performance testing of all
three combinatorial solvers, experiments show that the solver with the best
asymptotic average-case performance, $lssOrel\_8 = 0.000032*1.1504^L$, has the
best chance of finding solutions that improve, as $L$ increases, figures of
merit reported to date. The same methodology can be applied to engineering new
$labs$ solvers that may return merit factors even closer to the conjectured
asymptotic value of 12.3248.
Image Annotation using Multi-Layer Sparse Coding
Authors: Amara Tariq, Hassan Foroosh
Date: 2017-05-06 00:00:00
Tasks: Descriptive, Image Retrieval, Retrieval

Automatic annotation of images with descriptive words is a challenging
problem with vast applications in the areas of image search and retrieval. This
problem can be viewed as a label-assignment problem by a classifier dealing
with a very large set of labels, i.e., the vocabulary set. We propose a novel
annotation method that employs two layers of sparse coding and performs
coarse-to-fine labeling. Themes extracted from the training data are treated as
coarse labels. Each theme is a set of training images that share a common
subject in their visual and textual contents. Our system extracts coarse labels
for training and test images without requiring any prior knowledge. Vocabulary
words are the fine labels to be associated with images. Most of the annotation
methods achieve low recall due to the large number of available fine labels,
i.e., vocabulary words. These systems also tend to achieve high precision for
highly frequent words only while relatively rare words are more important for
search and retrieval purposes. Our system not only outperforms various
previously proposed annotation systems, but also achieves symmetric response in
terms of precision and recall. Our system scores and maintains high precision
for words with a wide range of frequencies. Such behavior is achieved by
intelligently reducing the number of available fine labels or words for each
image based on coarse labels assigned to it.
Towards String-to-Tree Neural Machine Translation
Authors: Roee Aharoni, Yoav Goldberg
Date: 2017-04-16 00:00:00
Proceeding: ACL 2017 7
Tasks: Machine Translation, NMT, Translation

We present a simple method to incorporate syntactic information about the
target language in a neural machine translation system by translating into
linearized, lexicalized constituency trees. An experiment on the WMT16
German-English news translation task resulted in an improved BLEU score when
compared to a syntax-agnostic NMT baseline trained on the same dataset. An
analysis of the translations from the syntax-aware system shows that it
performs more reordering during translation in comparison to the baseline. A
small-scale human evaluation also showed an advantage to the syntax-aware
system.
A Generative Model of a Pronunciation Lexicon for Hindi
Authors: Pramod Pandey, Somnath Roy
Date: 2017-05-06 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, text-to-speech, Text to Speech

Voice browser applications in Text-to- Speech (TTS) and Automatic Speech
Recognition (ASR) systems crucially depend on a pronunciation lexicon. The
present paper describes the model of pronunciation lexicon of Hindi developed
to automatically generate the output forms of Hindi at two levels, the
<phoneme> and the <PS> (PS, in short for Prosodic Structure). The latter level
involves both syllable-division and stress placement. The paper describes the
tool developed for generating the two-level outputs of lexica in Hindi.
On human motion prediction using recurrent neural networks
Authors: Julieta Martinez, Michael J. Black, Javier Romero
Date: 2017-05-06 00:00:00
Proceeding: CVPR 2017 7
Tasks: Human motion prediction, Human Pose Forecasting, Motion Estimation, motion prediction, Motion Synthesis, Prediction

Human motion modelling is a classical problem at the intersection of graphics
and computer vision, with applications spanning human-computer interaction,
motion synthesis, and motion prediction for virtual and augmented reality.
Following the success of deep learning methods in several computer vision
tasks, recent work has focused on using deep recurrent neural networks (RNNs)
to model human motion, with the goal of learning time-dependent representations
that perform tasks such as short-term motion prediction and long-term human
motion synthesis. We examine recent work, with a focus on the evaluation
methodologies commonly used in the literature, and show that, surprisingly,
state-of-the-art performance can be achieved by a simple baseline that does not
attempt to model motion at all. We investigate this result, and analyze recent
RNN methods by looking at the architectures, loss functions, and training
procedures used in state-of-the-art approaches. We propose three changes to the
standard RNN models typically used for human motion, which result in a simple
and scalable RNN architecture that obtains state-of-the-art performance on
human motion prediction.
Comments on `High-dimensional simultaneous inference with the bootstrap'
Authors: Jelena Bradic, Yinchu Zhu
Date: 2017-05-06 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We provide comments on the article "High-dimensional simultaneous inference
with the bootstrap" by Ruben Dezeure, Peter Buhlmann and Cun-Hui Zhang.
Face Super-Resolution Through Wasserstein GANs
Authors: Zhimin Chen, Yuguang Tong
Date: 2017-05-06 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Generative adversarial networks (GANs) have received a tremendous amount of
attention in the past few years, and have inspired applications addressing a
wide range of problems. Despite its great potential, GANs are difficult to
train. Recently, a series of papers (Arjovsky & Bottou, 2017a; Arjovsky et al.
2017b; and Gulrajani et al. 2017) proposed using Wasserstein distance as the
training objective and promised easy, stable GAN training across architectures
with minimal hyperparameter tuning. In this paper, we compare the performance
of Wasserstein distance with other training objectives on a variety of GAN
architectures in the context of single image super-resolution. Our results
agree that Wasserstein GAN with gradient penalty (WGAN-GP) provides stable and
converging GAN training and that Wasserstein distance is an effective metric to
gauge training progress.
Sparse Representation-based Open Set Recognition
Authors: He Zhang, Vishal M. Patel
Date: 2017-05-06 00:00:00
Tasks: Classification, General Classification, Open Set Learning, Sparse Representation-based Classification, Two-sample testing

We propose a generalized Sparse Representation- based Classification (SRC)
algorithm for open set recognition where not all classes presented during
testing are known during training. The SRC algorithm uses class reconstruction
errors for classification. As most of the discriminative information for open
set recognition is hidden in the tail part of the matched and sum of
non-matched reconstruction error distributions, we model the tail of those two
error distributions using the statistical Extreme Value Theory (EVT). Then we
simplify the open set recognition problem into a set of hypothesis testing
problems. The confidence scores corresponding to the tail distributions of a
novel test sample are then fused to determine its identity. The effectiveness
of the proposed method is demonstrated using four publicly available image and
object classification datasets and it is shown that this method can perform
significantly better than many competitive open set recognition algorithms.
Code is public available: https://github.com/hezhangsprinter/SROSR
Neural Discourse Structure for Text Categorization
Authors: Yangfeng Ji, Noah Smith
Date: 2017-02-07 00:00:00
Proceeding: ACL 2017 7
Tasks: Text Categorization

We show that discourse structure, as defined by Rhetorical Structure Theory
and provided by an existing discourse parser, benefits text categorization. Our
approach uses a recursive neural network and a newly proposed attention
mechanism to compute a representation of the text that focuses on salient
content, from the perspective of both RST and the task. Experiments consider
variants of the approach and illustrate its strengths and weaknesses.
Deep Patch Learning for Weakly Supervised Object Classification and Discovery
Authors: Peng Tang, Xinggang Wang, Zilong Huang, Xiang Bai, Wenyu Liu
Date: 2017-05-06 00:00:00
Tasks: Classification, General Classification, Multiple Instance Learning, Object, Object Discovery

Patch-level image representation is very important for object classification
and detection, since it is robust to spatial transformation, scale variation,
and cluttered background. Many existing methods usually require fine-grained
supervisions (e.g., bounding-box annotations) to learn patch features, which
requires a great effort to label images may limit their potential applications.
In this paper, we propose to learn patch features via weak supervisions, i.e.,
only image-level supervisions. To achieve this goal, we treat images as bags
and patches as instances to integrate the weakly supervised multiple instance
learning constraints into deep neural networks. Also, our method integrates the
traditional multiple stages of weakly supervised object classification and
discovery into a unified deep convolutional neural network and optimizes the
network in an end-to-end way. The network processes the two tasks object
classification and discovery jointly, and shares hierarchical deep features.
Through this jointly learning strategy, weakly supervised object classification
and discovery are beneficial to each other. We test the proposed method on the
challenging PASCAL VOC datasets. The results show that our method can obtain
state-of-the-art performance on object classification, and very competitive
results on object discovery, with faster testing speed than competitors.