pwc-archive/papers-with-abstracts huggingface dataset:
Interpretable Transformations with Encoder-Decoder Networks
Authors: Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov, Gabriel J. Brostow
Date: 2017-10-19 00:00:00
Proceeding: ICCV 2017 10
Tasks: Decoder

Deep feature spaces have the capacity to encode complex transformations of
their input data. However, understanding the relative feature-space
relationship between two transformed encoded images is difficult. For instance,
what is the relative feature space relationship between two rotated images?
What is decoded when we interpolate in feature space? Ideally, we want to
disentangle confounding factors, such as pose, appearance, and illumination,
from object identity. Disentangling these is difficult because they interact in
very nonlinear ways. We propose a simple method to construct a deep feature
space, with explicitly disentangled representations of several known
transformations. A person or algorithm can then manipulate the disentangled
representation, for example, to re-render an image with explicit control over
parameterized degrees of freedom. The feature space is constructed using a
transforming encoder-decoder network with a custom feature transform layer,
acting on the hidden representations. We demonstrate the advantages of explicit
disentangling on a variety of datasets and transformations, and as an aid for
traditional tasks, such as classification.
Time Series Prediction : Predicting Stock Price
Authors: Aaron Elliot, Cheng Hua Hsu
Date: 2017-10-16 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis, Time Series Forecasting, Time Series Prediction

Time series forecasting is widely used in a multitude of domains. In this
paper, we present four models to predict the stock price using the SPX index as
input time series data. The martingale and ordinary linear models require the
strongest assumption in stationarity which we use as baseline models. The
generalized linear model requires lesser assumptions but is unable to
outperform the martingale. In empirical testing, the RNN model performs the
best comparing to other two models, because it will update the input through
LSTM instantaneously, but also does not beat the martingale. In addition, we
introduce an online to batch algorithm and discrepancy measure to inform
readers the newest research in time series predicting method, which doesn't
require any stationarity or non mixing assumptions in time series data.
Finally, to apply these forecasting to practice, we introduce basic trading
strategies that can create Win win and Zero sum situations.
Recent Advances in Convolutional Neural Networks
Authors: Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Li Wang, Gang Wang, Jianfei Cai, Tsuhan Chen
Date: 2015-12-22 00:00:00
Tasks: speech-recognition, Speech Recognition

In the last few years, deep learning has led to very good performance on a
variety of problems, such as visual recognition, speech recognition and natural
language processing. Among different types of deep neural networks,
convolutional neural networks have been most extensively studied. Leveraging on
the rapid growth in the amount of the annotated data and the great improvements
in the strengths of graphics processor units, the research on convolutional
neural networks has been emerged swiftly and achieved state-of-the-art results
on various tasks. In this paper, we provide a broad survey of the recent
advances in convolutional neural networks. We detailize the improvements of CNN
on different aspects, including layer design, activation function, loss
function, regularization, optimization and fast computation. Besides, we also
introduce various applications of convolutional neural networks in computer
vision, speech and natural language processing.
Dress like a Star: Retrieving Fashion Products from Videos
Authors: Noa Garcia, George Vogiatzis
Date: 2017-10-19 00:00:00
Tasks: Retrieval

This work proposes a system for retrieving clothing and fashion products from
video content. Although films and television are the perfect showcase for
fashion brands to promote their products, spectators are not always aware of
where to buy the latest trends they see on screen. Here, a framework for
breaking the gap between fashion products shown on videos and users is
presented. By relating clothing items and video frames in an indexed database
and performing frame retrieval with temporal aggregation and fast indexing
techniques, we can find fashion products from videos in a simple and
non-intrusive way. Experiments in a large-scale dataset conducted here show
that, by using the proposed framework, memory requirements can be reduced by
42.5X with respect to linear search, whereas accuracy is maintained at around
90%.
Block DCT filtering using vector processing
Authors: Mostafa Amin-Naji, Ali Aghagolzadeh
Date: 2017-10-19 00:00:00

Filtering is an important issue in signals and images processing. Many images
and videos are compressed using discrete cosine transform (DCT). For reducing
the computation complexity, we are interested in filtering block and images
directly in DCT domain. This article proposed an efficient and yet very simple
filtering method directly in DCT domain for any symmetric, asymmetric,
separable, inseparable and one or two dimensional filter. The proposed method
is achieved by mathematical relations using vector processing for image
filtering which it is equivalent to the spatial domain zero padding filtering.
Also to avoid the zero padding artifacts around the edge of the block, we
prepare preliminary matrices in DCT domain by implementation elements of
selected mask which satisfies border replication for a block in the spatial
domain. To evaluate the performance of the proposed algorithm, we compared the
spatial domain filtering results with the results of the proposed method in DCT
domain. The experiments show that the results of our proposed method in DCT are
exactly the same as the spatial domain filtering.
Findings of the Second Shared Task on Multimodal Machine Translation and Multilingual Image Description
Authors: Desmond Elliott, Stella Frank, Loïc Barrault, Fethi Bougares, Lucia Specia
Date: 2017-10-19 00:00:00
Proceeding: WS 2017 9
Tasks: Image Description, Machine Translation, Multimodal Machine Translation, Sentence, Translation

We present the results from the second shared task on multimodal machine
translation and multilingual image description. Nine teams submitted 19 systems
to two tasks. The multimodal translation task, in which the source sentence is
supplemented by an image, was extended with a new language (French) and two new
test sets. The multilingual image description task was changed such that at
test time, only the image is given. Compared to last year, multimodal systems
improved, but text-only systems remain competitive.
Combining Multiple Views for Visual Speech Recognition
Authors: Marina Zimmermann, Mostafa Mehdipour Ghazi, Hazim Kemal Ekenel, Jean-Philippe Thiran
Date: 2017-10-19 00:00:00
Tasks: Sentence, speech-recognition, Speech Recognition, Visual Speech Recognition

Visual speech recognition is a challenging research problem with a particular
practical application of aiding audio speech recognition in noisy scenarios.
Multiple camera setups can be beneficial for the visual speech recognition
systems in terms of improved performance and robustness. In this paper, we
explore this aspect and provide a comprehensive study on combining multiple
views for visual speech recognition. The thorough analysis covers fusion of all
possible view angle combinations both at feature level and decision level. The
employed visual speech recognition system in this study extracts features
through a PCA-based convolutional neural network, followed by an LSTM network.
Finally, these features are processed in a tandem system, being fed into a
GMM-HMM scheme. The decision fusion acts after this point by combining the
Viterbi path log-likelihoods. The results show that the complementary
information contained in recordings from different view angles improves the
results significantly. For example, the sentence correctness on the test set is
increased from 76% for the highest performing single view ($30^\circ$) to up to
83% when combining this view with the frontal and $60^\circ$ view angles.
Visual Speech Recognition Using PCA Networks and LSTMs in a Tandem GMM-HMM System
Authors: Marina Zimmermann, Mostafa Mehdipour Ghazi, Hazim Kemal Ekenel, Jean-Philippe Thiran
Date: 2017-10-19 00:00:00
Tasks: Sentence, speech-recognition, Speech Recognition, Visual Speech Recognition

Automatic visual speech recognition is an interesting problem in pattern
recognition especially when audio data is noisy or not readily available. It is
also a very challenging task mainly because of the lower amount of information
in the visual articulations compared to the audible utterance. In this work,
principle component analysis is applied to the image patches - extracted from
the video data - to learn the weights of a two-stage convolutional network.
Block histograms are then extracted as the unsupervised learning features.
These features are employed to learn a recurrent neural network with a set of
long short-term memory cells to obtain spatiotemporal features. Finally, the
obtained features are used in a tandem GMM-HMM system for speech recognition.
Our results show that the proposed method has outperformed the baseline
techniques applied to the OuluVS2 audiovisual database for phrase recognition
with the frontal view cross-validation and testing sentence correctness
reaching 79% and 73%, respectively, as compared to the baseline of 74% on
cross-validation.
NNVLP: A Neural Network-Based Vietnamese Language Processing Toolkit
Authors: Thai-Hoang Pham, Xuan-Khoai Pham, Tuan-Anh Nguyen, Phuong Le-Hong
Date: 2017-08-24 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Chunking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Part-Of-Speech Tagging, POS, POS Tagging, Word Embeddings

This paper demonstrates neural network-based toolkit namely NNVLP for
essential Vietnamese language processing tasks including part-of-speech (POS)
tagging, chunking, named entity recognition (NER). Our toolkit is a combination
of bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network
(CNN), Conditional Random Field (CRF), using pre-trained word embeddings as
input, which achieves state-of-the-art results on these three tasks. We provide
both API and web demo for this toolkit.
Clustering with Missing Features: A Penalized Dissimilarity Measure based approach
Authors: Shounak Datta, Supritam Bhattacharjee, Swagatam Das
Date: 2016-04-22 00:00:00
Tasks: Clustering, Imputation

Many real-world clustering problems are plagued by incomplete data
characterized by missing or absent features for some or all of the data
instances. Traditional clustering methods cannot be directly applied to such
data without preprocessing by imputation or marginalization techniques. In this
article, we overcome this drawback by utilizing a penalized dissimilarity
measure which we refer to as the Feature Weighted Penalty based Dissimilarity
(FWPD). Using the FWPD measure, we modify the traditional k-means clustering
algorithm and the standard hierarchical agglomerative clustering algorithms so
as to make them directly applicable to datasets with missing features. We
present time complexity analyses for these new techniques and also undertake a
detailed theoretical analysis showing that the new FWPD based k-means algorithm
converges to a local optimum within a finite number of iterations. We also
present a detailed method for simulating random as well as feature dependent
missingness. We report extensive experiments on various benchmark datasets for
different types of missingness showing that the proposed clustering techniques
have generally better results compared to some of the most well-known
imputation methods which are commonly used to handle such incomplete data. We
append a possible extension of the proposed dissimilarity measure to the case
of absent features (where the unobserved features are known to be undefined).
Swift Linked Data Miner: Mining OWL 2 EL class expressions directly from online RDF datasets
Authors: Jedrzej Potoniec, Piotr Jakubowski, Agnieszka Ławrynowicz
Date: 2017-10-19 00:00:00

In this study, we present Swift Linked Data Miner, an interruptible algorithm
that can directly mine an online Linked Data source (e.g., a SPARQL endpoint)
for OWL 2 EL class expressions to extend an ontology with new SubClassOf:
axioms. The algorithm works by downloading only a small part of the Linked Data
source at a time, building a smart index in the memory and swiftly iterating
over the index to mine axioms. We propose a transformation function from mined
axioms to RDF Data Shapes. We show, by means of a crowdsourcing experiment,
that most of the axioms mined by Swift Linked Data Miner are correct and can be
added to an ontology. We provide a ready to use Prot\'eg\'e plugin implementing
the algorithm, to support ontology engineers in their daily modeling work.
Meta-Learning via Feature-Label Memory Network
Authors: Dawit Mureja, Hyunsin Park, Chang D. Yoo
Date: 2017-10-19 00:00:00
Tasks: Meta-Learning

Deep learning typically requires training a very capable architecture using
large datasets. However, many important learning problems demand an ability to
draw valid inferences from small size datasets, and such problems pose a
particular challenge for deep learning. In this regard, various researches on
"meta-learning" are being actively conducted. Recent work has suggested a
Memory Augmented Neural Network (MANN) for meta-learning. MANN is an
implementation of a Neural Turing Machine (NTM) with the ability to rapidly
assimilate new data in its memory, and use this data to make accurate
predictions. In models such as MANN, the input data samples and their
appropriate labels from previous step are bound together in the same memory
locations. This often leads to memory interference when performing a task as
these models have to retrieve a feature of an input from a certain memory
location and read only the label information bound to that location. In this
paper, we tried to address this issue by presenting a more robust MANN. We
revisited the idea of meta-learning and proposed a new memory augmented neural
network by explicitly splitting the external memory into feature and label
memories. The feature memory is used to store the features of input data
samples and the label memory stores their labels. Hence, when predicting the
label of a given input, our model uses its feature memory unit as a reference
to extract the stored feature of the input, and based on that feature, it
retrieves the label information of the input from the label memory unit. In
order for the network to function in this framework, a new memory-writingmodule
to encode label information into the label memory in accordance with the
meta-learning task structure is designed. Here, we demonstrate that our model
outperforms MANN by a large margin in supervised one-shot classification tasks
using Omniglot and MNIST datasets.
Sea Level Anomaly Prediction using Recurrent Neural Networks
Authors: Anne Braakmann-Folgmann, Ribana Roscher, Susanne Wenzel, Bernd Uebbing, Jürgen Kusche
Date: 2017-10-19 00:00:00
Tasks: Prediction

Sea level change, one of the most dire impacts of anthropogenic global
warming, will affect a large amount of the world's population. However, sea
level change is not uniform in time and space, and the skill of conventional
prediction methods is limited due to the ocean's internal variabi-lity on
timescales from weeks to decades. Here we study the potential of neural network
methods which have been used successfully in other applications, but rarely
been applied for this task. We develop a combination of a convolutional neural
network (CNN) and a recurrent neural network (RNN) to ana-lyse both the spatial
and the temporal evolution of sea level and to suggest an independent, accurate
method to predict interannual sea level anomalies (SLA). We test our method for
the northern and equatorial Pacific Ocean, using gridded altimeter-derived SLA
data. We show that the used network designs outperform a simple regression and
that adding a CNN improves the skill significantly. The predictions are stable
over several years.
Decision Trees for Helpdesk Advisor Graphs
Authors: Spyros Gkezerlis, Dimitris Kalles
Date: 2017-10-19 00:00:00
Tasks: Decision Making

We use decision trees to build a helpdesk agent reference network to
facilitate the on-the-job advising of junior or less experienced staff on how
to better address telecommunication customer fault reports. Such reports
generate field measurements and remote measurements which, when coupled with
location data and client attributes, and fused with organization-level
statistics, can produce models of how support should be provided. Beyond
decision support, these models can help identify staff who can act as advisors,
based on the quality, consistency and predictability of dealing with complex
troubleshooting reports. Advisor staff models are then used to guide less
experienced staff in their decision making; thus, we advocate the deployment of
a simple mechanism which exploits the availability of staff with a sound track
record at the helpdesk to act as dormant tutors.
Reti bayesiane per lo studio del fenomeno degli incidenti stradali tra i giovani in Toscana
Authors: Filippo Elba, Lisa Gnaulati, Fabio Voeller
Date: 2017-10-19 00:00:00
Tasks: BIG-bench Machine Learning

This paper aims to analyse adolescents' road accidents in Tuscany. The
analysis is based on the Database Edit of Osservatorio di Epidemiologia della
Toscana. Complexity and heterogeneity of Edit's data represet an interesting
scope to apply Machine Learning methods. In particular, in this paper is
proposed an analysis based on a Bayesian probabilistic network, used to
discover relationships between adolescents' characteristics and behaviours that
are more often associated with an audacious driving style. The probabilistic
network developed by this study can be considered a useful starting point for
follow up reasearches, aiming to develop a causal network, a tool to limit this
phenomenon.
Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings
Authors: Pieter Fivez, Simon Šuster, Walter Daelemans
Date: 2017-10-19 00:00:00
Tasks: Spelling Correction

We present an unsupervised context-sensitive spelling correction method for
clinical free-text that uses word and character n-gram embeddings. Our method
generates misspelling replacement candidates and ranks them according to their
semantic fit, by calculating a weighted cosine similarity between the
vectorized representation of a candidate and the misspelling context. To tune
the parameters of this model, we generate self-induced spelling error corpora.
We perform our experiments for two languages. For English, we greatly
outperform off-the-shelf spelling correction tools on a manually annotated
MIMIC-III test set, and counter the frequency bias of a noisy channel model,
showing that neural embeddings can be successfully exploited to improve upon
the state-of-the-art. For Dutch, we also outperform an off-the-shelf spelling
correction tool on manually annotated clinical records from the Antwerp
University Hospital, but can offer no empirical evidence that our method
counters the frequency bias of a noisy channel model in this case as well.
However, both our context-sensitive model and our implementation of the noisy
channel model obtain high scores on the test set, establishing a
state-of-the-art for Dutch clinical spelling correction with the noisy channel
model.
Generative Adversarial Networks: An Overview
Authors: Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, Anil A. Bharath
Date: 2017-10-19 00:00:00
Tasks: General Classification, Image Generation, Image Super-Resolution, Style Transfer, Super-Resolution

Generative adversarial networks (GANs) provide a way to learn deep
representations without extensively annotated training data. They achieve this
through deriving backpropagation signals through a competitive process
involving a pair of networks. The representations that can be learned by GANs
may be used in a variety of applications, including image synthesis, semantic
image editing, style transfer, image super-resolution and classification. The
aim of this review paper is to provide an overview of GANs for the signal
processing community, drawing on familiar analogies and concepts where
possible. In addition to identifying different methods for training and
constructing GANs, we also point to remaining challenges in their theory and
application.
Recognizing Abnormal Heart Sounds Using Deep Learning
Authors: Jonathan Rubin, Rui Abreu, Anurag Ganguli, Saigopal Nelaturi, Ion Matei, Kumar Sricharan
Date: 2017-07-14 00:00:00
Tasks: Deep Learning, Sensitivity, Sound Classification, Specificity

The work presented here applies deep learning to the task of automated
cardiac auscultation, i.e. recognizing abnormalities in heart sounds. We
describe an automated heart sound classification algorithm that combines the
use of time-frequency heat map representations with a deep convolutional neural
network (CNN). Given the cost-sensitive nature of misclassification, our CNN
architecture is trained using a modified loss function that directly optimizes
the trade-off between sensitivity and specificity. We evaluated our algorithm
at the 2016 PhysioNet Computing in Cardiology challenge where the objective was
to accurately classify normal and abnormal heart sounds from single, short,
potentially noisy recordings. Our entry to the challenge achieved a final
specificity of 0.95, sensitivity of 0.73 and overall score of 0.84. We achieved
the greatest specificity score out of all challenge entries and, using just a
single CNN, our algorithm differed in overall score by only 0.02 compared to
the top place finisher, which used an ensemble approach.
SLING: A framework for frame semantic parsing
Authors: Michael Ringgaard, Rahul Gupta, Fernando C. N. Pereira
Date: 2017-10-19 00:00:00
Tasks: Semantic Frame Parsing, Semantic Parsing

We describe SLING, a framework for parsing natural language into semantic
frames. SLING supports general transition-based, neural-network parsing with
bidirectional LSTM input encoding and a Transition Based Recurrent Unit (TBRU)
for output decoding. The parsing model is trained end-to-end using only the
text tokens as input. The transition system has been designed to output frame
graphs directly without any intervening symbolic representation. The SLING
framework includes an efficient and scalable frame store implementation as well
as a neural network JIT compiler for fast inference during parsing. SLING is
implemented in C++ and it is available for download on GitHub.