pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Joint Mining of Deep Features and Image Labels for Large-scale Radiology Image Categorization and Scene Recognition
Authors: Xiaosong Wang, Le Lu, Hoo-chang Shin, Lauren Kim, Mohammadhadi Bagheri, Isabella Nogues, Jianhua Yao, Ronald M. Summers
Date: 2017-01-23 00:00:00
Tasks: Clustering, General Classification, Image Categorization, Scene Classification, Scene Recognition

The recent rapid and tremendous success of deep convolutional neural networks
(CNN) on many challenging computer vision tasks largely derives from the
accessibility of the well-annotated ImageNet and PASCAL VOC datasets.
Nevertheless, unsupervised image categorization (i.e., without the ground-truth
labeling) is much less investigated, yet critically important and difficult
when annotations are extremely hard to obtain in the conventional way of
"Google Search" and crowd sourcing. We address this problem by presenting a
looped deep pseudo-task optimization (LDPO) framework for joint mining of deep
CNN features and image labels. Our method is conceptually simple and rests upon
the hypothesized "convergence" of better labels leading to better trained CNN
models which in turn feed more discriminative image representations to
facilitate more meaningful clusters/labels. Our proposed method is validated in
tackling two important applications: 1) Large-scale medical image annotation
has always been a prohibitively expensive and easily-biased task even for
well-trained radiologists. Significantly better image categorization results
are achieved via our proposed approach compared to the previous
state-of-the-art method. 2) Unsupervised scene recognition on representative
and publicly available datasets with our proposed technique is examined. The
LDPO achieves excellent quantitative scene classification results. On the MIT
indoor scene dataset, it attains a clustering accuracy of 75.3%, compared to
the state-of-the-art supervised classification accuracy of 81.0% (when both are
based on the VGG-VD model).
The information bottleneck and geometric clustering
Authors: DJ Strouse, David J. Schwab
Date: 2017-12-27 00:00:00
Tasks: Clustering, Model Selection

The information bottleneck (IB) approach to clustering takes a joint distribution $P\!\left(X,Y\right)$ and maps the data $X$ to cluster labels $T$ which retain maximal information about $Y$ (Tishby et al., 1999). This objective results in an algorithm that clusters data points based upon the similarity of their conditional distributions $P\!\left(Y\mid X\right)$. This is in contrast to classic "geometric clustering'' algorithms such as $k$-means and gaussian mixture models (GMMs) which take a set of observed data points $\left\{ \mathbf{x}_{i}\right\} _{i=1:N}$ and cluster them based upon their geometric (typically Euclidean) distance from one another. Here, we show how to use the deterministic information bottleneck (DIB) (Strouse and Schwab, 2017), a variant of IB, to perform geometric clustering, by choosing cluster labels that preserve information about data point location on a smoothed dataset. We also introduce a novel method to choose the number of clusters, based on identifying solutions where the tradeoff between number of clusters used and spatial information preserved is strongest. We apply this approach to a variety of simple clustering problems, showing that DIB with our model selection procedure recovers the generative cluster labels. We also show that, in particular limits of our model parameters, clustering with DIB and IB is equivalent to $k$-means and EM fitting of a GMM with hard and soft assignments, respectively. Thus, clustering with (D)IB generalizes and provides an information-theoretic perspective on these classic algorithms.
A Composite Quantile Fourier Neural Network for Multi-Step Probabilistic Forecasting of Nonstationary Univariate Time Series
Authors: Kostas Hatalis, Shalinee Kishore
Date: 2017-12-27 00:00:00
Tasks: Form, Prediction Intervals, quantile regression, regression, Time Series, Time Series Analysis

Point forecasting of univariate time series is a challenging problem with extensive work having been conducted. However, nonparametric probabilistic forecasting of time series, such as in the form of quantiles or prediction intervals is an even more challenging problem. In an effort to expand the possible forecasting paradigms we devise and explore an extrapolation-based approach that has not been applied before for probabilistic forecasting. We present a novel quantile Fourier neural network is for nonparametric probabilistic forecasting of univariate time series. Multi-step predictions are provided in the form of composite quantiles using time as the only input to the model. This effectively is a form of extrapolation based nonlinear quantile regression applied for forecasting. Experiments are conducted on eight real world datasets that demonstrate a variety of periodic and aperiodic patterns. Nine naive and advanced methods are used as benchmarks including quantile regression neural network, support vector quantile regression, SARIMA, and exponential smoothing. The obtained empirical results validate the effectiveness of the proposed method in providing high quality and accurate probabilistic predictions.
Gap Safe screening rules for sparsity enforcing penalties
Authors: Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
Date: 2016-11-17 00:00:00
Tasks: regression

In high dimensional regression settings, sparsity enforcing penalties have
proved useful to regularize the data-fitting term. A recently introduced
technique called screening rules propose to ignore some variables in the
optimization leveraging the expected sparsity of the solutions and consequently
leading to faster solvers. When the procedure is guaranteed not to discard
variables wrongly the rules are said to be safe. In this work, we propose a
unifying framework for generalized linear models regularized with standard
sparsity enforcing penalties such as $\ell_1$ or $\ell_1/\ell_2$ norms. Our
technique allows to discard safely more variables than previously considered
safe rules, particularly for low regularization parameters. Our proposed Gap
Safe rules (so called because they rely on duality gap computation) can cope
with any iterative solver but are particularly well suited to (block)
coordinate descent methods. Applied to many standard learning tasks, Lasso,
Sparse-Group Lasso, multi-task Lasso, binary and multinomial logistic
regression, etc., we report significant speed-ups compared to previously
proposed safe rules on all tested data sets.
An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods
Authors: Mouhammd Al-kasassbeh
Date: 2017-12-27 00:00:00
Tasks: BIG-bench Machine Learning, feature selection, Intrusion Detection, Management

Despite the great developments in information technology, particularly the
Internet, computer networks, global information exchange, and its positive
impact in all areas of daily life, it has also contributed to the development
of penetration and intrusion which forms a high risk to the security of
information organizations, government agencies, and causes large economic
losses. There are many techniques designed for protection such as firewall and
intrusion detection systems (IDS). IDS is a set of software and/or hardware
techniques used to detect hacker's activities in computer systems. Two types of
anomalies are used in IDS to detect intrusive activities different from normal
user behavior. Misuse relies on the knowledge base that contains all known
attack techniques and intrusion is discovered through research in this
knowledge base. Artificial intelligence techniques have been introduced to
improve the performance of these systems. The importance of IDS is to identify
unauthorized access attempting to compromise confidentiality, integrity or
availability of the computer network. This paper investigates the Intrusion
Detection (ID) problem using three machine learning algorithms namely, BayesNet
algorithm, Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM). The
algorithms are applied on a real, Management Information Based (MIB) dataset
that is collected from real life environment. To enhance the detection process
accuracy, a set of feature selection approaches is used; Infogain (IG), ReleifF
(RF), and Genetic Search (GS). Our experiments show that the three feature
selection methods have enhanced the classification performance. GS with
bayesNet, MLP and SVM give high accuracy rates, more specifically the BayesNet
with the GS accuracy rate is 99.9%.
An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework
Authors: O. B. Sezer, M. Ozbayoglu, E. Dogdu
Date: 2017-12-27 00:00:00
Tasks: Stock Price Prediction, Time Series, Time Series Analysis

In this paper, a neural network-based stock price prediction and trading
system using technical analysis indicators is presented. The model developed
first converts the financial time series data into a series of buy-sell-hold
trigger signals using the most commonly preferred technical analysis
indicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN)
model is trained in the learning stage on the daily stock prices between 1997
and 2007 for all of the Dow30 stocks. Apache Spark big data framework is used
in the training stage. The trained model is then tested with data from 2007 to
2017. The results indicate that by choosing the most appropriate technical
indicators, the neural network model can achieve comparable results against the
Buy and Hold strategy in most of the cases. Furthermore, fine tuning the
technical indicators and/or optimization strategy can enhance the overall
trading performance.
DeepIEP: a Peptide Sequence Model of Isoelectric Point (IEP/pI) using Recurrent Neural Networks (RNNs)
Authors: Esben Jannik Bjerrum
Date: 2017-12-27 00:00:00
Tasks: Position, Prediction

The isoelectric point (IEP or pI) is the pH where the net charge on the
molecular ensemble of peptides and proteins is zero. This physical-chemical
property is dependent on protonable/deprotonable sidechains and their pKa
values. Here an pI prediction model is trained from a database of peptide
sequences and pIs using a recurrent neural network (RNN) with long short-term
memory (LSTM) cells. The trained model obtains an RMSE and R$^2$ of 0.28 and
0.95 for the external test set. The model is not based on pKa values, but
prediction of constructed test sequences show similar rankings as already known
pKa values. The prediction depends mostly on the existence of known acidic and
basic amino acids with fine-adjusted based on the neighboring sequence and
position of the charged amino acids in the peptide chain.
Dual Based DSP Bidding Strategy and its Application
Authors: Huahui Liu, Mingrui Zhu, Xiaonan Meng, Yi Hu, Hao Wang
Date: 2017-05-26 00:00:00

In recent years, RTB(Real Time Bidding) becomes a popular online
advertisement trading method. During the auction, each DSP(Demand Side
Platform) is supposed to evaluate current opportunity and respond with an ad
and corresponding bid price. It's essential for DSP to find an optimal ad
selection and bid price determination strategy which maximizes revenue or
performance under budget and ROI(Return On Investment) constraints in P4P(Pay
For Performance) or P4U(Pay For Usage) mode. We solve this problem by 1)
formalizing the DSP problem as a constrained optimization problem, 2) proposing
the augmented MMKP(Multi-choice Multi-dimensional Knapsack Problem) with
general solution, 3) and demonstrating the DSP problem is a special case of the
augmented MMKP and deriving specialized strategy. Our strategy is verified
through simulation and outperforms state-of-the-art strategies in real
application. To the best of our knowledge, our solution is the first dual based
DSP bidding framework that is derived from strict second price auction
assumption and generally applicable to the multiple ads scenario with various
objectives and constraints.
Consensus-based Sequence Training for Video Captioning
Authors: Sang Phan, Gustav Eje Henter, Yusuke Miyao, Shin'ichi Satoh
Date: 2017-12-27 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL), Video Captioning

Captioning models are typically trained using the cross-entropy loss.
However, their performance is evaluated on other metrics designed to better
correlate with human assessments. Recently, it has been shown that
reinforcement learning (RL) can directly optimize these metrics in tasks such
as captioning. However, this is computationally costly and requires specifying
a baseline reward at each step to make training converge. We propose a fast
approach to optimize one's objective of interest through the REINFORCE
algorithm. First we show that, by replacing model samples with ground-truth
sentences, RL training can be seen as a form of weighted cross-entropy loss,
giving a fast, RL-based pre-training algorithm. Second, we propose to use the
consensus among ground-truth captions of the same video as the baseline reward.
This can be computed very efficiently. We call the complete proposal
Consensus-based Sequence Training (CST). Applied to the MSRVTT video captioning
benchmark, our proposals train significantly faster than comparable methods and
establish a new state-of-the-art on the task, improving the CIDEr score from
47.3 to 54.2.
Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project
Authors: Zhimeng Zhang, Jia-Nan Wu, Xuan Zhang, Chi Zhang
Date: 2017-12-27 00:00:00
Tasks: Clustering, Person Re-Identification

Although many methods perform well in single camera tracking, multi-camera
tracking remains a challenging problem with less attention. DukeMTMC is a
large-scale, well-annotated multi-camera tracking benchmark which makes great
progress in this field. This report is dedicated to briefly introduce our
method on DukeMTMC and show that simple hierarchical clustering with
well-trained person re-identification features can get good results on this
dataset.
Co-Morbidity Exploration on Wearables Activity Data Using Unsupervised Pre-training and Multi-Task Learning
Authors: Karan Aggarwal, Shafiq Joty, Luis F. Luque, Jaideep Srivastava
Date: 2017-12-27 00:00:00
Tasks: Decision Making, Management, Multi-Task Learning, Representation Learning, Time Series, Time Series Analysis, Unsupervised Pre-training

Physical activity and sleep play a major role in the prevention and
management of many chronic conditions. It is not a trivial task to understand
their impact on chronic conditions. Currently, data from electronic health
records (EHRs), sleep lab studies, and activity/sleep logs are used. The rapid
increase in the popularity of wearable health devices provides a significant
new data source, making it possible to track the user's lifestyle real-time
through web interfaces, both to consumer as well as their healthcare provider,
potentially. However, at present there is a gap between lifestyle data (e.g.,
sleep, physical activity) and clinical outcomes normally captured in EHRs. This
is a critical barrier for the use of this new source of signal for healthcare
decision making. Applying deep learning to wearables data provides a new
opportunity to overcome this barrier.
  To address the problem of the unavailability of clinical data from a major
fraction of subjects and unrepresentative subject populations, we propose a
novel unsupervised (task-agnostic) time-series representation learning
technique called act2vec. act2vec learns useful features by taking into account
the co-occurrence of activity levels along with periodicity of human activity
patterns. The learned representations are then exploited to boost the
performance of disorder-specific supervised learning models. Furthermore, since
many disorders are often related to each other, a phenomenon referred to as
co-morbidity, we use a multi-task learning framework for exploiting the shared
structure of disorder inducing life-style choices partially captured in the
wearables data. Empirical evaluation using actigraphy data from 4,124 subjects
shows that our proposed method performs and generalizes substantially better
than the conventional time-series symbolic representational methods and
task-specific deep learning models.
Tensor Regression Networks with various Low-Rank Tensor Approximations
Authors: Xingwei Cao, Guillaume Rabusseau
Date: 2017-12-27 00:00:00
Tasks: regression

Tensor regression networks achieve high compression rate of neural networks
while having slight impact on performances. They do so by imposing low tensor
rank structure on the weight matrices of fully connected layers. In recent
years, tensor regression networks have been investigated from the perspective
of their compressive power, however, the regularization effect of enforcing
low-rank tensor structure has not been investigated enough. We study tensor
regression networks using various low-rank tensor approximations, aiming to
compare the compressive and regularization power of different low-rank
constraints. We evaluate the compressive and regularization performances of the
proposed model with both deep and shallow convolutional neural networks. The
outcome of our experiment suggests the superiority of Global Average Pooling
Layer over Tensor Regression Layer when applied to deep convolutional neural
network with CIFAR-10 dataset. On the contrary, shallow convolutional neural
networks with tensor regression layer and dropout achieved lower test error
than both Global Average Pooling and fully-connected layer with dropout
function when trained with a small number of samples.
Improving Text Normalization by Optimizing Nearest Neighbor Matching
Authors: Salman Ahmad Ansari, Usman Zafar, Asim Karim
Date: 2017-12-27 00:00:00
Tasks: Text Normalization

Text normalization is an essential task in the processing and analysis of
social media that is dominated with informal writing. It aims to map informal
words to their intended standard forms. Previously proposed text normalization
approaches typically require manual selection of parameters for improved
performance. In this paper, we present an automatic optimizationbased nearest
neighbor matching approach for text normalization. This approach is motivated
by the observation that text normalization is essentially a matching problem
and nearest neighbor matching with an adaptive similarity function is the most
direct procedure for it. Our similarity function incorporates weighted
contributions of contextual, string, and phonetic similarity, and the nearest
neighbor matching involves a minimum similarity threshold. These four
parameters are tuned efficiently using grid search. We evaluate the performance
of our approach on two benchmark datasets. The results demonstrate that
parameter tuning on small sized labeled datasets produce state-of-the-art text
normalization performances. Thus, this approach allows practically easy
construction of evolving domain-specific normalization lexicons
Steps Towards a Theory of Visual Information: Active Perception, Signal-to-Symbol Conversion and the Interplay Between Sensing and Control
Authors: Stefano Soatto
Date: 2011-10-10 00:00:00

This manuscript describes the elements of a theory of information tailored to
control and decision tasks and specifically to visual data. The concept of
Actionable Information is described, that relates to a notion of information
championed by J. Gibson, and a notion of "complete information" that relates to
the minimal sufficient statistics of a complete representation. It is shown
that the "actionable information gap" between the two can be reduced by
exercising control on the sensing process. Thus, senging, control and
information are inextricably tied. This has consequences in the so-called
"signal-to-symbol barrier" problem, as well as in the analysis and design of
active sensing systems. It has ramifications in vision-based control,
navigation, 3-D reconstruction and rendering, as well as detection,
localization, recognition and categorization of objects and scenes in live
video.
  This manuscript has been developed from a set of lecture notes for a summer
course at the First International Computer Vision Summer School (ICVSS) in
Scicli, Italy, in July of 2008. They were later expanded and amended for
subsequent lectures in the same School in July 2009. Starting on November 1,
2009, they were further expanded for a special topics course, CS269, taught at
UCLA in the Spring term of 2010.
A Gap-Based Framework for Chinese Word Segmentation via Very Deep Convolutional Networks
Authors: Zhiqing Sun, Gehui Shen, Zhi-Hong Deng
Date: 2017-12-27 00:00:00
Tasks: Chinese Word Segmentation, Sentence

Most previous approaches to Chinese word segmentation can be roughly
classified into character-based and word-based methods. The former regards this
task as a sequence-labeling problem, while the latter directly segments
character sequence into words. However, if we consider segmenting a given
sentence, the most intuitive idea is to predict whether to segment for each gap
between two consecutive characters, which in comparison makes previous
approaches seem too complex. Therefore, in this paper, we propose a gap-based
framework to implement this intuitive idea. Moreover, very deep convolutional
neural networks, namely, ResNets and DenseNets, are exploited in our
experiments. Results show that our approach outperforms the best
character-based and word-based methods on 5 benchmarks, without any further
post-processing module (e.g. Conditional Random Fields) nor beam search.
Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation
Authors: Fahim Irfan Alam, Jun Zhou, Alan Wee-Chung Liew, Xiuping Jia, Jocelyn Chanussot, Yongsheng Gao
Date: 2017-11-13 00:00:00
Tasks: Hyperspectral Image Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

Image segmentation is considered to be one of the critical tasks in
hyperspectral remote sensing image processing. Recently, convolutional neural
network (CNN) has established itself as a powerful model in segmentation and
classification by demonstrating excellent performances. The use of a graphical
model such as a conditional random field (CRF) contributes further in capturing
contextual information and thus improving the segmentation performance. In this
paper, we propose a method to segment hyperspectral images by considering both
spectral and spatial information via a combined framework consisting of CNN and
CRF. We use multiple spectral cubes to learn deep features using CNN, and then
formulate deep CRF with CNN-based unary and pairwise potential functions to
effectively extract the semantic correlations between patches consisting of
three-dimensional data cubes. Effective piecewise training is applied in order
to avoid the computationally expensive iterative CRF inference. Furthermore, we
introduce a deep deconvolution network that improves the segmentation masks. We
also introduce a new dataset and experimented our proposed method on it along
with several widely adopted benchmark datasets to evaluate the effectiveness of
our method. By comparing our results with those from several state-of-the-art
models, we show the promising potential of our method.
Matrix Completion with Noisy Entries and Outliers
Authors: Raymond K. W. Wong, Thomas C. M. Lee
Date: 2015-03-01 00:00:00
Tasks: Image Inpainting, Matrix Completion

This paper considers the problem of matrix completion when the observed
entries are noisy and contain outliers. It begins with introducing a new
optimization criterion for which the recovered matrix is defined as its
solution. This criterion uses the celebrated Huber function from the robust
statistics literature to downweigh the effects of outliers. A practical
algorithm is developed to solve the optimization involved. This algorithm is
fast, straightforward to implement, and monotonic convergent. Furthermore, the
proposed methodology is theoretically shown to be stable in a well defined
sense. Its promising empirical performance is demonstrated via a sequence of
simulation experiments, including image inpainting.