pwc-archive/papers-with-abstracts huggingface dataset:
Grid Loss: Detecting Occluded Faces
Authors: Michael Opitz, Georg Waltner, Georg Poier, Horst Possegger, Horst Bischof
Date: 2016-09-01 00:00:00
Tasks: Face Detection

Detection of partially occluded objects is a challenging computer vision
problem. Standard Convolutional Neural Network (CNN) detectors fail if parts of
the detection window are occluded, since not every sub-part of the window is
discriminative on its own. To address this issue, we propose a novel loss layer
for CNNs, named grid loss, which minimizes the error rate on sub-blocks of a
convolution layer independently rather than over the whole feature map. This
results in parts being more discriminative on their own, enabling the detector
to recover if the detection window is partially occluded. By mapping our loss
layer back to a regular fully connected layer, no additional computational cost
is incurred at runtime compared to standard CNNs. We demonstrate our method for
face detection on several public face detection benchmarks and show that our
method outperforms regular CNNs, is suitable for realtime applications and
achieves state-of-the-art performance.
Neural Coarse-Graining: Extracting slowly-varying latent degrees of freedom with neural networks
Authors: Nicholas Guttenberg, Martin Biehl, Ryota Kanai
Date: 2016-09-01 00:00:00
Tasks: Time Series, Time Series Analysis

We present a loss function for neural networks that encompasses an idea of
trivial versus non-trivial predictions, such that the network jointly
determines its own prediction goals and learns to satisfy them. This permits
the network to choose sub-sets of a problem which are most amenable to its
abilities to focus on solving, while discarding 'distracting' elements that
interfere with its learning. To do this, the network first transforms the raw
data into a higher-level categorical representation, and then trains a
predictor from that new time series to its future. To prevent a trivial
solution of mapping the signal to zero, we introduce a measure of
non-triviality via a contrast between the prediction error of the learned model
with a naive model of the overall signal statistics. The transform can learn to
discard uninformative and unpredictable components of the signal in favor of
the features which are both highly predictive and highly predictable. This
creates a coarse-grained model of the time-series dynamics, focusing on
predicting the slowly varying latent parameters which control the statistics of
the time-series, rather than predicting the fast details directly. The result
is a semi-supervised algorithm which is capable of extracting latent
parameters, segmenting sections of time-series with differing statistics, and
building a higher-level representation of the underlying dynamics from
unlabeled data.
A Multilevel Coordinate Search Algorithm for Well Placement, Control and Joint Optimization
Authors: Xiang Wang, Ronald D. Haynes, Qihong Feng
Date: 2015-10-13 00:00:00

Determining optimal well placements and controls are two important tasks in
oil field development. These problems are computationally expensive, nonconvex,
and contain multiple optima. The practical solution of these problems require
efficient and robust algorithms. In this paper, the multilevel coordinate
search (MCS) algorithm is applied for well placement and control optimization
problems. MCS is a derivative-free algorithm that combines global and local
search. Both synthetic and real oil fields are considered. The performance of
MCS is compared to generalized pattern search (GPS), particle swarm
optimization (PSO), and covariance matrix adaptive evolution strategy (CMA-ES)
algorithms. Results show that the MCS algorithm is strongly competitive, and
outperforms for the joint optimization problem and with a limited computational
budget. The effect of parameter settings for MCS are compared for the test
examples. For the joint optimization problem we compare the performance of the
simultaneous and sequential procedures and show the utility of the latter.
Image segmentation based on histogram of depth and an application in driver distraction detection
Authors: Tran Hiep Dinh, Minh Trien Pham, Manh Duong Phung, Duc Manh Nguyen, Van Manh Hoang, Quang Vinh Tran
Date: 2016-09-01 00:00:00
Tasks: Image Segmentation, Position, Semantic Segmentation

This study proposes an approach to segment human object from a depth image
based on histogram of depth values. The region of interest is first extracted
based on a predefined threshold for histogram regions. A region growing process
is then employed to separate multiple human bodies with the same depth
interval. Our contribution is the identification of an adaptive growth
threshold based on the detected histogram region. To demonstrate the
effectiveness of the proposed method, an application in driver distraction
detection was introduced. After successfully extracting the driver's position
inside the car, we came up with a simple solution to track the driver motion.
With the analysis of the difference between initial and current frame, a change
of cluster position or depth value in the interested region, which cross the
preset threshold, is considered as a distracted activity. The experiment
results demonstrated the success of the algorithm in detecting typical
distracted driving activities such as using phone for calling or texting,
adjusting internal devices and drinking in real time.
A novel online multi-label classifier for high-speed streaming data applications
Authors: Rajasekar Venkatesan, Meng Joo Er, Mihika Dave, Mahardhika Pratama, Shiqian Wu
Date: 2016-09-01 00:00:00
Tasks: Classification, General Classification, Multi-class Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

In this paper, a high-speed online neural network classifier based on extreme
learning machines for multi-label classification is proposed. In multi-label
classification, each of the input data sample belongs to one or more than one
of the target labels. The traditional binary and multi-class classification
where each sample belongs to only one target class forms the subset of
multi-label classification. Multi-label classification problems are far more
complex than binary and multi-class classification problems, as both the number
of target labels and each of the target labels corresponding to each of the
input samples are to be identified. The proposed work exploits the high-speed
nature of the extreme learning machines to achieve real-time multi-label
classification of streaming data. A new threshold-based online sequential
learning algorithm is proposed for high speed and streaming data classification
of multi-label problems. The proposed method is experimented with six different
datasets from different application domains such as multimedia, text, and
biology. The hamming loss, accuracy, training time and testing time of the
proposed technique is compared with nine different state-of-the-art methods.
Experimental studies shows that the proposed technique outperforms the existing
multi-label classifiers in terms of performance and speed.
All Fingers are not Equal: Intensity of References in Scientific Articles
Authors: Tanmoy Chakraborty, Ramasuri Narayanam
Date: 2016-09-01 00:00:00
Proceeding: EMNLP 2016 11
Tasks: All, Articles

Research accomplishment is usually measured by considering all citations with
equal importance, thus ignoring the wide variety of purposes an article is
being cited for. Here, we posit that measuring the intensity of a reference is
crucial not only to perceive better understanding of research endeavor, but
also to improve the quality of citation-based applications. To this end, we
collect a rich annotated dataset with references labeled by the intensity, and
propose a novel graph-based semi-supervised model, GraLap to label the
intensity of references. Experiments with AAN datasets show a significant
improvement compared to the baselines to achieve the true labels of the
references (46% better correlation). Finally, we provide four applications to
demonstrate how the knowledge of reference intensity leads to design better
real-world applications.
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
Authors: Xiao-Jiao Mao, Chunhua Shen, Yu-Bin Yang
Date: 2016-03-30 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Decoder, Denoising, Image Restoration, Image Super-Resolution, Super-Resolution

In this paper, we propose a very deep fully convolutional encoding-decoding
framework for image restoration such as denoising and super-resolution. The
network is composed of multiple layers of convolution and de-convolution
operators, learning end-to-end mappings from corrupted images to the original
ones. The convolutional layers act as the feature extractor, which capture the
abstraction of image contents while eliminating noises/corruptions.
De-convolutional layers are then used to recover the image details. We propose
to symmetrically link convolutional and de-convolutional layers with skip-layer
connections, with which the training converges much faster and attains a
higher-quality local optimum. First, The skip connections allow the signal to
be back-propagated to bottom layers directly, and thus tackles the problem of
gradient vanishing, making training deep networks easier and achieving
restoration performance gains consequently. Second, these skip connections pass
image details from convolutional layers to de-convolutional layers, which is
beneficial in recovering the original image. Significantly, with the large
capacity, we can handle different levels of noises using a single model.
Experimental results show that our network achieves better performance than all
previously reported state-of-the-art methods.
Statistical Properties of the Single Linkage Hierarchical Clustering Estimator
Authors: Dekang Zhu, Dan P. Guralnik, Xuezhi Wang, Xiang Li, Bill Moran
Date: 2015-11-24 00:00:00
Tasks: Clustering

Distance-based hierarchical clustering (HC) methods are widely used in
unsupervised data analysis but few authors take account of uncertainty in the
distance data. We incorporate a statistical model of the uncertainty through
corruption or noise in the pairwise distances and investigate the problem of
estimating the HC as unknown parameters from measurements. Specifically, we
focus on single linkage hierarchical clustering (SLHC) and study its geometry.
We prove that under fairly reasonable conditions on the probability
distribution governing measurements, SLHC is equivalent to maximum partial
profile likelihood estimation (MPPLE) with some of the information contained in
the data ignored. At the same time, we show that direct evaluation of SLHC on
maximum likelihood estimation (MLE) of pairwise distances yields a consistent
estimator. Consequently, a full MLE is expected to perform better than SLHC in
getting the correct HC results for the ground truth metric.
Attentional Push: Augmenting Salience with Shared Attention Modeling
Authors: Siavash Gorji, James J. Clark
Date: 2016-09-01 00:00:00

We present a novel visual attention tracking technique based on Shared
Attention modeling. Our proposed method models the viewer as a participant in
the activity occurring in the scene. We go beyond image salience and instead of
only computing the power of an image region to pull attention to it, we also
consider the strength with which other regions of the image push attention to
the region in question. We use the term Attentional Push to refer to the power
of image regions to direct and manipulate the attention allocation of the
viewer. An attention model is presented that incorporates the Attentional Push
cues with standard image salience-based attention modeling algorithms to
improve the ability to predict where viewers will fixate. Experimental
evaluation validates significant improvements in predicting viewers' fixations
using the proposed methodology in both static and dynamic imagery.
How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions
Authors: Arun Tejasvi Chaganty, Percy Liang
Date: 2016-09-01 00:00:00
Proceeding: ACL 2016 8
Tasks: Articles

How much is 131 million US dollars? To help readers put such numbers in
context, we propose a new task of automatically generating short descriptions
known as perspectives, e.g. "$131 million is about the cost to employ everyone
in Texas over a lunch period". First, we collect a dataset of numeric mentions
in news articles, where each mention is labeled with a set of rated
perspectives. We then propose a system to generate these descriptions
consisting of two steps: formula construction and description generation. In
construction, we compose formulae from numeric facts in a knowledge base and
rank the resulting formulas based on familiarity, numeric proximity and
semantic compatibility. In generation, we convert a formula into natural
language using a sequence-to-sequence recurrent neural network. Our system
obtains a 15.2% F1 improvement over a non-compositional baseline at formula
construction and a 12.5 BLEU point improvement over a baseline description
generation.
Low-rank Multi-view Clustering in Third-Order Tensor Space
Authors: Ming Yin, Junbin Gao, Shengli Xie, Yi Guo
Date: 2016-08-30 00:00:00
Tasks: Clustering, Multi-view Subspace Clustering

The plenty information from multiple views data as well as the complementary
information among different views are usually beneficial to various tasks,
e.g., clustering, classification, de-noising. Multi-view subspace clustering is
based on the fact that the multi-view data are generated from a latent
subspace. To recover the underlying subspace structure, the success of the
sparse and/or low-rank subspace clustering has been witnessed recently. Despite
some state-of-the-art subspace clustering approaches can numerically handle
multi-view data, by simultaneously exploring all possible pairwise correlation
within views, the high order statistics is often disregarded which can only be
captured by simultaneously utilizing all views. As a consequence, the
clustering performance for multi-view data is compromised. To address this
issue, in this paper, a novel multi-view clustering method is proposed by using
\textit{t-product} in third-order tensor space. Based on the circular
convolution operation, multi-view data can be effectively represented by a
\textit{t-linear} combination with sparse and low-rank penalty using
"self-expressiveness". Our extensive experimental results on facial, object,
digits image and text data demonstrate that the proposed method outperforms the
state-of-the-art methods in terms of many criteria.
Importance weighting without importance weights: An efficient algorithm for combinatorial semi-bandits
Authors: Gergely Neu, Gábor Bartók
Date: 2015-03-17 00:00:00
Tasks: Combinatorial Optimization

We propose a sample-efficient alternative for importance weighting for
situations where one only has sample access to the probability distribution
that generates the observations. Our new method, called Geometric Resampling
(GR), is described and analyzed in the context of online combinatorial
optimization under semi-bandit feedback, where a learner sequentially selects
its actions from a combinatorial decision set so as to minimize its cumulative
loss. In particular, we show that the well-known Follow-the-Perturbed-Leader
(FPL) prediction method coupled with Geometric Resampling yields the first
computationally efficient reduction from offline to online optimization in this
setting. We provide a thorough theoretical analysis for the resulting
algorithm, showing that its performance is on par with previous, inefficient
solutions. Our main contribution is showing that, despite the relatively large
variance induced by the GR procedure, our performance guarantees hold with high
probability rather than only in expectation. As a side result, we also improve
the best known regret bounds for FPL in online combinatorial optimization with
full feedback, closing the perceived performance gap between FPL and
exponential weights in this setting.
PDDL+ Planning via Constraint Answer Set Programming
Authors: Marcello Balduccini, Daniele Magazzeni, Marco Maratea
Date: 2016-08-31 00:00:00

PDDL+ is an extension of PDDL that enables modelling planning domains with
mixed discrete-continuous dynamics. In this paper we present a new approach to
PDDL+ planning based on Constraint Answer Set Programming (CASP), i.e. ASP
rules plus numerical constraints. To the best of our knowledge, ours is the
first attempt to link PDDL+ planning and logic programming. We provide an
encoding of PDDL+ models into CASP problems. The encoding can handle non-linear
hybrid domains, and represents a solid basis for applying logic programming to
PDDL+ planning. As a case study, we consider the EZCSP CASP solver and obtain
promising results on a set of PDDL+ benchmark problems.
Leveraging Visual Question Answering for Image-Caption Ranking
Authors: Xiao Lin, Devi Parikh
Date: 2016-05-04 00:00:00
Tasks: Image Retrieval, Question Answering, Retrieval, Visual Question Answering, Visual Question Answering (VQA)

Visual Question Answering (VQA) is the task of taking as input an image and a
free-form natural language question about the image, and producing an accurate
answer. In this work we view VQA as a "feature extraction" module to extract
image and caption representations. We employ these representations for the task
of image-caption ranking. Each feature dimension captures (imagines) whether a
fact (question-answer pair) could plausibly be true for the image and caption.
This allows the model to interpret images and captions from a wide variety of
perspectives. We propose score-level and representation-level fusion models to
incorporate VQA knowledge in an existing state-of-the-art VQA-agnostic
image-caption ranking model. We find that incorporating and reasoning about
consistency between images and captions significantly improves performance.
Concretely, our model improves state-of-the-art on caption retrieval by 7.1%
and on image retrieval by 4.4% on the MSCOCO dataset.
Radiation Search Operations using Scene Understanding with Autonomous UAV and UGV
Authors: Gordon Christie, Adam Shoemaker, Kevin Kochersberger, Pratap Tokekar, Lance McLean, Alexander Leonessa
Date: 2016-08-31 00:00:00
Tasks: Scene Segmentation, Scene Understanding, Segmentation, Semantic Segmentation

Autonomously searching for hazardous radiation sources requires the ability
of the aerial and ground systems to understand the scene they are scouting. In
this paper, we present systems, algorithms, and experiments to perform
radiation search using unmanned aerial vehicles (UAV) and unmanned ground
vehicles (UGV) by employing semantic scene segmentation. The aerial data is
used to identify radiological points of interest, generate an orthophoto along
with a digital elevation model (DEM) of the scene, and perform semantic
segmentation to assign a category (e.g. road, grass) to each pixel in the
orthophoto. We perform semantic segmentation by training a model on a dataset
of images we collected and annotated, using the model to perform inference on
images of the test area unseen to the model, and then refining the results with
the DEM to better reason about category predictions at each pixel. We then use
all of these outputs to plan a path for a UGV carrying a LiDAR to map the
environment and avoid obstacles not present during the flight, and a radiation
detector to collect more precise radiation measurements from the ground.
Results of the analysis for each scenario tested favorably. We also note that
our approach is general and has the potential to work for a variety of
different sensing tasks.
A Tutorial on Online Supervised Learning with Applications to Node Classification in Social Networks
Authors: Alexander Rakhlin, Karthik Sridharan
Date: 2016-08-31 00:00:00
Tasks: General Classification, Node Classification

We revisit the elegant observation of T. Cover '65 which, perhaps, is not as
well-known to the broader community as it should be. The first goal of the
tutorial is to explain---through the prism of this elementary result---how to
solve certain sequence prediction problems by modeling sets of solutions rather
than the unknown data-generating mechanism. We extend Cover's observation in
several directions and focus on computational aspects of the proposed
algorithms. The applicability of the methods is illustrated on several
examples, including node classification in a network.
  The second aim of this tutorial is to demonstrate the following phenomenon:
it is possible to predict as well as a combinatorial "benchmark" for which we
have a certain multiplicative approximation algorithm, even if the exact
computation of the benchmark given all the data is NP-hard. The proposed
prediction methods, therefore, circumvent some of the computational
difficulties associated with finding the best model given the data. These
difficulties arise rather quickly when one attempts to develop a probabilistic
model for graph-based or other problems with a combinatorial structure.
Measuring the Quality of Exercises
Authors: Paritosh Parmar, Brendan Tran Morris
Date: 2016-08-31 00:00:00
Tasks: BIG-bench Machine Learning, Dynamic Time Warping, General Classification, Management

This work explores the problem of exercise quality measurement since it is
essential for effective management of diseases like cerebral palsy (CP). This
work examines the assessment of quality of large amplitude movement (LAM)
exercises designed to treat CP in an automated fashion. Exercise data was
collected by trained participants to generate ideal examples to use as a
positive samples for machine learning. Following that, subjects were asked to
deliberately make subtle errors during the exercise, such as restricting
movements, as is commonly seen in cases of patients suffering from CP. The
quality measurement problem was then posed as a classification to determine
whether an example exercise was either "good" or "bad". Popular machine
learning techniques for classification, including support vector machines
(SVM), single and doublelayered neural networks (NN), boosted decision trees,
and dynamic time warping (DTW), were compared. The AdaBoosted tree performed
best with an accuracy of 94.68% demonstrating the feasibility of assessing
exercise quality.