pwc-archive/papers-with-abstracts huggingface dataset:
Global Continuous Optimization with Error Bound and Fast Convergence
Authors: Kenji Kawaguchi, Yu Maruyama, Xiaoyu Zheng
Date: 2016-07-17 00:00:00
Tasks: global-optimization, Management

This paper considers global optimization with a black-box unknown objective
function that can be non-convex and non-differentiable. Such a difficult
optimization problem arises in many real-world applications, such as parameter
tuning in machine learning, engineering design problem, and planning with a
complex physics simulator. This paper proposes a new global optimization
algorithm, called Locally Oriented Global Optimization (LOGO), to aim for both
fast convergence in practice and finite-time error bound in theory. The
advantage and usage of the new algorithm are illustrated via theoretical
analysis and an experiment conducted with 11 benchmark test functions. Further,
we modify the LOGO algorithm to specifically solve a planning problem via
policy search with continuous state/action space and long time horizon while
maintaining its finite-time error bound. We apply the proposed planning method
to accident management of a nuclear power plant. The result of the application
study demonstrates the practical utility of our method.
Knowledge Representation on the Web revisited: Tools for Prototype Based Ontologies
Authors: Michael Cochez, Stefan Decker, Eric Prud'hommeaux
Date: 2016-07-16 00:00:00

In recent years RDF and OWL have become the most common knowledge
representation languages in use on the Web, propelled by the recommendation of
the W3C. In this paper we present a practical implementation of a different
kind of knowledge representation based on Prototypes. In detail, we present a
concrete syntax easily and effectively parsable by applications. We also
present extensible implementations of a prototype knowledge base, specifically
designed for storage of Prototypes. These implementations are written in Java
and can be extended by using the implementation as a library. Alternatively,
the software can be deployed as such. Further, results of benchmarks for both
local and web deployment are presented. This paper augments a research paper,
in which we describe the more theoretical aspects of our Prototype system.
Inferring solutions of differential equations using noisy multi-fidelity data
Authors: Maziar Raissi, Paris Perdikaris, George Em. Karniadakis
Date: 2016-07-16 00:00:00
Tasks: Active Learning

For more than two centuries, solutions of differential equations have been
obtained either analytically or numerically based on typically well-behaved
forcing and boundary conditions for well-posed problems. We are changing this
paradigm in a fundamental way by establishing an interface between
probabilistic machine learning and differential equations. We develop
data-driven algorithms for general linear equations using Gaussian process
priors tailored to the corresponding integro-differential operators. The only
observables are scarce noisy multi-fidelity data for the forcing and solution
that are not required to reside on the domain boundary. The resulting
predictive posterior distributions quantify uncertainty and naturally lead to
adaptive solution refinement via active learning. This general framework
circumvents the tyranny of numerical discretization as well as the consistency
and stability issues of time-integration, and is scalable to high-dimensions.
Exploiting Multi-modal Curriculum in Noisy Web Data for Large-scale Concept Learning
Authors: Junwei Liang, Lu Jiang, Deyu Meng, Alexander Hauptmann
Date: 2016-07-16 00:00:00
Tasks: BIG-bench Machine Learning

Learning video concept detectors automatically from the big but noisy web
data with no additional manual annotations is a novel but challenging area in
the multimedia and the machine learning community. A considerable amount of
videos on the web are associated with rich but noisy contextual information,
such as the title, which provides weak annotations or labels about the video
content. To leverage the big noisy web labels, this paper proposes a novel
method called WEbly-Labeled Learning (WELL), which is established on the
state-of-the-art machine learning algorithm inspired by the learning process of
human. WELL introduces a number of novel multi-modal approaches to incorporate
meaningful prior knowledge called curriculum from the noisy web videos. To
investigate this problem, we empirically study the curriculum constructed from
the multi-modal features of the videos collected from YouTube and Flickr. The
efficacy and the scalability of WELL have been extensively demonstrated on two
public benchmarks, including the largest multimedia dataset and the largest
manually-labeled video set. The comprehensive experimental results demonstrate
that WELL outperforms state-of-the-art studies by a statically significant
margin on learning concepts from noisy web video data. In addition, the results
also verify that WELL is robust to the level of noisiness in the video data.
Notably, WELL trained on sufficient noisy web labels is able to achieve a
comparable accuracy to supervised learning methods trained on the clean
manually-labeled data.
Construction of extended 3D field of views of the internal bladder wall surface: a proof of concept
Authors: Achraf Ben-Hamadou, Christian Daul, Charles Soussen
Date: 2016-07-16 00:00:00
Tasks: Image Registration

3D extended field of views (FOVs) of the internal bladder wall facilitate
lesion diagnosis, patient follow-up and treatment traceability. In this paper,
we propose a 3D image mosaicing algorithm guided by 2D cystoscopic video-image
registration for obtaining textured FOV mosaics. In this feasibility study, the
registration makes use of data from a 3D cystoscope prototype providing, in
addition to each small FOV image, some 3D points located on the surface. This
proof of concept shows that textured surfaces can be constructed with minimally
modified cystoscopes. The potential of the method is demonstrated on numerical
and real phantoms reproducing various surface shapes. Pig and human bladder
textures are superimposed on phantoms with known shape and dimensions. These
data allow for quantitative assessment of the 3D mosaicing algorithm based on
the registration of images simulating bladder textures.
Shesop Healthcare: Stress and influenza classification using support vector machine kernel
Authors: Andrien Ivander Wijaya, Ary Setijadi Prihatmanto, Rifki Wijaya
Date: 2016-07-16 00:00:00
Tasks: General Classification

Shesop is an integrated system to make human lives more easily and to help
people in terms of healthcare. Stress and influenza classification is a part of
Shesop's application for a healthcare devices such as smartwatch, polar and
fitbit. The main objective of this paper is to classify a new data and inform
whether you are stress, depressed, caught by influenza or not. We will use the
heart rate data taken for months in Bandung, analyze the data and find the
Heart rate variance that constantly related with the stress and flu level.
After we found the variable, we will use the variable as an input to the
support vector machine learning. We will use the lagrangian and kernel
technique to transform 2D data into 3D data so we can use the linear
classification in 3D space. In the end, we could use the machine learning's
result to classify new data and get the final result immediately: stress or
not, influenza or not.
Online Algorithms for Factorization-Based Structure from Motion
Authors: Ryan Kennedy, Laura Balzano, Stephen J. Wright, Camillo J. Taylor
Date: 2013-09-26 00:00:00
Tasks: Matrix Completion

We present a family of online algorithms for real-time factorization-based
structure from motion, leveraging a relationship between incremental singular
value decomposition and recently proposed methods for online matrix completion.
Our methods are orders of magnitude faster than previous state of the art, can
handle missing data and a variable number of feature points, and are robust to
noise and sparse outliers. We demonstrate our methods on both real and
synthetic sequences and show that they perform well in both online and batch
settings. We also provide an implementation which is able to produce 3D models
in real time using a laptop with a webcam.
Design and implementation of image processing system for Lumen social robot-humanoid as an exhibition guide for Electrical Engineering Days 2015
Authors: Setyaki Sholata Sya, Ary Setijadi Prihatmanto
Date: 2016-07-16 00:00:00
Tasks: Electrical Engineering, Face Detection, Face Recognition, Human Detection

Lumen Social Robot is a humanoid robot development with the purpose that it
could be a good friend to all people. In this year, the Lumen Social Robot is
being developed into a guide in the exhibition and in the seminar of the Final
Exam of undergraduate and graduate students in Electrical Engineering ITB,
named Electrical Engineering Days 2015. In order to be the guide in that
occasion, Lumen is supported by several things. They are Nao robot components,
servers, and multiple processor systems. The image processing system is a
processing application system that allows Lumen to recognize and determine an
object from the image taken from the camera eye. The image processing system is
provided with four modules. They are face detection module to detect a person's
face, face recognition module to recognize a person's face, face tracking
module to follow a person's face, and human detection module to detect humans
based on the upper parts of person's body. Face detection module and human
detection module are implemented by using the library harcascade.xml on EMGU
CV. Face recognition module is implemented by adding the database for the face
that has been detected and store it in that database. Face tracking module is
implemented by using the Smooth Gaussian filter to the image.
  -----
  Lumen Sosial Robot merupakan sebuah pengembangan robot humanoid agar dapat
menjadi teman bagi banyak orang. Sistem pengolahan citra merupakan sistem
aplikasi pengolah yang bertujuan Lumen dapat mengenali dan mengetahui suatu
objek pada citra yang diambil dari camera mata Lumen. System pengolahan citra
dilengkapi dengan empat buah modul, yaitu modul face detection untuk mendeteksi
wajah seseorang, modul face recognition untuk mengenali wajah orang tersebut,
modul face tracking untuk mengikuti wajah seseorang, dan modul human detection
untuk mendeteksi manusia berdasarkan bagian tubuh atas orang
New version of Gram-Schmidt Process with inverse for Signal and Image Processing
Authors: Mario Mastriani
Date: 2016-07-16 00:00:00

The Gram-Schmidt Process (GSP) is used to convert a non-orthogonal basis (a
set of linearly independent vectors, matrices, etc) into an orthonormal basis
(a set of orthogonal, unit-length vectors, bi or tri dimensional matrices). The
process consists of taking each array and then subtracting the projections in
common with the previous arrays. This paper introduces an enhanced version of
the Gram-Schmidt Process (EGSP) with inverse, which is useful for Digital
Signal and Image Processing, among others applications.
Weakly supervised object detection using pseudo-strong labels
Authors: Ke Yang, Dongsheng Li, Yong Dou, Shaohe Lv, Qiang Wang
Date: 2016-07-16 00:00:00
Tasks: Object, object-detection, Object Detection, Weakly Supervised Object Detection

Object detection is an import task of computer vision.A variety of methods
have been proposed,but methods using the weak labels still do not have a
satisfactory result.In this paper,we propose a new framework that using the
weakly supervised method's output as the pseudo-strong labels to train a
strongly supervised model.One weakly supervised method is treated as black-box
to generate class-specific bounding boxes on train dataset.A de-noise method is
then applied to the noisy bounding boxes.Then the de-noised pseudo-strong
labels are used to train a strongly object detection network.The whole
framework is still weakly supervised because the entire process only uses the
image-level labels.The experiment results on PASCAL VOC 2007 prove the validity
of our framework, and we get result 43.4% on mean average precision compared to
39.5% of the previous best result and 34.5% of the initial
method,respectively.And this frame work is simple and distinct,and is promising
to be applied to other method easily.
TabletGaze: Unconstrained Appearance-based Gaze Estimation in Mobile Tablets
Authors: Qiong Huang, Ashok Veeraraghavan, Ashutosh Sabharwal
Date: 2015-08-05 00:00:00
Tasks: Gaze Estimation

We study gaze estimation on tablets, our key design goal is uncalibrated gaze
estimation using the front-facing camera during natural use of tablets, where
the posture and method of holding the tablet is not constrained. We collected
the first large unconstrained gaze dataset of tablet users, labeled Rice
TabletGaze dataset. The dataset consists of 51 subjects, each with 4 different
postures and 35 gaze locations. Subjects vary in race, gender and in their need
for prescription glasses, all of which might impact gaze estimation accuracy.
Driven by our observations on the collected data, we present a TabletGaze
algorithm for automatic gaze estimation using multi-level HoG feature and
Random Forests regressor. The TabletGaze algorithm achieves a mean error of
3.17 cm. We perform extensive evaluation on the impact of various factors such
as dataset size, race, wearing glasses and user posture on the gaze estimation
accuracy and make important observations about the impact of these factors.
A Comprehensive Survey on Pose-Invariant Face Recognition
Authors: Changxing Ding, DaCheng Tao
Date: 2015-02-15 00:00:00
Tasks: Face Generation, Face Recognition, Robust Face Recognition, Survey

The capacity to recognize faces under varied poses is a fundamental human
ability that presents a unique challenge for computer vision systems. Compared
to frontal face recognition, which has been intensively studied and has
gradually matured in the past few decades, pose-invariant face recognition
(PIFR) remains a largely unsolved problem. However, PIFR is crucial to
realizing the full potential of face recognition for real-world applications,
since face recognition is intrinsically a passive biometric technology for
recognizing uncooperative subjects. In this paper, we discuss the inherent
difficulties in PIFR and present a comprehensive review of established
techniques. Existing PIFR methods can be grouped into four categories, i.e.,
pose-robust feature extraction approaches, multi-view subspace learning
approaches, face synthesis approaches, and hybrid approaches. The motivations,
strategies, pros/cons, and performance of representative approaches are
described and compared. Moreover, promising directions for future research are
discussed.
A New Learning Method for Inference Accuracy, Core Occupation, and Performance Co-optimization on TrueNorth Chip
Authors: Wei Wen, Chunpeng Wu, Yandan Wang, Kent Nixon, Qing Wu, Mark Barnell, Hai Li, Yiran Chen
Date: 2016-04-03 00:00:00
Tasks: General Classification, Quantization

IBM TrueNorth chip uses digital spikes to perform neuromorphic computing and
achieves ultrahigh execution parallelism and power efficiency. However, in
TrueNorth chip, low quantization resolution of the synaptic weights and spikes
significantly limits the inference (e.g., classification) accuracy of the
deployed neural network model. Existing workaround, i.e., averaging the results
over multiple copies instantiated in spatial and temporal domains, rapidly
exhausts the hardware resources and slows down the computation. In this work,
we propose a novel learning method on TrueNorth platform that constrains the
random variance of each computation copy and reduces the number of needed
copies. Compared to the existing learning method, our method can achieve up to
68.8% reduction of the required neuro-synaptic cores or 6.5X speedup, with even
slightly improved inference accuracy.
Dynamic Sum Product Networks for Tractable Inference on Sequence Data (Extended Version)
Authors: Mazen Melibari, Pascal Poupart, Prashant Doshi, George Trimponias
Date: 2015-11-13 00:00:00

Sum-Product Networks (SPN) have recently emerged as a new class of tractable
probabilistic graphical models. Unlike Bayesian networks and Markov networks
where inference may be exponential in the size of the network, inference in
SPNs is in time linear in the size of the network. Since SPNs represent
distributions over a fixed set of variables only, we propose dynamic sum
product networks (DSPNs) as a generalization of SPNs for sequence data of
varying length. A DSPN consists of a template network that is repeated as many
times as needed to model data sequences of any length. We present a local
search technique to learn the structure of the template network. In contrast to
dynamic Bayesian networks for which inference is generally exponential in the
number of variables per time slice, DSPNs inherit the linear inference
complexity of SPNs. We demonstrate the advantages of DSPNs over DBNs and other
models on several datasets of sequence data.
A Class of DCT Approximations Based on the Feig-Winograd Algorithm
Authors: C. J. Tablada, F. M. Bayer, R. J. Cintra
Date: 2015-02-02 00:00:00
Tasks: Image Compression

A new class of matrices based on a parametrization of the Feig-Winograd
factorization of 8-point DCT is proposed. Such parametrization induces a matrix
subspace, which unifies a number of existing methods for DCT approximation. By
solving a comprehensive multicriteria optimization problem, we identified
several new DCT approximations. Obtained solutions were sought to possess the
following properties: (i) low multiplierless computational complexity, (ii)
orthogonality or near orthogonality, (iii) low complexity invertibility, and
(iv) close proximity and performance to the exact DCT. Proposed approximations
were submitted to assessment in terms of proximity to the DCT, coding
performance, and suitability for image compression. Considering Pareto
efficiency, particular new proposed approximations could outperform various
existing methods archived in literature.
On Deep Representation Learning from Noisy Web Images
Authors: Phong D. Vo, Alexandru Ginsca, Hervé Le Borgne, Adrian Popescu
Date: 2015-12-15 00:00:00
Tasks: Representation Learning, Reranking

The keep-growing content of Web images may be the next important data source
to scale up deep neural networks, which recently obtained a great success in
the ImageNet classification challenge and related tasks. This prospect,
however, has not been validated on convolutional networks (convnet) -- one of
best performing deep models -- because of their supervised regime. While
unsupervised alternatives are not so good as convnet in generalizing the
learned model to new domains, we use convnet to leverage semi-supervised
representation learning. Our approach is to use massive amounts of unlabeled
and noisy Web images to train convnets as general feature detectors despite
challenges coming from data such as high level of mislabeled data, outliers,
and data biases. Extensive experiments are conducted at several data scales,
different network architectures, and data reranking techniques. The learned
representations are evaluated on nine public datasets of various topics. The
best results obtained by our convnets, trained on 3.14 million Web images,
outperform AlexNet trained on 1.2 million clean images of ILSVRC 2012 and is
closing the gap with VGG-16. These prominent results suggest a budget solution
to use deep learning in practice and motivate more research in semi-supervised
representation learning.
Guided Policy Search as Approximate Mirror Descent
Authors: William Montgomery, Sergey Levine
Date: 2016-07-15 00:00:00
Tasks: Reinforcement Learning

Guided policy search algorithms can be used to optimize complex nonlinear
policies, such as deep neural networks, without directly computing policy
gradients in the high-dimensional parameter space. Instead, these methods use
supervised learning to train the policy to mimic a "teacher" algorithm, such as
a trajectory optimizer or a trajectory-centric reinforcement learning method.
Guided policy search methods provide asymptotic local convergence guarantees by
construction, but it is not clear how much the policy improves within a small,
finite number of iterations. We show that guided policy search algorithms can
be interpreted as an approximate variant of mirror descent, where the
projection onto the constraint manifold is not exact. We derive a new guided
policy search algorithm that is simpler and provides appealing improvement and
convergence guarantees in simplified convex and linear settings, and show that
in the more general nonlinear setting, the error in the projection step can be
bounded. We provide empirical results on several simulated robotic navigation
and manipulation tasks that show that our method is stable and achieves similar
or better performance when compared to prior guided policy search methods, with
a simpler formulation and fewer hyperparameters.
Optimally-Weighted Herding is Bayesian Quadrature
Authors: Ferenc Huszár, David Duvenaud
Date: 2012-04-07 00:00:00

Herding and kernel herding are deterministic methods of choosing samples
which summarise a probability distribution. A related task is choosing samples
for estimating integrals using Bayesian quadrature. We show that the criterion
minimised when selecting samples in kernel herding is equivalent to the
posterior variance in Bayesian quadrature. We then show that sequential
Bayesian quadrature can be viewed as a weighted version of kernel herding which
achieves performance superior to any other weighted herding method. We
demonstrate empirically a rate of convergence faster than O(1/N). Our results
also imply an upper bound on the empirical error of the Bayesian quadrature
estimate.
Person Re-identification with Hyperspectral Multi-Camera Systems --- A Pilot Study
Authors: Saurabh Prasad, Tanu Priya, Minshan Cui, Shishir Shah
Date: 2016-07-15 00:00:00
Tasks: Person Re-Identification

Person re-identification in a multi-camera environment is an important part
of modern surveillance systems. Person re-identification from color images has
been the focus of much active research, due to the numerous challenges posed
with such analysis tasks, such as variations in illumination, pose and
viewpoints. In this paper, we suggest that hyperspectral imagery has the
potential to provide unique information that is expected to be beneficial for
the re-identification task. Specifically, we assert that by accurately
characterizing the unique spectral signature for each person's skin,
hyperspectral imagery can provide very useful descriptors (e.g. spectral
signatures from skin pixels) for re-identification. Towards this end, we
acquired proof-of-concept hyperspectral re-identification data under
challenging (practical) conditions from 15 people. Our results indicate that
hyperspectral data result in a substantially enhanced re-identification
performance compared to color (RGB) images, when using spectral signatures over
skin as the feature descriptor.