pwc-archive/papers-with-abstracts huggingface dataset:
A Chaotic Dynamical System that Paints
Authors: Tuhin Sahai, George Mathew, Amit Surana
Date: 2015-04-08 00:00:00
Tasks: Uncertainty Quantification

Can a dynamical system paint masterpieces such as Da Vinci's Mona Lisa or
Monet's Water Lilies? Moreover, can this dynamical system be chaotic in the
sense that although the trajectories are sensitive to initial conditions, the
same painting is created every time? Setting aside the creative aspect of
painting a picture, in this work, we develop a novel algorithm to reproduce
paintings and photographs. Combining ideas from ergodic theory and control
theory, we construct a chaotic dynamical system with predetermined statistical
properties. If one makes the spatial distribution of colors in the picture the
target distribution, akin to a human, the algorithm first captures large scale
features and then goes on to refine small scale features. Beyond reproducing
paintings, this approach is expected to have a wide variety of applications
such as uncertainty quantification, sampling for efficient inference in
scalable machine learning for big data, and developing effective strategies for
search and rescue. In particular, our preliminary studies demonstrate that this
algorithm provides significant acceleration and higher accuracy than competing
methods for Markov Chain Monte Carlo (MCMC).
Zero-bias autoencoders and the benefits of co-adapting features
Authors: Kishore Konda, Roland Memisevic, David Krueger
Date: 2014-02-13 00:00:00

Regularized training of an autoencoder typically results in hidden unit
biases that take on large negative values. We show that negative biases are a
natural result of using a hidden layer whose responsibility is to both
represent the input data and act as a selection mechanism that ensures sparsity
of the representation. We then show that negative biases impede the learning of
data distributions whose intrinsic dimensionality is high. We also propose a
new activation function that decouples the two roles of the hidden layer and
that allows us to learn representations on data with very high intrinsic
dimensionality, where standard autoencoders typically fail. Since the decoupled
activation function acts like an implicit regularizer, the model can be trained
by minimizing the reconstruction error of training data, without requiring any
additional regularization.
Pixel-wise Deep Learning for Contour Detection
Authors: Jyh-Jing Hwang, Tyng-Luh Liu
Date: 2015-04-08 00:00:00
Tasks: Contour Detection, Deep Learning

We address the problem of contour detection via per-pixel classifications of
edge point. To facilitate the process, the proposed approach leverages with
DenseNet, an efficient implementation of multiscale convolutional neural
networks (CNNs), to extract an informative feature vector for each pixel and
uses an SVM classifier to accomplish contour detection. In the experiment of
contour detection, we look into the effectiveness of combining per-pixel
features from different CNN layers and verify their performance on BSDS500.
Protein Contact Prediction by Integrating Joint Evolutionary Coupling Analysis and Supervised Learning
Authors: Jianzhu Ma, Sheng Wang, Zhiyong Wang, Jinbo Xu
Date: 2013-12-10 00:00:00

Protein contacts contain important information for protein structure and
functional study, but contact prediction from sequence remains very
challenging. Both evolutionary coupling (EC) analysis and supervised machine
learning methods are developed to predict contacts, making use of different
types of information, respectively. This paper presents a group graphical lasso
(GGL) method for contact prediction that integrates joint multi-family EC
analysis and supervised learning. Different from existing single-family EC
analysis that uses residue co-evolution information in only the target protein
family, our joint EC analysis uses residue co-evolution in both the target
family and its related families, which may have divergent sequences but similar
folds. To implement joint EC analysis, we model a set of related protein
families using Gaussian graphical models (GGM) and then co-estimate their
precision matrices by maximum-likelihood, subject to the constraint that the
precision matrices shall share similar residue co-evolution patterns. To
further improve the accuracy of the estimated precision matrices, we employ a
supervised learning method to predict contact probability from a variety of
evolutionary and non-evolutionary information and then incorporate the
predicted probability as prior into our GGL framework. Experiments show that
our method can predict contacts much more accurately than existing methods, and
that our method performs better on both conserved and family-specific contacts.
Image Subset Selection Using Gabor Filters and Neural Networks
Authors: Heider K. Ali, Anthony Whitehead
Date: 2015-04-08 00:00:00
Tasks: General Classification

An automatic method for the selection of subsets of images, both modern and
historic, out of a set of landmark large images collected from the Internet is
presented in this paper. This selection depends on the extraction of dominant
features using Gabor filtering. Features are selected carefully from a
preliminary image set and fed into a neural network as a training data. The
method collects a large set of raw landmark images containing modern and
historic landmark images and non-landmark images. The method then processes
these images to classify them as landmark and non-landmark images. The
classification performance highly depends on the number of candidate features
of the landmark.
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
Authors: Laura Leal-Taixé, Anton Milan, Ian Reid, Stefan Roth, Konrad Schindler
Date: 2015-04-08 00:00:00
Tasks: 3D Reconstruction, Multiple Object Tracking, Object, Object Tracking, Optical Flow Estimation, Pedestrian Detection

In the recent past, the computer vision community has developed centralized
benchmarks for the performance evaluation of a variety of tasks, including
generic object and pedestrian detection, 3D reconstruction, optical flow,
single-object short-term tracking, and stereo estimation. Despite potential
pitfalls of such benchmarks, they have proved to be extremely helpful to
advance the state of the art in the respective area. Interestingly, there has
been rather limited work on the standardization of quantitative benchmarks for
multiple target tracking. One of the few exceptions is the well-known PETS
dataset, targeted primarily at surveillance applications. Despite being widely
used, it is often applied inconsistently, for example involving using different
subsets of the available data, different ways of training the models, or
differing evaluation scripts. This paper describes our work toward a novel
multiple object tracking benchmark aimed to address such issues. We discuss the
challenges of creating such a framework, collecting existing and new data,
gathering state-of-the-art methods to be tested on the datasets, and finally
creating a unified evaluation system. With MOTChallenge we aim to pave the way
toward a unified evaluation framework for a more meaningful quantification of
multi-target tracking.
Data Mining for Prediction of Human Performance Capability in the Software-Industry
Authors: Gaurav Singh Thakur, Anubhav Gupta, Sangita Gupta
Date: 2015-04-08 00:00:00
Tasks: Ensemble Learning

The recruitment of new personnel is one of the most essential business
processes which affect the quality of human capital within any company. It is
highly essential for the companies to ensure the recruitment of right talent to
maintain a competitive edge over the others in the market. However IT companies
often face a problem while recruiting new people for their ongoing projects due
to lack of a proper framework that defines a criteria for the selection
process. In this paper we aim to develop a framework that would allow any
project manager to take the right decision for selecting new talent by
correlating performance parameters with the other domain-specific attributes of
the candidates. Also, another important motivation behind this project is to
check the validity of the selection procedure often followed by various big
companies in both public and private sectors which focus only on academic
scores, GPA/grades of students from colleges and other academic backgrounds. We
test if such a decision will produce optimal results in the industry or is
there a need for change that offers a more holistic approach to recruitment of
new talent in the software companies. The scope of this work extends beyond the
IT domain and a similar procedure can be adopted to develop a recruitment
framework in other fields as well. Data-mining techniques provide useful
information from the historical projects depending on which the hiring-manager
can make decisions for recruiting high-quality workforce. This study aims to
bridge this hiatus by developing a data-mining framework based on an
ensemble-learning technique to refocus on the criteria for personnel selection.
The results from this research clearly demonstrated that there is a need to
refocus on the selection-criteria for quality objectives.
Evaluating Two-Stream CNN for Video Classification
Authors: Hao Ye, Zuxuan Wu, Rui-Wei Zhao, Xi Wang, Yu-Gang Jiang, xiangyang xue
Date: 2015-04-08 00:00:00
Tasks: Classification, General Classification, Video Classification, Vocal Bursts Valence Prediction

Videos contain very rich semantic information. Traditional hand-crafted
features are known to be inadequate in analyzing complex video semantics.
Inspired by the huge success of the deep learning methods in analyzing image,
audio and text data, significant efforts are recently being devoted to the
design of deep nets for video analytics. Among the many practical needs,
classifying videos (or video clips) based on their major semantic categories
(e.g., "skiing") is useful in many applications. In this paper, we conduct an
in-depth study to investigate important implementation options that may affect
the performance of deep nets on video classification. Our evaluations are
conducted on top of a recent two-stream convolutional neural network (CNN)
pipeline, which uses both static frames and motion optical flows, and has
demonstrated competitive performance against the state-of-the-art methods. In
order to gain insights and to arrive at a practical guideline, many important
options are studied, including network architectures, model fusion, learning
parameters and the final prediction methods. Based on the evaluations, very
competitive results are attained on two popular video classification
benchmarks. We hope that the discussions and conclusions from this work can
help researchers in related fields to quickly set up a good basis for further
investigations along this very promising direction.
Robust real time face recognition and tracking on gpu using fusion of rgb and depth image
Authors: Narmada Naik, G. N Rathna
Date: 2015-04-08 00:00:00
Tasks: Face Recognition, GPU

This paper presents a real-time face recognition system using kinect sensor.
The algorithm is implemented on GPU using opencl and significant speed
improvements are observed. We use kinect depth image to increase the robustness
and reduce computational cost of conventional LBP based face recognition. The
main objective of this paper was to perform robust, high speed fusion based
face recognition and tracking. The algorithm is mainly composed of three steps.
First step is to detect all faces in the video using viola jones algorithm. The
second step is online database generation using a tracking window on the face.
A modified LBP feature vector is calculated using fusion information from depth
and greyscale image on GPU. This feature vector is used to train a svm
classifier. Third step involves recognition of multiple faces based on our
modified feature vector.
Modeling Brain Circuitry over a Wide Range of Scales
Authors: Pascal Fua, Graham Knott
Date: 2015-02-13 00:00:00

If we are ever to unravel the mysteries of brain function at its most
fundamental level, we will need a precise understanding of how its component
neurons connect to each other. Electron Microscopes (EM) can now provide the
nanometer resolution that is needed to image synapses, and therefore
connections, while Light Microscopes (LM) see at the micrometer resolution
required to model the 3D structure of the dendritic network. Since both the
topology and the connection strength are integral parts of the brain's wiring
diagram, being able to combine these two modalities is critically important.
  In fact, these microscopes now routinely produce high-resolution imagery in
such large quantities that the bottleneck becomes automated processing and
interpretation, which is needed for such data to be exploited to its full
potential. In this paper, we briefly review the Computer Vision techniques we
have developed at EPFL to address this need. They include delineating dendritic
arbors from LM imagery, segmenting organelles from EM, and combining the two
into a consistent representation.
Autonomous CRM Control via CLV Approximation with Deep Reinforcement Learning in Discrete and Continuous Action Space
Authors: Yegor Tkachenko
Date: 2015-04-08 00:00:00
Tasks: Deep Reinforcement Learning, Management, Marketing, Position, Q-Learning, Reinforcement Learning

The paper outlines a framework for autonomous control of a CRM (customer
relationship management) system. First, it explores how a modified version of
the widely accepted Recency-Frequency-Monetary Value system of metrics can be
used to define the state space of clients or donors. Second, it describes a
procedure to determine the optimal direct marketing action in discrete and
continuous action space for the given individual, based on his position in the
state space. The procedure involves the use of model-free Q-learning to train a
deep neural network that relates a client's position in the state space to
rewards associated with possible marketing actions. The estimated value
function over the client state space can be interpreted as customer lifetime
value, and thus allows for a quick plug-in estimation of CLV for a given
client. Experimental results are presented, based on KDD Cup 1998 mailing
dataset of donation solicitations.
Structured Matrix Completion with Applications to Genomic Data Integration
Authors: Tianxi Cai, T. Tony Cai, Anru Zhang
Date: 2015-04-08 00:00:00
Tasks: Data Integration, Electrical Engineering, Matrix Completion

Matrix completion has attracted significant recent attention in many fields
including statistics, applied mathematics and electrical engineering. Current
literature on matrix completion focuses primarily on independent sampling
models under which the individual observed entries are sampled independently.
Motivated by applications in genomic data integration, we propose a new
framework of structured matrix completion (SMC) to treat structured missingness
by design. Specifically, our proposed method aims at efficient matrix recovery
when a subset of the rows and columns of an approximately low-rank matrix are
observed. We provide theoretical justification for the proposed SMC method and
derive lower bound for the estimation errors, which together establish the
optimal rate of recovery over certain classes of approximately low-rank
matrices. Simulation studies show that the method performs well in finite
sample under a variety of configurations. The method is applied to integrate
several ovarian cancer genomic studies with different extent of genomic
measurements, which enables us to construct more accurate prediction rules for
ovarian cancer survival.
Low Rank Representation on Grassmann Manifolds: An Extrinsic Perspective
Authors: Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Bao-Cai Yin
Date: 2015-04-08 00:00:00
Tasks: Clustering

Many computer vision algorithms employ subspace models to represent data. The
Low-rank representation (LRR) has been successfully applied in subspace
clustering for which data are clustered according to their subspace structures.
The possibility of extending LRR on Grassmann manifold is explored in this
paper. Rather than directly embedding Grassmann manifold into a symmetric
matrix space, an extrinsic view is taken by building the self-representation of
LRR over the tangent space of each Grassmannian point. A new algorithm for
solving the proposed Grassmannian LRR model is designed and implemented.
Several clustering experiments are conducted on handwritten digits dataset,
dynamic texture video clips and YouTube celebrity face video data. The
experimental results show our method outperforms a number of existing methods.
Kernelized Low Rank Representation on Grassmann Manifolds
Authors: Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Bao-Cai Yin
Date: 2015-04-08 00:00:00
Tasks: Clustering

Low rank representation (LRR) has recently attracted great interest due to
its pleasing efficacy in exploring low-dimensional subspace structures embedded
in data. One of its successful applications is subspace clustering which means
data are clustered according to the subspaces they belong to. In this paper, at
a higher level, we intend to cluster subspaces into classes of subspaces. This
is naturally described as a clustering problem on Grassmann manifold. The
novelty of this paper is to generalize LRR on Euclidean space onto an LRR model
on Grassmann manifold in a uniform kernelized framework. The new methods have
many applications in computer vision tasks. Several clustering experiments are
conducted on handwritten digit images, dynamic textures, human face clips and
traffic scene sequences. The experimental results show that the proposed
methods outperform a number of state-of-the-art subspace clustering methods.
Proximal operators for multi-agent path planning
Authors: José Bento, Nate Derbinsky, Charles Mathy, Jonathan S. Yedidia
Date: 2015-04-07 00:00:00

We address the problem of planning collision-free paths for multiple agents
using optimization methods known as proximal algorithms. Recently this approach
was explored in Bento et al. 2013, which demonstrated its ease of
parallelization and decentralization, the speed with which the algorithms
generate good quality solutions, and its ability to incorporate different
proximal operators, each ensuring that paths satisfy a desired property.
Unfortunately, the operators derived only apply to paths in 2D and require that
any intermediate waypoints we might want agents to follow be preassigned to
specific agents, limiting their range of applicability. In this paper we
resolve these limitations. We introduce new operators to deal with agents
moving in arbitrary dimensions that are faster to compute than their 2D
predecessors and we introduce landmarks, space-time positions that are
automatically assigned to the set of agents under different optimality
criteria. Finally, we report the performance of the new operators in several
numerical experiments.
A Simple Way to Initialize Recurrent Networks of Rectified Linear Units
Authors: Quoc V. Le, Navdeep Jaitly, Geoffrey E. Hinton
Date: 2015-04-03 00:00:00
Tasks: Language Modeling, Language Modelling, Sequential Image Classification, speech-recognition, Speech Recognition

Learning long term dependencies in recurrent networks is difficult due to
vanishing and exploding gradients. To overcome this difficulty, researchers
have developed sophisticated optimization techniques and network architectures.
In this paper, we propose a simpler solution that use recurrent neural networks
composed of rectified linear units. Key to our solution is the use of the
identity matrix or its scaled version to initialize the recurrent weight
matrix. We find that our solution is comparable to LSTM on our four benchmarks:
two toy problems involving long-range temporal structures, a large language
modeling problem and a benchmark speech recognition problem.
Transformation Properties of Learned Visual Representations
Authors: Taco S. Cohen, Max Welling
Date: 2014-12-24 00:00:00

When a three-dimensional object moves relative to an observer, a change
occurs on the observer's image plane and in the visual representation computed
by a learned model. Starting with the idea that a good visual representation is
one that transforms linearly under scene motions, we show, using the theory of
group representations, that any such representation is equivalent to a
combination of the elementary irreducible representations. We derive a striking
relationship between irreducibility and the statistical dependency structure of
the representation, by showing that under restricted conditions, irreducible
representations are decorrelated. Under partial observability, as induced by
the perspective projection of a scene onto the image plane, the motion group
does not have a linear action on the space of images, so that it becomes
necessary to perform inference over a latent representation that does transform
linearly. This idea is demonstrated in a model of rotating NORB objects that
employs a latent representation of the non-commutative 3D rotation group SO(3).
Consistent Collective Matrix Completion under Joint Low Rank Structure
Authors: Suriya Gunasekar, Makoto Yamada, Dawei Yin, Yi Chang
Date: 2014-12-05 00:00:00
Tasks: Matrix Completion

We address the collective matrix completion problem of jointly recovering a
collection of matrices with shared structure from partial (and potentially
noisy) observations. To ensure well--posedness of the problem, we impose a
joint low rank structure, wherein each component matrix is low rank and the
latent space of the low rank factors corresponding to each entity is shared
across the entire collection. We first develop a rigorous algebra for
representing and manipulating collective--matrix structure, and identify
sufficient conditions for consistent estimation of collective matrices. We then
propose a tractable convex estimator for solving the collective matrix
completion problem, and provide the first non--trivial theoretical guarantees
for consistency of collective matrix completion. We show that under reasonable
assumptions stated in Section 3.1, with high probability, the proposed
estimator exactly recovers the true matrices whenever sample complexity
requirements dictated by Theorem 1 are met. The sample complexity requirement
derived in the paper are optimum up to logarithmic factors, and significantly
improve upon the requirements obtained by trivial extensions of standard matrix
completion. Finally, we propose a scalable approximate algorithm to solve the
proposed convex program, and corroborate our results through simulated
experiments.
Design and Implementation of a 3D Undersea Camera System
Authors: Xida Chen, Steve Sutphen, Paul Macoun, Yee-Hong Yang
Date: 2015-04-07 00:00:00

In this paper, we present the design and development of an undersea camera
system. The goal of our system is to provide a 3D model of the undersea habitat
in a long-term continuous manner. The most important feature of our system is
the use of multiple cameras and multiple projectors, which is able to provide
accurate 3D models with an accuracy of a millimeter. By introducing projectors
in our system, we can use many different structured light methods for different
tasks. There are two main advantages comparing our system with using ROVs or
AUVs. First, our system can provide continuous monitoring of the undersea
habitat. Second, our system has a low hardware cost. Comparing to existing
deployed camera systems, the advantage of our system is that it can provide
accurate 3D models and provides opportunities for future development of
innovative algorithms for undersea research.