pwc-archive/papers-with-abstracts huggingface dataset:
MAP Support Detection for Greedy Sparse Signal Recovery Algorithms in Compressive Sensing
Authors: Namyoon Lee
Date: 2015-08-05 00:00:00
Tasks: Compressive Sensing

A reliable support detection is essential for a greedy algorithm to
reconstruct a sparse signal accurately from compressed and noisy measurements.
This paper proposes a novel support detection method for greedy algorithms,
which is referred to as "\textit{maximum a posteriori (MAP) support
detection}". Unlike existing support detection methods that identify support
indices with the largest correlation value in magnitude per iteration, the
proposed method selects them with the largest likelihood ratios computed under
the true and null support hypotheses by simultaneously exploiting the
distributions of sensing matrix, sparse signal, and noise. Leveraging this
technique, MAP-Matching Pursuit (MAP-MP) is first presented to show the
advantages of exploiting the proposed support detection method, and a
sufficient condition for perfect signal recovery is derived for the case when
the sparse signal is binary. Subsequently, a set of iterative greedy
algorithms, called MAP-generalized Orthogonal Matching Pursuit (MAP-gOMP),
MAP-Compressive Sampling Matching Pursuit (MAP-CoSaMP), and MAP-Subspace
Pursuit (MAP-SP) are presented to demonstrate the applicability of the proposed
support detection method to existing greedy algorithms. From empirical results,
it is shown that the proposed greedy algorithms with highly reliable support
detection can be better, faster, and easier to implement than basis pursuit via
linear programming.
Discriminative Training of Deep Fully-connected Continuous CRF with Task-specific Loss
Authors: Fayao Liu, Guosheng Lin, Chunhua Shen
Date: 2016-01-28 00:00:00
Tasks: Depth Estimation, Multi-class Classification

Recent works on deep conditional random fields (CRF) have set new records on
many vision tasks involving structured predictions. Here we propose a
fully-connected deep continuous CRF model for both discrete and continuous
labelling problems. We exemplify the usefulness of the proposed model on
multi-class semantic labelling (discrete) and the robust depth estimation
(continuous) problems.
  In our framework, we model both the unary and the pairwise potential
functions as deep convolutional neural networks (CNN), which are jointly
learned in an end-to-end fashion. The proposed method possesses the main
advantage of continuously-valued CRF, which is a closed-form solution for the
Maximum a posteriori (MAP) inference.
  To better adapt to different tasks, instead of using the commonly employed
maximum likelihood CRF parameter learning protocol, we propose task-specific
loss functions for learning the CRF parameters.
  It enables direct optimization of the quality of the MAP estimates during the
course of learning.
  Specifically, we optimize the multi-class classification loss for the
semantic labelling task and the Turkey's biweight loss for the robust depth
estimation problem.
  Experimental results on the semantic labelling and robust depth estimation
tasks demonstrate that the proposed method compare favorably against both
baseline and state-of-the-art methods.
  In particular, we show that although the proposed deep CRF model is
continuously valued, with the equipment of task-specific loss, it achieves
impressive results even on discrete labelling tasks.
Sparse Generalized Principal Component Analysis for Large-scale Applications beyond Gaussianity
Authors: Qiaoya Zhang, Yiyuan She
Date: 2015-12-12 00:00:00
Tasks: Dimensionality Reduction, Missing Values

Principal Component Analysis (PCA) is a dimension reduction technique. It
produces inconsistent estimators when the dimensionality is moderate to high,
which is often the problem in modern large-scale applications where algorithm
scalability and model interpretability are difficult to achieve, not to mention
the prevalence of missing values. While existing sparse PCA methods alleviate
inconsistency, they are constrained to the Gaussian assumption of classical PCA
and fail to address algorithm scalability issues. We generalize sparse PCA to
the broad exponential family distributions under high-dimensional setup, with
built-in treatment for missing values. Meanwhile we propose a family of
iterative sparse generalized PCA (SG-PCA) algorithms such that despite the
non-convexity and non-smoothness of the optimization task, the loss function
decreases in every iteration. In terms of ease and intuitive parameter tuning,
our sparsity-inducing regularization is far superior to the popular Lasso.
Furthermore, to promote overall scalability, accelerated gradient is integrated
for fast convergence, while a progressive screening technique gradually
squeezes out nuisance dimensions of a large-scale problem for feasible
optimization. High-dimensional simulation and real data experiments demonstrate
the efficiency and efficacy of SG-PCA.
Efficient Hill-Climber for Multi-Objective Pseudo-Boolean Optimization
Authors: Francisco Chicano, Darrell Whitley, Renato Tinos
Date: 2016-01-27 00:00:00
Tasks: Evolutionary Algorithms

Local search algorithms and iterated local search algorithms are a basic
technique. Local search can be a stand along search methods, but it can also be
hybridized with evolutionary algorithms. Recently, it has been shown that it is
possible to identify improving moves in Hamming neighborhoods for k-bounded
pseudo-Boolean optimization problems in constant time. This means that local
search does not need to enumerate neighborhoods to find improving moves. It
also means that evolutionary algorithms do not need to use random mutation as a
operator, except perhaps as a way to escape local optima. In this paper, we
show how improving moves can be identified in constant time for multiobjective
problems that are expressed as k-bounded pseudo-Boolean functions. In
particular, multiobjective forms of NK Landscapes and Mk Landscapes are
considered.
Multi-Target Regression via Input Space Expansion: Treating Targets as Inputs
Authors: Eleftherios Spyromitros-Xioufis, Grigorios Tsoumakas, William Groves, Ioannis Vlahavas
Date: 2012-11-28 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-target regression, Multi-Task Learning, regression

In many practical applications of supervised learning the task involves the
prediction of multiple target variables from a common set of input variables.
When the prediction targets are binary the task is called multi-label
classification, while when the targets are continuous the task is called
multi-target regression. In both tasks, target variables often exhibit
statistical dependencies and exploiting them in order to improve predictive
accuracy is a core challenge. A family of multi-label classification methods
address this challenge by building a separate model for each target on an
expanded input space where other targets are treated as additional input
variables. Despite the success of these methods in the multi-label
classification domain, their applicability and effectiveness in multi-target
regression has not been studied until now. In this paper, we introduce two new
methods for multi-target regression, called Stacked Single-Target and Ensemble
of Regressor Chains, by adapting two popular multi-label classification methods
of this family. Furthermore, we highlight an inherent problem of these methods
- a discrepancy of the values of the additional input variables between
training and prediction - and develop extensions that use out-of-sample
estimates of the target variables during training in order to tackle this
problem. The results of an extensive experimental evaluation carried out on a
large and diverse collection of datasets show that, when the discrepancy is
appropriately mitigated, the proposed methods attain consistent improvements
over the independent regressions baseline. Moreover, two versions of Ensemble
of Regression Chains perform significantly better than four state-of-the-art
methods including regularization-based multi-task learning methods and a
multi-objective random forest approach.
Artificial neural networks in calibration of nonlinear mechanical models
Authors: Tomáš Mareš, Eliška Janouchová, Anna Kučerová
Date: 2015-02-04 00:00:00

Rapid development in numerical modelling of materials and the complexity of
new models increases quickly together with their computational demands. Despite
the growing performance of modern computers and clusters, calibration of such
models from noisy experimental data remains a nontrivial and often
computationally exhaustive task. The layered neural networks thus represent a
robust and efficient technique to overcome the time-consuming simulations of a
calibrated model. The potential of neural networks consists in simple
implementation and high versatility in approximating nonlinear relationships.
Therefore, there were several approaches proposed to accelerate the calibration
of nonlinear models by neural networks. This contribution reviews and compares
three possible strategies based on approximating (i) model response, (ii)
inverse relationship between the model response and its parameters and (iii)
error function quantifying how well the model fits the data. The advantages and
drawbacks of particular strategies are demonstrated on the calibration of four
parameters of the affinity hydration model from simulated data as well as from
experimental measurements. This model is highly nonlinear, but computationally
cheap thus allowing its calibration without any approximation and better
quantification of results obtained by the examined calibration strategies. The
paper can be thus viewed as a guide intended for the engineers to help them
select an appropriate strategy in their particular calibration problems.
Osteoporotic and Neoplastic Compression Fracture Classification on Longitudinal CT
Authors: Yinong Wang, Jianhua Yao, Joseph E. Burns, Ronald M. Summers
Date: 2016-01-27 00:00:00
Tasks: Classification, General Classification

Classification of vertebral compression fractures (VCF) having osteoporotic
or neoplastic origin is fundamental to the planning of treatment. We developed
a fracture classification system by acquiring quantitative morphologic and bone
density determinants of fracture progression through the use of automated
measurements from longitudinal studies. A total of 250 CT studies were acquired
for the task, each having previously identified VCFs with osteoporosis or
neoplasm. Thirty-six features or each identified VCF were computed and
classified using a committee of support vector machines. Ten-fold cross
validation on 695 identified fractured vertebrae showed classification
accuracies of 0.812, 0.665, and 0.820 for the measured, longitudinal, and
combined feature sets respectively.
Learning to Extract Motion from Videos in Convolutional Neural Networks
Authors: Damien Teney, Martial Hebert
Date: 2016-01-27 00:00:00
Tasks: Motion Estimation, Optical Flow Estimation

This paper shows how to extract dense optical flow from videos with a
convolutional neural network (CNN). The proposed model constitutes a potential
building block for deeper architectures to allow using motion without resorting
to an external algorithm, \eg for recognition in videos. We derive our network
architecture from signal processing principles to provide desired invariances
to image contrast, phase and texture. We constrain weights within the network
to enforce strict rotation invariance and substantially reduce the number of
parameters to learn. We demonstrate end-to-end training on only 8 sequences of
the Middlebury dataset, orders of magnitude less than competing CNN-based
motion estimation methods, and obtain comparable performance to classical
methods on the Middlebury benchmark. Importantly, our method outputs a
distributed representation of motion that allows representing multiple,
transparent motions, and dynamic textures. Our contributions on network design
and rotation invariance offer insights nonspecific to motion estimation.
FlatCam: Thin, Bare-Sensor Cameras using Coded Aperture and Computation
Authors: M. Salman Asif, Ali Ayremlou, Aswin Sankaranarayanan, Ashok Veeraraghavan, Richard Baraniuk
Date: 2015-09-01 00:00:00
Tasks: Image Reconstruction

FlatCam is a thin form-factor lensless camera that consists of a coded mask
placed on top of a bare, conventional sensor array. Unlike a traditional,
lens-based camera where an image of the scene is directly recorded on the
sensor pixels, each pixel in FlatCam records a linear combination of light from
multiple scene elements. A computational algorithm is then used to demultiplex
the recorded measurements and reconstruct an image of the scene. FlatCam is an
instance of a coded aperture imaging system; however, unlike the vast majority
of related work, we place the coded mask extremely close to the image sensor
that can enable a thin system. We employ a separable mask to ensure that both
calibration and image reconstruction are scalable in terms of memory
requirements and computational complexity. We demonstrate the potential of the
FlatCam design using two prototypes: one at visible wavelengths and one at
infrared wavelengths.
Analysis and approximation of some Shape-from-Shading models for non-Lambertian surfaces
Authors: Silvia Tozza, Maurizio Falcone
Date: 2015-02-18 00:00:00

The reconstruction of a 3D object or a scene is a classical inverse problem
in Computer Vision. In the case of a single image this is called the
Shape-from-Shading (SfS) problem and it is known to be ill-posed even in a
simplified version like the vertical light source case. A huge number of works
deals with the orthographic SfS problem based on the Lambertian reflectance
model, the most common and simplest model which leads to an eikonal type
equation when the light source is on the vertical axis. In this paper we want
to study non-Lambertian models since they are more realistic and suitable
whenever one has to deal with different kind of surfaces, rough or specular. We
will present a unified mathematical formulation of some popular orthographic
non-Lambertian models, considering vertical and oblique light directions as
well as different viewer positions. These models lead to more complex
stationary nonlinear partial differential equations of Hamilton-Jacobi type
which can be regarded as the generalization of the classical eikonal equation
corresponding to the Lambertian case. However, all the equations corresponding
to the models considered here (Oren-Nayar and Phong) have a similar structure
so we can look for weak solutions to this class in the viscosity solution
framework. Via this unified approach, we are able to develop a semi-Lagrangian
approximation scheme for the Oren-Nayar and the Phong model and to prove a
general convergence result. Numerical simulations on synthetic and real images
will illustrate the effectiveness of this approach and the main features of the
scheme, also comparing the results with previous results in the literature.
Unsupervised Learning in Neuromemristive Systems
Authors: Cory Merkel, Dhireesha Kudithipudi
Date: 2016-01-27 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

Neuromemristive systems (NMSs) currently represent the most promising
platform to achieve energy efficient neuro-inspired computation. However, since
the research field is less than a decade old, there are still countless
algorithms and design paradigms to be explored within these systems. One
particular domain that remains to be fully investigated within NMSs is
unsupervised learning. In this work, we explore the design of an NMS for
unsupervised clustering, which is a critical element of several machine
learning algorithms. Using a simple memristor crossbar architecture and
learning rule, we are able to achieve performance which is on par with MATLAB's
k-means clustering.
Shape Distributions of Nonlinear Dynamical Systems for Video-based Inference
Authors: Vinay Venkataraman, Pavan Turaga
Date: 2016-01-27 00:00:00
Tasks: Activity Recognition, Gesture Recognition, Scene Classification, Time Series, Time Series Analysis

This paper presents a shape-theoretic framework for dynamical analysis of
nonlinear dynamical systems which appear frequently in several video-based
inference tasks. Traditional approaches to dynamical modeling have included
linear and nonlinear methods with their respective drawbacks. A novel approach
we propose is the use of descriptors of the shape of the dynamical attractor as
a feature representation of nature of dynamics. The proposed framework has two
main advantages over traditional approaches: a) representation of the dynamical
system is derived directly from the observational data, without any inherent
assumptions, and b) the proposed features show stability under different
time-series lengths where traditional dynamical invariants fail. We illustrate
our idea using nonlinear dynamical models such as Lorenz and Rossler systems,
where our feature representations (shape distribution) support our hypothesis
that the local shape of the reconstructed phase space can be used as a
discriminative feature. Our experimental analyses on these models also indicate
that the proposed framework show stability for different time-series lengths,
which is useful when the available number of samples are small/variable. The
specific applications of interest in this paper are: 1) activity recognition
using motion capture and RGBD sensors, 2) activity quality assessment for
applications in stroke rehabilitation, and 3) dynamical scene classification.
We provide experimental validation through action and gesture recognition
experiments on motion capture and Kinect datasets. In all these scenarios, we
show experimental evidence of the favorable properties of the proposed
representation.
Distributed User Association in Energy Harvesting Small Cell Networks: A Probabilistic Model
Authors: Setareh Maghsudi, Ekram Hossain
Date: 2016-01-27 00:00:00

We consider a distributed downlink user association problem in a small cell
network, where small cells obtain the required energy for providing wireless
services to users through ambient energy harvesting. Since energy harvesting is
opportunistic in nature, the amount of harvested energy is a random variable,
without any a priori known characteristics. Moreover, since users arrive in the
network randomly and require different wireless services, the energy
consumption is a random variable as well. In this paper, we propose a
probabilistic framework to mathematically model and analyze the random behavior
of energy harvesting and energy consumption in dense small cell networks.
Furthermore, as acquiring (even statistical) channel and network knowledge is
very costly in a distributed dense network, we develop a bandit-theoretical
formulation for distributed user association when no information is available
at users
High Dimensional Forecasting via Interpretable Vector Autoregression
Authors: William B. Nicholson, Ines Wilms, Jacob Bien, David S. Matteson
Date: 2014-12-17 00:00:00
Tasks: Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

Vector autoregression (VAR) is a fundamental tool for modeling multivariate time series. However, as the number of component series is increased, the VAR model becomes overparameterized. Several authors have addressed this issue by incorporating regularized approaches, such as the lasso in VAR estimation. Traditional approaches address overparameterization by selecting a low lag order, based on the assumption of short range dependence, assuming that a universal lag order applies to all components. Such an approach constrains the relationship between the components and impedes forecast performance. The lasso-based approaches work much better in high-dimensional situations but do not incorporate the notion of lag order selection. We propose a new class of hierarchical lag structures (HLag) that embed the notion of lag selection into a convex regularizer. The key modeling tool is a group lasso with nested groups which guarantees that the sparsity pattern of lag coefficients honors the VAR's ordered structure. The HLag framework offers three structures, which allow for varying levels of flexibility. A simulation study demonstrates improved performance in forecasting and lag order selection over previous approaches, and a macroeconomic application further highlights forecasting improvements as well as HLag's convenient, interpretable output.
A First Attempt to Cloud-Based User Verification in Distributed System
Authors: Marcin Wozniak, Dawid Polap, Grzegorz Borowik, Christian Napoli
Date: 2016-01-27 00:00:00

In this paper, the idea of client verification in distributed systems is
presented. The proposed solution presents a sample system where client
verification through cloud resources using input signature is discussed. For
different signatures the proposed method has been examined. Research results
are presented and discussed to show potential advantages.
Quantum machine learning with glow for episodic tasks and decision games
Authors: Jens Clausen, Hans J. Briegel
Date: 2016-01-27 00:00:00
Tasks: BIG-bench Machine Learning, Quantum Machine Learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider a general class of models, where a reinforcement learning (RL)
agent learns from cyclic interactions with an external environment via
classical signals. Perceptual inputs are encoded as quantum states, which are
subsequently transformed by a quantum channel representing the agent's memory,
while the outcomes of measurements performed at the channel's output determine
the agent's actions. The learning takes place via stepwise modifications of the
channel properties. They are described by an update rule that is inspired by
the projective simulation (PS) model and equipped with a glow mechanism that
allows for a backpropagation of policy changes, analogous to the eligibility
traces in RL and edge glow in PS. In this way, the model combines features of
PS with the ability for generalization, offered by its physical embodiment as a
quantum system. We apply the agent to various setups of an invasion game and a
grid world, which serve as elementary model tasks allowing a direct comparison
with a basic classical PS agent.
Learning Model-Based Sparsity via Projected Gradient Descent
Authors: Sohail Bahmani, Petros T. Boufounos, Bhiksha Raj
Date: 2012-09-07 00:00:00
Tasks: model

Several convex formulation methods have been proposed previously for
statistical estimation with structured sparsity as the prior. These methods
often require a carefully tuned regularization parameter, often a cumbersome or
heuristic exercise. Furthermore, the estimate that these methods produce might
not belong to the desired sparsity model, albeit accurately approximating the
true parameter. Therefore, greedy-type algorithms could often be more desirable
in estimating structured-sparse parameters. So far, these greedy methods have
mostly focused on linear statistical models. In this paper we study the
projected gradient descent with non-convex structured-sparse parameter model as
the constraint set. Should the cost function have a Stable Model-Restricted
Hessian the algorithm produces an approximation for the desired minimizer. As
an example we elaborate on application of the main results to estimation in
Generalized Linear Model.