pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Detection and Decoding of Photogrammetric Coded Targets
Authors: Udaya Wijenayake, Sung-In Choi, Soon-Yong Park
Date: 2016-01-04 00:00:00

Close-range Photogrammetry is widely used in many industries because of the
cost effectiveness and efficiency of the technique. In this research, we
introduce an automated coded target detection method which can be used to
enhance the efficiency of the Photogrammetry.
On the Reducibility of Submodular Functions
Authors: Jincheng Mei, Hao Zhang, Bao-liang Lu
Date: 2016-01-04 00:00:00

The scalability of submodular optimization methods is critical for their
usability in practice. In this paper, we study the reducibility of submodular
functions, a property that enables us to reduce the solution space of
submodular optimization problems without performance loss. We introduce the
concept of reducibility using marginal gains. Then we show that by adding
perturbation, we can endow irreducible functions with reducibility, based on
which we propose the perturbation-reduction optimization framework. Our
theoretical analysis proves that given the perturbation scales, the
reducibility gain could be computed, and the performance loss has additive
upper bounds. We further conduct empirical studies and the results demonstrate
that our proposed framework significantly accelerates existing optimization
methods for irreducible submodular functions with a cost of only small
performance losses.
Unsupervised Learning of Video Representations using LSTMs
Authors: Nitish Srivastava, Elman Mansimov, Ruslan Salakhutdinov
Date: 2015-02-16 00:00:00
Tasks: Action Recognition, Decoder, Temporal Action Localization

We use multilayer Long Short Term Memory (LSTM) networks to learn
representations of video sequences. Our model uses an encoder LSTM to map an
input sequence into a fixed length representation. This representation is
decoded using single or multiple decoder LSTMs to perform different tasks, such
as reconstructing the input sequence, or predicting the future sequence. We
experiment with two kinds of input sequences - patches of image pixels and
high-level representations ("percepts") of video frames extracted using a
pretrained convolutional net. We explore different design choices such as
whether the decoder LSTMs should condition on the generated output. We analyze
the outputs of the model qualitatively to see how well the model can
extrapolate the learned video representation into the future and into the past.
We try to visualize and interpret the learned features. We stress test the
model by running it on longer time scales and on out-of-domain data. We further
evaluate the representations by finetuning them for a supervised learning
problem - human action recognition on the UCF-101 and HMDB-51 datasets. We show
that the representations help improve classification accuracy, especially when
there are only a few training examples. Even models pretrained on unrelated
datasets (300 hours of YouTube videos) can help action recognition performance.
Sparse Diffusion Steepest-Descent for One Bit Compressed Sensing in Wireless Sensor Networks
Authors: Hadi Zayyani, Mehdi Korki, Farrokh Marvasti
Date: 2016-01-03 00:00:00
Tasks: compressed sensing

This letter proposes a sparse diffusion steepest-descent algorithm for one
bit compressed sensing in wireless sensor networks. The approach exploits the
diffusion strategy from distributed learning in the one bit compressed sensing
framework. To estimate a common sparse vector cooperatively from only the sign
of measurements, steepest-descent is used to minimize the suitable global and
local convex cost functions. A diffusion strategy is suggested for distributive
learning of the sparse vector. Simulation results show the effectiveness of the
proposed distributed algorithm compared to the state-of-the-art non
distributive algorithms in the one bit compressed sensing framework.
Backward and Forward Language Modeling for Constrained Sentence Generation
Authors: Lili Mou, Rui Yan, Ge Li, Lu Zhang, Zhi Jin
Date: 2015-12-21 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Question Answering, Sentence, Text Generation, Translation

Recent language models, especially those based on recurrent neural networks
(RNNs), make it possible to generate natural language from a learned
probability. Language generation has wide applications including machine
translation, summarization, question answering, conversation systems, etc.
Existing methods typically learn a joint probability of words conditioned on
additional information, which is (either statically or dynamically) fed to
RNN's hidden layer. In many applications, we are likely to impose hard
constraints on the generated texts, i.e., a particular word must appear in the
sentence. Unfortunately, existing approaches could not solve this problem. In
this paper, we propose a novel backward and forward language model. Provided a
specific word, we use RNNs to generate previous words and future words, either
simultaneously or asynchronously, resulting in two model variants. In this way,
the given word could appear at any position in the sentence. Experimental
results show that the generated texts are comparable to sequential LMs in
quality.
Reducing the Training Time of Neural Networks by Partitioning
Authors: Conrado S. Miranda, Fernando J. Von Zuben
Date: 2015-11-10 00:00:00

This paper presents a new method for pre-training neural networks that can
decrease the total training time for a neural network while maintaining the
final performance, which motivates its use on deep neural networks. By
partitioning the training task in multiple training subtasks with sub-models,
which can be performed independently and in parallel, it is shown that the size
of the sub-models reduces almost quadratically with the number of subtasks
created, quickly scaling down the sub-models used for the pre-training. The
sub-models are then merged to provide a pre-trained initial set of weights for
the original model. The proposed method is independent of the other aspects of
the training, such as architecture of the neural network, training method, and
objective, making it compatible with a wide range of existing approaches. The
speedup without loss of performance is validated experimentally on MNIST and on
CIFAR10 data sets, also showing that even performing the subtasks sequentially
can decrease the training time. Moreover, we show that larger models may
present higher speedups and conjecture about the benefits of the method in
distributed learning systems.
Wavelet Scattering on the Pitch Spiral
Authors: Vincent Lostanlen, Stéphane Mallat
Date: 2016-01-03 00:00:00

We present a new representation of harmonic sounds that linearizes the
dynamics of pitch and spectral envelope, while remaining stable to deformations
in the time-frequency plane. It is an instance of the scattering transform, a
generic operator which cascades wavelet convolutions and modulus
nonlinearities. It is derived from the pitch spiral, in that convolutions are
successively performed in time, log-frequency, and octave index. We give a
closed-form approximation of spiral scattering coefficients for a nonstationary
generalization of the harmonic source-filter model.
Image Resolution Enhancement by Using Interpolation Followed by Iterative Back Projection
Authors: Pejman Rasti, Hasan Demirel, Gholamreza Anbarjafari
Date: 2016-01-03 00:00:00
Tasks: Image Super-Resolution, SSIM, Super-Resolution

In this paper, we propose a new super resolution technique based on the
interpolation followed by registering them using iterative back projection
(IBP). Low resolution images are being interpolated and then the interpolated
images are being registered in order to generate a sharper high resolution
image. The proposed technique has been tested on Lena, Elaine, Pepper, and
Baboon. The quantitative peak signal-to-noise ratio (PSNR) and structural
similarity index (SSIM) results as well as the visual results show the
superiority of the proposed technique over the conventional and state-of-art
image super resolution techniques. For Lena's image, the PSNR is 6.52 dB higher
than the bicubic interpolation.
Supervised Dimensionality Reduction via Distance Correlation Maximization
Authors: Praneeth Vepakomma, Chetan Tonde, Ahmed Elgammal
Date: 2016-01-03 00:00:00
Tasks: Dimensionality Reduction, regression, Supervised dimensionality reduction

In our work, we propose a novel formulation for supervised dimensionality
reduction based on a nonlinear dependency criterion called Statistical Distance
Correlation, Szekely et. al. (2007). We propose an objective which is free of
distributional assumptions on regression variables and regression model
assumptions. Our proposed formulation is based on learning a low-dimensional
feature representation $\mathbf{z}$, which maximizes the squared sum of
Distance Correlations between low dimensional features $\mathbf{z}$ and
response $y$, and also between features $\mathbf{z}$ and covariates
$\mathbf{x}$. We propose a novel algorithm to optimize our proposed objective
using the Generalized Minimization Maximizaiton method of \Parizi et. al.
(2015). We show superior empirical results on multiple datasets proving the
effectiveness of our proposed approach over several relevant state-of-the-art
supervised dimensionality reduction methods.
Supervised Texture Segmentation: A Comparative Study
Authors: Omar S. Al-Kadi
Date: 2016-01-02 00:00:00
Tasks: Segmentation

This paper aims to compare between four different types of feature extraction
approaches in terms of texture segmentation. The feature extraction methods
that were used for segmentation are Gabor filters (GF), Gaussian Markov random
fields (GMRF), run-length matrix (RLM) and co-occurrence matrix (GLCM). It was
shown that the GF performed best in terms of quality of segmentation while the
GLCM localises the texture boundaries better as compared to the other methods.
A fractal dimension based optimal wavelet packet analysis technique for classification of meningioma brain tumours
Authors: Omar S. Al-Kadi
Date: 2016-01-02 00:00:00
Tasks: Classification, General Classification

With the heterogeneous nature of tissue texture, using a single resolution
approach for optimum classification might not suffice. In contrast, a
multiresolution wavelet packet analysis can decompose the input signal into a
set of frequency subbands giving the opportunity to characterise the texture at
the appropriate frequency channel. An adaptive best bases algorithm for optimal
bases selection for meningioma histopathological images is proposed, via
applying the fractal dimension (FD) as the bases selection criterion in a
tree-structured manner. Thereby, the most significant subband that better
identifies texture discontinuities will only be chosen for further
decomposition, and its fractal signature would represent the extracted feature
vector for classification. The best basis selection using the FD outperformed
the energy based selection approaches, achieving an overall classification
accuracy of 91.25% as compared to 83.44% and 73.75% for the co-occurrence
matrix and energy texture signatures; respectively.
Susceptibility of texture measures to noise: an application to lung tumor CT images
Authors: O. S. Al-Kadi, D. Watson
Date: 2016-01-02 00:00:00
Tasks: Computed Tomography (CT)

Five different texture methods are used to investigate their susceptibility
to subtle noise occurring in lung tumor Computed Tomography (CT) images caused
by acquisition and reconstruction deficiencies. Noise of Gaussian and Rayleigh
distributions with varying mean and variance was encountered in the analyzed CT
images. Fisher and Bhattacharyya distance measures were used to differentiate
between an original extracted lung tumor region of interest (ROI) with a
filtered and noisy reconstructed versions. Through examining the texture
characteristics of the lung tumor areas by five different texture measures, it
was determined that the autocovariance measure was least affected and the gray
level co-occurrence matrix was the most affected by noise. Depending on the
selected ROI size, it was concluded that the number of extracted features from
each texture measure increases susceptibility to noise.
A Unified Framework for Compositional Fitting of Active Appearance Models
Authors: Joan Alabort-i-Medina, Stefanos Zafeiriou
Date: 2016-01-02 00:00:00

Active Appearance Models (AAMs) are one of the most popular and
well-established techniques for modeling deformable objects in computer vision.
In this paper, we study the problem of fitting AAMs using Compositional
Gradient Descent (CGD) algorithms. We present a unified and complete view of
these algorithms and classify them with respect to three main characteristics:
i) cost function; ii) type of composition; and iii) optimization method.
Furthermore, we extend the previous view by: a) proposing a novel Bayesian cost
function that can be interpreted as a general probabilistic formulation of the
well-known project-out loss; b) introducing two new types of composition,
asymmetric and bidirectional, that combine the gradients of both image and
appearance model to derive better conver- gent and more robust CGD algorithms;
and c) providing new valuable insights into existent CGD algorithms by
reinterpreting them as direct applications of the Schur complement and the
Wiberg method. Finally, in order to encourage open research and facilitate
future comparisons with our work, we make the implementa- tion of the
algorithms studied in this paper publicly available as part of the Menpo
Project.
An Improved Intelligent Agent for Mining Real-Time Databases Using Modified Cortical Learning Algorithms
Authors: N. E. Osegi
Date: 2016-01-02 00:00:00

Cortical Learning Algorithms based on the Hierarchical Temporal Memory, HTM
have been developed by Numenta Incorporation from which variations and
modifications are currently being investigated upon. HTM offers better promises
as a future computational model of the neocortex the seat of intelligence in
the brain. Currently, intelligent agents are embedded in almost every modern
day electronic system found in homes, offices and industries worldwide. In this
paper, we present a first step in realising useful HTM like applications
specifically for mining a synthetic and real time dataset based on a novel
intelligent agent framework, and demonstrate how a modified version of this
very important computational technique will lead to improved recognition.
Adaptive Independent Sticky MCMC algorithms
Authors: L. Martino, R. Casarin, F. Leisen, D. Luengo
Date: 2013-08-17 00:00:00

In this work, we introduce a novel class of adaptive Monte Carlo methods,
called adaptive independent sticky MCMC algorithms, for efficient sampling from
a generic target probability density function (pdf). The new class of
algorithms employs adaptive non-parametric proposal densities which become
closer and closer to the target as the number of iterations increases. The
proposal pdf is built using interpolation procedures based on a set of support
points which is constructed iteratively based on previously drawn samples. The
algorithm's efficiency is ensured by a test that controls the evolution of the
set of support points. This extra stage controls the computational cost and the
convergence of the proposal density to the target. Each part of the novel
family of algorithms is discussed and several examples are provided. Although
the novel algorithms are presented for univariate target densities, we show
that they can be easily extended to the multivariate context within a
Gibbs-type sampler. The ergodicity is ensured and discussed. Exhaustive
numerical examples illustrate the efficiency of sticky schemes, both as a
stand-alone methods to sample from complicated one-dimensional pdfs and within
Gibbs in order to draw from multi-dimensional target distributions.
Joint Estimation of Precision Matrices in Heterogeneous Populations
Authors: Takumi Saegusa, Ali Shojaie
Date: 2016-01-02 00:00:00
Tasks: Clustering, parameter estimation, Variable Selection

We introduce a general framework for estimation of inverse covariance, or
precision, matrices from heterogeneous populations. The proposed framework uses
a Laplacian shrinkage penalty to encourage similarity among estimates from
disparate, but related, subpopulations, while allowing for differences among
matrices. We propose an efficient alternating direction method of multipliers
(ADMM) algorithm for parameter estimation, as well as its extension for faster
computation in high dimensions by thresholding the empirical covariance matrix
to identify the joint block diagonal structure in the estimated precision
matrices. We establish both variable selection and norm consistency of the
proposed estimator for distributions with exponential or polynomial tails.
Further, to extend the applicability of the method to the settings with unknown
populations structure, we propose a Laplacian penalty based on hierarchical
clustering, and discuss conditions under which this data-driven choice results
in consistent estimation of precision matrices in heterogenous populations.
Extensive numerical studies and applications to gene expression data from
subtypes of cancer with distinct clinical outcomes indicate the potential
advantages of the proposed method over existing approaches.
Real-Time Anomalous Behavior Detection and Localization in Crowded Scenes
Authors: Mohammad Sabokrou, Mahmood Fathy, Mojtaba Hosseini
Date: 2015-11-21 00:00:00
Tasks: Anomaly Detection

In this paper, we propose an accurate and real-time anomaly detection and
localization in crowded scenes, and two descriptors for representing anomalous
behavior in video are proposed. We consider a video as being a set of cubic
patches. Based on the low likelihood of an anomaly occurrence, and the
redundancy of structures in normal patches in videos, two (global and local)
views are considered for modeling the video. Our algorithm has two components,
for (1) representing the patches using local and global descriptors, and for
(2) modeling the training patches using a new representation. We have two
Gaussian models for all training patches respect to global and local
descriptors. The local and global features are based on structure similarity
between adjacent patches and the features that are learned in an unsupervised
way. We propose a fusion strategy to combine the two descriptors as the output
of our system. Experimental results show that our algorithm performs like a
state-of-the-art method on several standard datasets, but even is more
time-efficient.
Discriminative Sparsity for Sonar ATR
Authors: John McKay, Raghu Raj, Vishal Monga, Jason Isaacs
Date: 2016-01-01 00:00:00

Advancements in Sonar image capture have enabled researchers to apply
sophisticated object identification algorithms in order to locate targets of
interest in images such as mines. Despite progress in this field, modern sonar
automatic target recognition (ATR) approaches lack robustness to the amount of
noise one would expect in real-world scenarios, the capability to handle
blurring incurred from the physics of image capture, and the ability to excel
with relatively few training samples. We address these challenges by adapting
modern sparsity-based techniques with dictionaries comprising of training from
each class. We develop new discriminative (as opposed to generative) sparse
representations which can help automatically classify targets in Sonar imaging.
Using a simulated SAS data set from the Naval Surface Warfare Center (NSWC), we
obtained compelling classification rates for multi-class problems even in cases
with considerable noise and sparsity in training samples.
Computational Pathology: Challenges and Promises for Tissue Analysis
Authors: Thomas J. Fuchs, Joachim M. Buhmann
Date: 2015-12-31 00:00:00
Tasks: Diagnostic, General Classification

The histological assessment of human tissue has emerged as the key challenge
for detection and treatment of cancer. A plethora of different data sources
ranging from tissue microarray data to gene expression, proteomics or
metabolomics data provide a detailed overview of the health status of a
patient. Medical doctors need to assess these information sources and they rely
on data driven automatic analysis tools. Methods for classification, grouping
and segmentation of heterogeneous data sources as well as regression of noisy
dependencies and estimation of survival probabilities enter the processing
workflow of a pathology diagnosis system at various stages. This paper reports
on state-of-the-art of the design and effectiveness of computational pathology
workflows and it discusses future research directions in this emergent field of
medical informatics and diagnostic machine learning.
Selecting Near-Optimal Learners via Incremental Data Allocation
Authors: Ashish Sabharwal, Horst Samulowitz, Gerald Tesauro
Date: 2015-12-31 00:00:00

We study a novel machine learning (ML) problem setting of sequentially
allocating small subsets of training data amongst a large set of classifiers.
The goal is to select a classifier that will give near-optimal accuracy when
trained on all data, while also minimizing the cost of misallocated samples.
This is motivated by large modern datasets and ML toolkits with many
combinations of learning algorithms and hyper-parameters. Inspired by the
principle of "optimism under uncertainty," we propose an innovative strategy,
Data Allocation using Upper Bounds (DAUB), which robustly achieves these
objectives across a variety of real-world datasets.
  We further develop substantial theoretical support for DAUB in an idealized
setting where the expected accuracy of a classifier trained on $n$ samples can
be known exactly. Under these conditions we establish a rigorous sub-linear
bound on the regret of the approach (in terms of misallocated data), as well as
a rigorous bound on suboptimality of the selected classifier. Our accuracy
estimates using real-world datasets only entail mild violations of the
theoretical scenario, suggesting that the practical behavior of DAUB is likely
to approach the idealized behavior.
A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data
Authors: Yin Zheng, Yu-Jin Zhang, Hugo Larochelle
Date: 2014-09-13 00:00:00
Tasks: image-classification, Image Classification, Information Retrieval, Retrieval, Topic Models

Topic modeling based on latent Dirichlet allocation (LDA) has been a
framework of choice to deal with multimodal data, such as in image annotation
tasks. Another popular approach to model the multimodal data is through deep
neural networks, such as the deep Boltzmann machine (DBM). Recently, a new type
of topic model called the Document Neural Autoregressive Distribution Estimator
(DocNADE) was proposed and demonstrated state-of-the-art performance for text
document modeling. In this work, we show how to successfully apply and extend
this model to multimodal data, such as simultaneous image classification and
annotation. First, we propose SupDocNADE, a supervised extension of DocNADE,
that increases the discriminative power of the learned hidden topic features
and show how to employ it to learn a joint representation from image visual
words, annotation words and class label information. We test our model on the
LabelMe and UIUC-Sports data sets and show that it compares favorably to other
topic models. Second, we propose a deep extension of our model and provide an
efficient way of training the deep model. Experimental results show that our
deep model outperforms its shallow version and reaches state-of-the-art
performance on the Multimedia Information Retrieval (MIR) Flickr data set.