pwc-archive/papers-with-abstracts huggingface dataset:
Multilayer Structured NMF for Spectral Unmixing of Hyperspectral Images
Authors: Roozbeh Rajabi, Hassan Ghassemian
Date: 2015-06-04 00:00:00

One of the challenges in hyperspectral data analysis is the presence of mixed
pixels. Mixed pixels are the result of low spatial resolution of hyperspectral
sensors. Spectral unmixing methods decompose a mixed pixel into a set of
endmembers and abundance fractions. Due to nonnegativity constraint on
abundance fraction values, NMF based methods are well suited to this problem.
In this paper multilayer NMF has been used to improve the results of NMF
methods for spectral unmixing of hyperspectral data under the linear mixing
framework. Sparseness constraint on both spectral signatures and abundance
fractions matrices are used in this paper. Evaluation of the proposed algorithm
is done using synthetic and real datasets in terms of spectral angle and
abundance angle distances. Results show that the proposed algorithm outperforms
other previously proposed methods.
Programs as Polypeptides
Authors: Lance R. Williams
Date: 2015-06-04 00:00:00

We describe a visual programming language for defining behaviors manifested
by reified actors in a 2D virtual world that can be compiled into programs
comprised of sequences of combinators that are themselves reified as actors.
This makes it possible to build programs that build programs from components of
a few fixed types delivered by diffusion using processes that resemble
chemistry as much as computation.
A Deep Embedding Model for Co-occurrence Learning
Authors: Yelong Shen, Ruoming Jin, Jianshu Chen, Xiaodong He, Jianfeng Gao, Li Deng
Date: 2015-04-11 00:00:00
Tasks: Clustering, model

Co-occurrence Data is a common and important information source in many
areas, such as the word co-occurrence in the sentences, friends co-occurrence
in social networks and products co-occurrence in commercial transaction data,
etc, which contains rich correlation and clustering information about the
items. In this paper, we study co-occurrence data using a general energy-based
probabilistic model, and we analyze three different categories of energy-based
model, namely, the $L_1$, $L_2$ and $L_k$ models, which are able to capture
different levels of dependency in the co-occurrence data. We also discuss how
several typical existing models are related to these three types of energy
models, including the Fully Visible Boltzmann Machine (FVBM) ($L_2$), Matrix
Factorization ($L_2$), Log-BiLinear (LBL) models ($L_2$), and the Restricted
Boltzmann Machine (RBM) model ($L_k$). Then, we propose a Deep Embedding Model
(DEM) (an $L_k$ model) from the energy model in a \emph{principled} manner.
Furthermore, motivated by the observation that the partition function in the
energy model is intractable and the fact that the major objective of modeling
the co-occurrence data is to predict using the conditional probability, we
apply the \emph{maximum pseudo-likelihood} method to learn DEM. In consequence,
the developed model and its learning method naturally avoid the above
difficulties and can be easily used to compute the conditional probability in
prediction. Interestingly, our method is equivalent to learning a special
structured deep neural network using back-propagation and a special sampling
strategy, which makes it scalable on large-scale datasets. Finally, in the
experiments, we show that the DEM can achieve comparable or better results than
state-of-the-art methods on datasets across several application domains.
Jointly Modeling Embedding and Translation to Bridge Video and Language
Authors: Yingwei Pan, Tao Mei, Ting Yao, Houqiang Li, Yong Rui
Date: 2015-05-07 00:00:00
Proceeding: CVPR 2016 6
Tasks: Sentence, Translation

Automatically describing video content with natural language is a fundamental
challenge of multimedia. Recurrent Neural Networks (RNN), which models sequence
dynamics, has attracted increasing attention on visual interpretation. However,
most existing approaches generate a word locally with given previous words and
the visual content, while the relationship between sentence semantics and
visual content is not holistically exploited. As a result, the generated
sentences may be contextually correct but the semantics (e.g., subjects, verbs
or objects) are not true.
  This paper presents a novel unified framework, named Long Short-Term Memory
with visual-semantic Embedding (LSTM-E), which can simultaneously explore the
learning of LSTM and visual-semantic embedding. The former aims to locally
maximize the probability of generating the next word given previous words and
visual content, while the latter is to create a visual-semantic embedding space
for enforcing the relationship between the semantics of the entire sentence and
visual content. Our proposed LSTM-E consists of three components: a 2-D and/or
3-D deep convolutional neural networks for learning powerful video
representation, a deep RNN for generating sentences, and a joint embedding
model for exploring the relationships between visual content and sentence
semantics. The experiments on YouTube2Text dataset show that our proposed
LSTM-E achieves to-date the best reported performance in generating natural
sentences: 45.3% and 31.0% in terms of BLEU@4 and METEOR, respectively. We also
demonstrate that LSTM-E is superior in predicting Subject-Verb-Object (SVO)
triplets to several state-of-the-art techniques.
A Novel Approach Towards Clustering Based Image Segmentation
Authors: Dibya Jyoti Bora, Anil Kumar Gupta
Date: 2015-06-04 00:00:00
Tasks: Clustering, Image Segmentation, Segmentation, Semantic Segmentation

In computer vision, image segmentation is always selected as a major research
topic by researchers. Due to its vital rule in image processing, there always
arises the need of a better image segmentation method. Clustering is an
unsupervised study with its application in almost every field of science and
engineering. Many researchers used clustering in image segmentation process.
But still there requires improvement of such approaches. In this paper, a novel
approach for clustering based image segmentation is proposed. Here, we give
importance on color space and choose lab for this task. The famous hard
clustering algorithm K-means is used, but as its performance is dependent on
choosing a proper distance measure, so, we go for cosine distance measure. Then
the segmented image is filtered with sobel filter. The filtered image is
analyzed with marker watershed algorithm to have the final segmented result of
our original image. The MSE and PSNR values are evaluated to observe the
performance.
Comparing the Performance of L*A*B* and HSV Color Spaces with Respect to Color Image Segmentation
Authors: Dibya Jyoti Bora, Anil Kumar Gupta, Fayaz Ahmad Khan
Date: 2015-06-04 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Color image segmentation is a very emerging topic for image processing
research. Since it has the ability to present the result in a way that is much
more close to the human yes perceive, so todays more research is going on this
area. Choosing a proper color space is a very important issue for color image
segmentation process. Generally LAB and HSV are the two frequently chosen color
spaces. In this paper a comparative analysis is performed between these two
color spaces with respect to color image segmentation. For measuring their
performance, we consider the parameters: mse and psnr . It is found that HSV
color space is performing better than LAB.
Robust Camera Location Estimation by Convex Programming
Authors: Onur Ozyesil, Amit Singer
Date: 2014-11-29 00:00:00
Proceeding: CVPR 2015 6

$3$D structure recovery from a collection of $2$D images requires the
estimation of the camera locations and orientations, i.e. the camera motion.
For large, irregular collections of images, existing methods for the location
estimation part, which can be formulated as the inverse problem of estimating
$n$ locations $\mathbf{t}_1, \mathbf{t}_2, \ldots, \mathbf{t}_n$ in
$\mathbb{R}^3$ from noisy measurements of a subset of the pairwise directions
$\frac{\mathbf{t}_i - \mathbf{t}_j}{\|\mathbf{t}_i - \mathbf{t}_j\|}$, are
sensitive to outliers in direction measurements. In this paper, we firstly
provide a complete characterization of well-posed instances of the location
estimation problem, by presenting its relation to the existing theory of
parallel rigidity. For robust estimation of camera locations, we introduce a
two-step approach, comprised of a pairwise direction estimation method robust
to outliers in point correspondences between image pairs, and a convex program
to maintain robustness to outlier directions. In the presence of partially
corrupted measurements, we empirically demonstrate that our convex formulation
can even recover the locations exactly. Lastly, we demonstrate the utility of
our formulations through experiments on Internet photo collections.
Higher-order Spatial Accuracy in Diffeomorphic Image Registration
Authors: Henry O. Jacobs, Stefan Sommer
Date: 2014-12-23 00:00:00
Tasks: Image Registration

We discretize a cost functional for image registration problems by deriving
Taylor expansions for the matching term. Minima of the discretized cost
functionals can be computed with no spatial discretization error, and the
optimal solutions are equivalent to minimal energy curves in the space of
$k$-jets. We show that the solutions convergence to optimal solutions of the
original cost functional as the number of particles increases with a
convergence rate of $O(h^{d+k})$ where $h$ is a resolution parameter. The
effect of this approach over traditional particle methods is illustrated on
synthetic examples and real images.
Implementation of Training Convolutional Neural Networks
Authors: Tianyi Liu, Shuangsang Fang, Yuehui Zhao, Peng Wang, Jun Zhang
Date: 2015-06-03 00:00:00
Tasks: BIG-bench Machine Learning, Face Recognition

Deep learning refers to the shining branch of machine learning that is based
on learning levels of representations. Convolutional Neural Networks (CNN) is
one kind of deep neural network. It can study concurrently. In this article, we
gave a detailed analysis of the process of CNN algorithm both the forward
process and back propagation. Then we applied the particular convolutional
neural network to implement the typical face recognition problem by java. Then,
a parallel strategy was proposed in section4. In addition, by measuring the
actual time of forward and backward computing, we analysed the maximal speed up
and parallel efficiency theoretically.
Recognition of Changes in SAR Images Based on Gauss-Log Ratio and MRFFCM
Authors: Jismy Alphonse, Biju V. G.
Date: 2015-06-03 00:00:00
Tasks: Change Detection, General Classification

A modified version of MRFFCM (Markov Random Field Fuzzy C means) based SAR
(Synthetic aperture Radar) image change detection method is proposed in this
paper. It involves three steps: Difference Image (DI) generation by using
Gauss-log ratio operator, speckle noise reduction by SRAD (Speckle Reducing
Anisotropic Diffusion), and the detection of changed regions by using MRFFCM.
The proposed method is compared with existing methods such as FCM and MRFFCM
using simulated and real SAR images. The measures used for evaluation includes
Overall Error (OE), Percentage Correct Classification (PCC), Kappa Coefficient
(KC), Root Mean Square Error (RMSE), and Peak Signal to Noise Ratio (PSNR). The
results show that the proposed method is better compared to FCM and MRFFCM
based change detection method.
A Nearly Optimal and Agnostic Algorithm for Properly Learning a Mixture of k Gaussians, for any Constant k
Authors: Jerry Li, Ludwig Schmidt
Date: 2015-06-03 00:00:00
Tasks: Learning Theory

Learning a Gaussian mixture model (GMM) is a fundamental problem in machine
learning, learning theory, and statistics. One notion of learning a GMM is
proper learning: here, the goal is to find a mixture of $k$ Gaussians
$\mathcal{M}$ that is close to the density $f$ of the unknown distribution from
which we draw samples. The distance between $\mathcal{M}$ and $f$ is typically
measured in the total variation or $L_1$-norm.
  We give an algorithm for learning a mixture of $k$ univariate Gaussians that
is nearly optimal for any fixed $k$. The sample complexity of our algorithm is
$\tilde{O}(\frac{k}{\epsilon^2})$ and the running time is $(k \cdot
\log\frac{1}{\epsilon})^{O(k^4)} + \tilde{O}(\frac{k}{\epsilon^2})$. It is
well-known that this sample complexity is optimal (up to logarithmic factors),
and it was already achieved by prior work. However, the best known time
complexity for proper learning a $k$-GMM was
$\tilde{O}(\frac{1}{\epsilon^{3k-1}})$. In particular, the dependence between
$\frac{1}{\epsilon}$ and $k$ was exponential. We significantly improve this
dependence by replacing the $\frac{1}{\epsilon}$ term with a $\log
\frac{1}{\epsilon}$ while only increasing the exponent moderately. Hence, for
any fixed $k$, the $\tilde{O} (\frac{k}{\epsilon^2})$ term dominates our
running time, and thus our algorithm runs in time which is nearly-linear in the
number of samples drawn. Achieving a running time of $\textrm{poly}(k,
\frac{1}{\epsilon})$ for proper learning of $k$-GMMs has recently been stated
as an open problem by multiple researchers, and we make progress on this
question.
  Moreover, our approach offers an agnostic learning guarantee: our algorithm
returns a good GMM even if the distribution we are sampling from is not a
mixture of Gaussians. To the best of our knowledge, our algorithm is the first
agnostic proper learning algorithm for GMMs.
Optimal high-level descriptions of dynamical systems
Authors: David H. Wolpert, Joshua A. Grochow, Eric Libby, Simon DeDeo
Date: 2014-09-25 00:00:00
Tasks: Vocal Bursts Intensity Prediction

To analyze high-dimensional systems, many fields in science and engineering
rely on high-level descriptions, sometimes called "macrostates,"
"coarse-grainings," or "effective theories". Examples of such descriptions
include the thermodynamic properties of a large collection of point particles
undergoing reversible dynamics, the variables in a macroeconomic model
describing the individuals that participate in an economy, and the summary
state of a cell composed of a large set of biochemical networks.
  Often these high-level descriptions are constructed without considering the
ultimate reason for needing them in the first place. Here, we formalize and
quantify one such purpose: the need to predict observables of interest
concerning the high-dimensional system with as high accuracy as possible, while
minimizing the computational cost of doing so. The resulting State Space
Compression (SSC) framework provides a guide for how to solve for the {optimal}
high-level description of a given dynamical system, rather than constructing it
based on human intuition alone.
  In this preliminary report, we introduce SSC, and illustrate it with several
information-theoretic quantifications of "accuracy", all with different
implications for the optimal compression. We also discuss some other possible
applications of SSC beyond the goal of accurate prediction. These include SSC
as a measure of the complexity of a dynamical system, and as a way to quantify
information flow between the scales of a system.
Understanding Random Forests: From Theory to Practice
Authors: Gilles Louppe
Date: 2014-07-28 00:00:00

Data analysis and machine learning have become an integrative part of the
modern scientific methodology, offering automated procedures for the prediction
of a phenomenon based on past observations, unraveling underlying patterns in
data and providing insights about the problem. Yet, caution should avoid using
machine learning as a black-box tool, but rather consider it as a methodology,
with a rational thought process that is entirely dependent on the problem under
study. In particular, the use of algorithms should ideally require a reasonable
understanding of their mechanisms, properties and limitations, in order to
better apprehend and interpret their results.
  Accordingly, the goal of this thesis is to provide an in-depth analysis of
random forests, consistently calling into question each and every part of the
algorithm, in order to shed new light on its learning capabilities, inner
workings and interpretability. The first part of this work studies the
induction of decision trees and the construction of ensembles of randomized
trees, motivating their design and purpose whenever possible. Our contributions
follow with an original complexity analysis of random forests, showing their
good computational performance and scalability, along with an in-depth
discussion of their implementation details, as contributed within Scikit-Learn.
  In the second part of this work, we analyse and discuss the interpretability
of random forests in the eyes of variable importance measures. The core of our
contributions rests in the theoretical characterization of the Mean Decrease of
Impurity variable importance measure, from which we prove and derive some of
its properties in the case of multiway totally randomized trees and in
asymptotic conditions. In consequence of this work, our analysis demonstrates
that variable importances [...].
Celeste: Variational inference for a generative model of astronomical images
Authors: Jeffrey Regier, Andrew Miller, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, Prabhat
Date: 2015-06-03 00:00:00
Tasks: Variational Inference

We present a new, fully generative model of optical telescope image sets,
along with a variational procedure for inference. Each pixel intensity is
treated as a Poisson random variable, with a rate parameter dependent on latent
properties of stars and galaxies. Key latent properties are themselves random,
with scientific prior distributions constructed from large ancillary data sets.
We check our approach on synthetic images. We also run it on images from a
major sky survey, where it exceeds the performance of the current
state-of-the-art method for locating celestial bodies and measuring their
colors.
Bayesian optimization for materials design
Authors: Peter I. Frazier, Jialei Wang
Date: 2015-06-03 00:00:00
Tasks: Bayesian Optimization, regression

We introduce Bayesian optimization, a technique developed for optimizing
time-consuming engineering simulations and for fitting machine learning models
on large datasets. Bayesian optimization guides the choice of experiments
during materials design and discovery to find good material designs in as few
experiments as possible. We focus on the case when materials designs are
parameterized by a low-dimensional vector. Bayesian optimization is built on a
statistical technique called Gaussian process regression, which allows
predicting the performance of a new design based on previously tested designs.
After providing a detailed introduction to Gaussian process regression, we
introduce two Bayesian optimization methods: expected improvement, for design
problems with noise-free evaluations; and the knowledge-gradient method, which
generalizes expected improvement and may be used in design problems with noisy
evaluations. Both methods are derived using a value-of-information analysis,
and enjoy one-step Bayes-optimality.
Unsupervised Feature Analysis with Class Margin Optimization
Authors: Sen Wang, Feiping Nie, Xiaojun Chang, Lina Yao, Xue Li, Quan Z. Sheng
Date: 2015-06-03 00:00:00
Tasks: Clustering, Feature Correlation, feature selection

Unsupervised feature selection has been always attracting research attention
in the communities of machine learning and data mining for decades. In this
paper, we propose an unsupervised feature selection method seeking a feature
coefficient matrix to select the most distinctive features. Specifically, our
proposed algorithm integrates the Maximum Margin Criterion with a
sparsity-based model into a joint framework, where the class margin and feature
correlation are taken into account at the same time. To maximize the total data
separability while preserving minimized within-class scatter simultaneously, we
propose to embed Kmeans into the framework generating pseudo class label
information in a scenario of unsupervised feature selection. Meanwhile, a
sparsity-based model, ` 2 ,p-norm, is imposed to the regularization term to
effectively discover the sparse structures of the feature coefficient matrix.
In this way, noisy and irrelevant features are removed by ruling out those
features whose corresponding coefficients are zeros. To alleviate the local
optimum problem that is caused by random initializations of K-means, a
convergence guaranteed algorithm with an updating strategy for the clustering
indicator matrix, is proposed to iteractively chase the optimal solution.
Performance evaluation is extensively conducted over six benchmark data sets.
From plenty of experimental results, it is demonstrated that our method has
superior performance against all other compared approaches.
Probabilistic Numerics and Uncertainty in Computations
Authors: Philipp Hennig, Michael A. Osborne, Mark Girolami
Date: 2015-06-03 00:00:00
Tasks: Management

We deliver a call to arms for probabilistic numerical methods: algorithms for
numerical tasks, including linear algebra, integration, optimization and
solving differential equations, that return uncertainties in their
calculations. Such uncertainties, arising from the loss of precision induced by
numerical calculation with limited time or hardware, are important for much
contemporary science and industry. Within applications such as climate science
and astrophysics, the need to make decisions on the basis of computations with
large and complex data has led to a renewed focus on the management of
numerical uncertainty. We describe how several seminal classic numerical
methods can be interpreted naturally as probabilistic inference. We then show
that the probabilistic view suggests new algorithms that can flexibly be
adapted to suit application specifics, while delivering improved empirical
performance. We provide concrete illustrations of the benefits of probabilistic
numeric algorithms on real scientific problems from astrometry and astronomical
imaging, while highlighting open problems with these new algorithms. Finally,
we describe how probabilistic numerical methods provide a coherent framework
for identifying the uncertainty in calculations performed with a combination of
numerical algorithms (e.g. both numerical optimisers and differential equation
solvers), potentially allowing the diagnosis (and control) of error sources in
computations.
Using PCA to Efficiently Represent State Spaces
Authors: William Curran, Tim Brys, Matthew Taylor, William Smart
Date: 2015-05-02 00:00:00
Tasks: Benchmarking, Dimensionality Reduction, Reinforcement Learning

Reinforcement learning algorithms need to deal with the exponential growth of
states and actions when exploring optimal control in high-dimensional spaces.
This is known as the curse of dimensionality. By projecting the agent's state
onto a low-dimensional manifold, we can represent the state space in a smaller
and more efficient representation. By using this representation during
learning, the agent can converge to a good policy much faster. We test this
approach in the Mario Benchmarking Domain. When using dimensionality reduction
in Mario, learning converges much faster to a good policy. But, there is a
critical convergence-performance trade-off. By projecting onto a
low-dimensional manifold, we are ignoring important data. In this paper, we
explore this trade-off of convergence and performance. We find that learning in
as few as 4 dimensions (instead of 9), we can improve performance past learning
in the full dimensional space at a faster convergence rate.