pwc-archive/papers-with-abstracts huggingface dataset:
Spike and Slab Gaussian Process Latent Variable Models
Authors: Zhenwen Dai, James Hensman, Neil Lawrence
Date: 2015-05-10 00:00:00
Tasks: Dimensionality Reduction, Gaussian Processes, Retrieval, Variational Inference

The Gaussian process latent variable model (GP-LVM) is a popular approach to
non-linear probabilistic dimensionality reduction. One design choice for the
model is the number of latent variables. We present a spike and slab prior for
the GP-LVM and propose an efficient variational inference procedure that gives
a lower bound of the log marginal likelihood. The new model provides a more
principled approach for selecting latent dimensions than the standard way of
thresholding the length-scale parameters. The effectiveness of our approach is
demonstrated through experiments on real and simulated data. Further, we extend
multi-view Gaussian processes that rely on sharing latent dimensions (known as
manifold relevance determination) with spike and slab priors. This allows a
more principled approach for selecting a subset of the latent space for each
view of data. The extended model outperforms the previous state-of-the-art when
applied to a cross-modal multimedia retrieval task.
Fast Rhetorical Structure Theory Discourse Parsing
Authors: Michael Heilman, Kenji Sagae
Date: 2015-05-10 00:00:00
Tasks: Articles, Discourse Parsing

In recent years, There has been a variety of research on discourse parsing,
particularly RST discourse parsing. Most of the recent work on RST parsing has
focused on implementing new types of features or learning algorithms in order
to improve accuracy, with relatively little focus on efficiency, robustness, or
practical use. Also, most implementations are not widely available. Here, we
describe an RST segmentation and parsing system that adapts models and feature
sets from various previous work, as described below. Its accuracy is near
state-of-the-art, and it was developed to be fast, robust, and practical. For
example, it can process short documents such as news articles or essays in less
than a second.
DistMS: A Non-Portfolio Distributed Solver for Maximum Satisfiability
Authors: Miguel Neves, Inês Lynce, Vasco Manquinho
Date: 2015-05-10 00:00:00

The most successful parallel SAT and MaxSAT solvers follow a portfolio
approach, where each thread applies a different algorithm (or the same
algorithm configured differently) to solve a given problem instance. The main
goal of building a portfolio is to diversify the search process being carried
out by each thread. As soon as one thread finishes, the instance can be deemed
solved. In this paper we present a new open source distributed solver for
MaxSAT solving that addresses two issues commonly found in multicore parallel
solvers, namely memory contention and scalability. Preliminary results show
that our non-portfolio distributed MaxSAT solver outperforms its sequential
version and is able to solve more instances as the number of processes
increases.
Exploiting Resolution-based Representations for MaxSAT Solving
Authors: Miguel Neves, Ruben Martins, Mikoláš Janota, Inês Lynce, Vasco Manquinho
Date: 2015-05-10 00:00:00

Most recent MaxSAT algorithms rely on a succession of calls to a SAT solver
in order to find an optimal solution. In particular, several algorithms take
advantage of the ability of SAT solvers to identify unsatisfiable subformulas.
Usually, these MaxSAT algorithms perform better when small unsatisfiable
subformulas are found early. However, this is not the case in many problem
instances, since the whole formula is given to the SAT solver in each call. In
this paper, we propose to partition the MaxSAT formula using a resolution-based
graph representation. Partitions are then iteratively joined by using a
proximity measure extracted from the graph representation of the formula. The
algorithm ends when only one partition remains and the optimal solution is
found. Experimental results show that this new approach further enhances a
state of the art MaxSAT solver to optimally solve a larger set of industrial
problem instances.
Bounded-Distortion Metric Learning
Authors: Renjie Liao, Jianping Shi, Ziyang Ma, Jun Zhu, Jiaya Jia
Date: 2015-05-10 00:00:00
Tasks: Clustering, General Classification, Metric Learning

Metric learning aims to embed one metric space into another to benefit tasks
like classification and clustering. Although a greatly distorted metric space
has a high degree of freedom to fit training data, it is prone to overfitting
and numerical inaccuracy. This paper presents {\it bounded-distortion metric
learning} (BDML), a new metric learning framework which amounts to finding an
optimal Mahalanobis metric space with a bounded-distortion constraint. An
efficient solver based on the multiplicative weights update method is proposed.
Moreover, we generalize BDML to pseudo-metric learning and devise the
semidefinite relaxation and a randomized algorithm to approximately solve it.
We further provide theoretical analysis to show that distortion is a key
ingredient for stability and generalization ability of our BDML algorithm.
Extensive experiments on several benchmark datasets yield promising results.
Optimization via Low-rank Approximation for Community Detection in Networks
Authors: Can M. Le, Elizaveta Levina, Roman Vershynin
Date: 2014-05-31 00:00:00
Tasks: Community Detection

Community detection is one of the fundamental problems of network analysis,
for which a number of methods have been proposed. Most model-based or
criteria-based methods have to solve an optimization problem over a discrete
set of labels to find communities, which is computationally infeasible. Some
fast spectral algorithms have been proposed for specific methods or models, but
only on a case-by-case basis. Here we propose a general approach for maximizing
a function of a network adjacency matrix over discrete labels by projecting the
set of labels onto a subspace approximating the leading eigenvectors of the
expected adjacency matrix. This projection onto a low-dimensional space makes
the feasible set of labels much smaller and the optimization problem much
easier. We prove a general result about this method and show how to apply it to
several previously proposed community detection criteria, establishing its
consistency for label estimation in each case and demonstrating the fundamental
connection between spectral properties of the network and various model-based
approaches to community detection. Simulations and applications to real-world
data are included to demonstrate our method performs well for multiple problems
over a wide range of parameters.
Bayesian Sparse Tucker Models for Dimension Reduction and Tensor Completion
Authors: Qibin Zhao, Liqing Zhang, Andrzej Cichocki
Date: 2015-05-10 00:00:00
Tasks: Compressive Sensing, Dimensionality Reduction, Tensor Decomposition

Tucker decomposition is the cornerstone of modern machine learning on
tensorial data analysis, which have attracted considerable attention for
multiway feature extraction, compressive sensing, and tensor completion. The
most challenging problem is related to determination of model complexity (i.e.,
multilinear rank), especially when noise and missing data are present. In
addition, existing methods cannot take into account uncertainty information of
latent factors, resulting in low generalization performance. To address these
issues, we present a class of probabilistic generative Tucker models for tensor
decomposition and completion with structural sparsity over multilinear latent
space. To exploit structural sparse modeling, we introduce two group sparsity
inducing priors by hierarchial representation of Laplace and Student-t
distributions, which facilitates fully posterior inference. For model learning,
we derived variational Bayesian inferences over all model (hyper)parameters,
and developed efficient and scalable algorithms based on multilinear
operations. Our methods can automatically adapt model complexity and infer an
optimal multilinear rank by the principle of maximum lower bound of model
evidence. Experimental results and comparisons on synthetic, chemometrics and
neuroimaging data demonstrate remarkable performance of our models for
recovering ground-truth of multilinear rank and missing entries.
Relations Between Adjacency and Modularity Graph Partitioning
Authors: Hansi Jiang, Carl Meyer
Date: 2015-05-09 00:00:00
Tasks: Clustering, graph partitioning

This paper develops the exact linear relationship between the leading eigenvector of the unnormalized modularity matrix and the eigenvectors of the adjacency matrix. We propose a method for approximating the leading eigenvector of the modularity matrix, and we derive the error of the approximation. There is also a complete proof of the equivalence between normalized adjacency clustering and normalized modularity clustering. Numerical experiments show that normalized adjacency clustering can be as twice efficient as normalized modularity clustering.
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes
Authors: Yves-Laurent Kom Samo, Stephen Roberts
Date: 2014-10-24 00:00:00
Tasks: Bayesian Inference, Gaussian Processes, Point Processes

In this paper we propose the first non-parametric Bayesian model using
Gaussian Processes to make inference on Poisson Point Processes without
resorting to gridding the domain or to introducing latent thinning points.
Unlike competing models that scale cubically and have a squared memory
requirement in the number of data points, our model has a linear complexity and
memory requirement. We propose an MCMC sampler and show that our model is
faster, more accurate and generates less correlated samples than competing
models on both synthetic and real-life data. Finally, we show that our model
easily handles data sizes not considered thus far by alternate approaches.
Should we really use post-hoc tests based on mean-ranks?
Authors: Alessio Benavoli, Giorgio Corani, Francesca Mangili
Date: 2015-05-09 00:00:00
Tasks: BIG-bench Machine Learning

The statistical comparison of multiple algorithms over multiple data sets is
fundamental in machine learning. This is typically carried out by the Friedman
test. When the Friedman test rejects the null hypothesis, multiple comparisons
are carried out to establish which are the significant differences among
algorithms. The multiple comparisons are usually performed using the mean-ranks
test. The aim of this technical note is to discuss the inconsistencies of the
mean-ranks post-hoc test with the goal of discouraging its use in machine
learning as well as in medicine, psychology, etc.. We show that the outcome of
the mean-ranks test depends on the pool of algorithms originally included in
the experiment. In other words, the outcome of the comparison between
algorithms A and B depends also on the performance of the other algorithms
included in the original experiment. This can lead to paradoxical situations.
For instance the difference between A and B could be declared significant if
the pool comprises algorithms C, D, E and not significant if the pool comprises
algorithms F, G, H. To overcome these issues, we suggest instead to perform the
multiple comparison using a test whose outcome only depends on the two
algorithms being compared, such as the sign-test or the Wilcoxon signed-rank
test.
Subset Feature Learning for Fine-Grained Category Classification
Authors: Zongyuan Ge, Christopher Mccool, Conrad Sanderson, Peter Corke
Date: 2015-05-09 00:00:00
Tasks: Classification, General Classification, Transfer Learning

Fine-grained categorisation has been a challenging problem due to small
inter-class variation, large intra-class variation and low number of training
images. We propose a learning system which first clusters visually similar
classes and then learns deep convolutional neural network features specific to
each subset. Experiments on the popular fine-grained Caltech-UCSD bird dataset
show that the proposed method outperforms recent fine-grained categorisation
methods under the most difficult setting: no bounding boxes are presented at
test time. It achieves a mean accuracy of 77.5%, compared to the previous best
performance of 73.2%. We also show that progressive transfer learning allows us
to first learn domain-generic features (for bird classification) which can then
be adapted to specific set of bird classes, yielding improvements in accuracy.
Probabilistic Cascading for Large Scale Hierarchical Classification
Authors: Aris Kosmopoulos, Georgios Paliouras, Ion Androutsopoulos
Date: 2015-05-09 00:00:00
Tasks: Classification, General Classification

Hierarchies are frequently used for the organization of objects. Given a
hierarchy of classes, two main approaches are used, to automatically classify
new instances: flat classification and cascade classification. Flat
classification ignores the hierarchy, while cascade classification greedily
traverses the hierarchy from the root to the predicted leaf. In this paper we
propose a new approach, which extends cascade classification to predict the
right leaf by estimating the probability of each root-to-leaf path. We provide
experimental results which indicate that, using the same classification
algorithm, one can achieve better results with our approach, compared to the
traditional flat and cascade classifications.
Newton Sketch: A Linear-time Optimization Algorithm with Linear-Quadratic Convergence
Authors: Mert Pilanci, Martin J. Wainwright
Date: 2015-05-09 00:00:00

We propose a randomized second-order method for optimization known as the
Newton Sketch: it is based on performing an approximate Newton step using a
randomly projected or sub-sampled Hessian. For self-concordant functions, we
prove that the algorithm has super-linear convergence with exponentially high
probability, with convergence and complexity guarantees that are independent of
condition numbers and related problem-dependent quantities. Given a suitable
initialization, similar guarantees also hold for strongly convex and smooth
objectives without self-concordance. When implemented using randomized
projections based on a sub-sampled Hadamard basis, the algorithm typically has
substantially lower complexity than Newton's method. We also describe
extensions of our methods to programs involving convex constraints that are
equipped with self-concordant barriers. We discuss and illustrate applications
to linear programs, quadratic programs with convex constraints, logistic
regression and other generalized linear models, as well as semidefinite
programs.
Performance Evaluation of Vision-Based Algorithms for MAVs
Authors: T. Holzmann, R. Prettenthaler, J. Pestana, D. Muschick, G. Graber, C. Mostegel, F. Fraundorfer, H. Bischof
Date: 2015-05-09 00:00:00
Tasks: State Estimation

An important focus of current research in the field of Micro Aerial Vehicles
(MAVs) is to increase the safety of their operation in general unstructured
environments. Especially indoors, where GPS cannot be used for localization,
reliable algorithms for localization and mapping of the environment are
necessary in order to keep an MAV airborne safely. In this paper, we compare
vision-based real-time capable methods for localization and mapping and point
out their strengths and weaknesses. Additionally, we describe algorithms for
state estimation, control and navigation, which use the localization and
mapping results of our vision-based algorithms as input.
Machine Learning Techniques for Intrusion Detection
Authors: Mahdi Zamani, Mahnush Movahedi
Date: 2013-12-08 00:00:00
Tasks: BIG-bench Machine Learning, Intrusion Detection

An Intrusion Detection System (IDS) is a software that monitors a single or a
network of computers for malicious activities (attacks) that are aimed at
stealing or censoring information or corrupting network protocols. Most
techniques used in today's IDS are not able to deal with the dynamic and
complex nature of cyber attacks on computer networks. Hence, efficient adaptive
methods like various techniques of machine learning can result in higher
detection rates, lower false alarm rates and reasonable computation and
communication costs. In this paper, we study several such schemes and compare
their performance. We divide the schemes into methods based on classical
artificial intelligence (AI) and methods based on computational intelligence
(CI). We explain how various characteristics of CI techniques can be used to
build efficient IDS.
Effects of Nonparanormal Transform on PC and GES Search Accuracies
Authors: Joseph D. Ramsey
Date: 2015-05-07 00:00:00

Liu, et al., 2009 developed a transformation of a class of non-Gaussian
univariate distributions into Gaussian distributions. Liu and collaborators
(2012) subsequently applied the transform to search for graphical causal models
for a number of empirical data sets. To our knowledge, there has been no
published investigation by simulation of the conditions under which the
transform aids, or harms, standard graphical model search procedures. We
consider here how the transform affects the performance of two search
algorithms in particular, PC (Spirtes et al., 2000; Meek 1995) and GES (Meek
1997; Chickering 2002). We find that the transform is harmless but ineffective
for most cases but quite effective in very special cases for GES, namely, for
moderate non-Gaussianity and moderate non-linearity. For strong-linearity,
another algorithm, PC-GES (a combination of PC with GES), is equally effective.
Bilevel approaches for learning of variational imaging models
Authors: Luca Calatroni, Cao Chung, Juan Carlos De Los Reyes, Carola-Bibiane Schönlieb, Tuomo Valkonen
Date: 2015-05-08 00:00:00

We review some recent learning approaches in variational imaging, based on
bilevel optimisation, and emphasize the importance of their treatment in
function space. The paper covers both analytical and numerical techniques.
Analytically, we include results on the existence and structure of minimisers,
as well as optimality conditions for their characterisation. Based on this
information, Newton type methods are studied for the solution of the problems
at hand, combining them with sampling techniques in case of large databases.
The computational verification of the developed techniques is extensively
documented, covering instances with different type of regularisers, several
noise models, spatially dependent weights and large image databases.
Short Portfolio Training for CSP Solving
Authors: Mirko Stojadinović, Mladen Nikolić, Filip Marić
Date: 2015-05-08 00:00:00

Many different approaches for solving Constraint Satisfaction Problems (CSPs)
and related Constraint Optimization Problems (COPs) exist. However, there is no
single solver (nor approach) that performs well on all classes of problems and
many portfolio approaches for selecting a suitable solver based on simple
syntactic features of the input CSP instance have been developed. In this paper
we first present a simple portfolio method for CSP based on k-nearest neighbors
method. Then, we propose a new way of using portfolio systems --- training them
shortly in the exploitation time, specifically for the set of instances to be
solved and using them on that set. Thorough evaluation has been performed and
has shown that the approach yields good results. We evaluated several machine
learning techniques for our portfolio. Due to its simplicity and efficiency,
the selected k-nearest neighbors method is especially suited for our short
training approach and it also yields the best results among the tested methods.
We also confirm that our approach yields good results on SAT domain.
Deep Learning for Medical Image Segmentation
Authors: Matthew Lai
Date: 2015-05-08 00:00:00
Tasks: Deep Learning, Hippocampus, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

This report provides an overview of the current state of the art deep
learning architectures and optimisation techniques, and uses the ADNI
hippocampus MRI dataset as an example to compare the effectiveness and
efficiency of different convolutional architectures on the task of patch-based
3-dimensional hippocampal segmentation, which is important in the diagnosis of
Alzheimer's Disease. We found that a slightly unconventional "stacked 2D"
approach provides much better classification performance than simple 2D patches
without requiring significantly more computational power. We also examined the
popular "tri-planar" approach used in some recently published studies, and
found that it provides much better results than the 2D approaches, but also
with a moderate increase in computational power requirement. Finally, we
evaluated a full 3D convolutional architecture, and found that it provides
marginally better results than the tri-planar approach, but at the cost of a
very significant increase in computational power requirement.
Evolving Boolean Networks with RNA Editing
Authors: Larry Bull
Date: 2015-05-08 00:00:00

The editing of transcribed RNA by other molecules such that the form of the
final product differs from that specified in the corresponding DNA sequence is
ubiquitous. This paper uses an abstract, tunable Boolean genetic regulatory
network model to explore aspects of RNA editing. In particular, it is shown how
dynamically altering expressed sequences via a guide RNA-inspired mechanism can
be selected for by simulated evolution under various single and multicellular
scenarios.
The structure of optimal parameters for image restoration problems
Authors: Juan Carlos De Los Reyes, Carola-Bibiane Schönlieb, Tuomo Valkonen
Date: 2015-05-08 00:00:00
Tasks: Image Restoration

We study the qualitative properties of optimal regularisation parameters in
variational models for image restoration. The parameters are solutions of
bilevel optimisation problems with the image restoration problem as constraint.
A general type of regulariser is considered, which encompasses total variation
(TV), total generalized variation (TGV) and infimal-convolution total variation
(ICTV). We prove that under certain conditions on the given data optimal
parameters derived by bilevel optimisation problems exist. A crucial point in
the existence proof turns out to be the boundedness of the optimal parameters
away from $0$ which we prove in this paper. The analysis is done on the
original -- in image restoration typically non-smooth variational problem -- as
well as on a smoothed approximation set in Hilbert space which is the one
considered in numerical computations. For the smoothed bilevel problem we also
prove that it $\Gamma$ converges to the original problem as the smoothing
vanishes. All analysis is done in function spaces rather than on the
discretised learning problem.
Shadow Optimization from Structured Deep Edge Detection
Authors: Li Shen, Teck Wee Chua, Karianto Leman
Date: 2015-05-07 00:00:00
Proceeding: CVPR 2015 6
Tasks: Edge Detection, Shadow Detection

Local structures of shadow boundaries as well as complex interactions of
image regions remain largely unexploited by previous shadow detection
approaches. In this paper, we present a novel learning-based framework for
shadow region recovery from a single image. We exploit the local structures of
shadow edges by using a structured CNN learning framework. We show that using
the structured label information in the classification can improve the local
consistency of the results and avoid spurious labelling. We further propose and
formulate a shadow/bright measure to model the complex interactions among image
regions. The shadow and bright measures of each patch are computed from the
shadow edges detected in the image. Using the global interaction constraints on
patches, we formulate a least-square optimization problem for shadow recovery
that can be solved efficiently. Our shadow recovery method achieves
state-of-the-art results on the major shadow benchmark databases collected
under various conditions.