pwc-archive/papers-with-abstracts huggingface dataset:
Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix Completion
Authors: Zheng Wang, Ming-Jun Lai, Zhaosong Lu, Wei Fan, Hasan Davulcu, Jieping Ye
Date: 2014-04-04 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion

In this paper, we propose an efficient and scalable low rank matrix
completion algorithm. The key idea is to extend orthogonal matching pursuit
method from the vector case to the matrix case. We further propose an economic
version of our algorithm by introducing a novel weight updating rule to reduce
the time and storage complexity. Both versions are computationally inexpensive
for each matrix pursuit iteration, and find satisfactory results in a few
iterations. Another advantage of our proposed algorithm is that it has only one
tunable parameter, which is the rank. It is easy to understand and to use by
the user. This becomes especially important in large-scale learning problems.
In addition, we rigorously show that both versions achieve a linear convergence
rate, which is significantly better than the previous known results. We also
empirically compare the proposed algorithms with several state-of-the-art
matrix completion algorithms on many real-world datasets, including the
large-scale recommendation dataset Netflix as well as the MovieLens datasets.
Numerical results show that our proposed algorithm is more efficient than
competing algorithms while achieving similar or better prediction performance.
Open Question Answering with Weakly Supervised Embedding Models
Authors: Antoine Bordes, Jason Weston, Nicolas Usunier
Date: 2014-04-16 00:00:00
Tasks: Open-Ended Question Answering, Question Answering

Building computers able to answer questions on any subject is a long standing
goal of artificial intelligence. Promising progress has recently been achieved
by methods that learn to map questions to logical forms or database queries.
Such approaches can be effective but at the cost of either large amounts of
human-labeled data or by defining lexicons and grammars tailored by
practitioners. In this paper, we instead take the radical approach of learning
to map questions to vectorial feature representations. By mapping answers into
the same space one can query any knowledge base independent of its schema,
without requiring any grammar or lexicon. Our method is trained with a new
optimization procedure combining stochastic gradient descent followed by a
fine-tuning step using the weak supervision provided by blending automatically
and collaboratively generated resources. We empirically demonstrate that our
model can capture meaningful signals from its noisy supervision leading to
major improvements over paralex, the only existing method able to be trained on
similar weakly labeled data.
Generic Object Detection With Dense Neural Patterns and Regionlets
Authors: Will Y. Zou, Xiaoyu Wang, Miao Sun, Yuanqing Lin
Date: 2014-04-16 00:00:00
Tasks: Object, object-detection, Object Detection

This paper addresses the challenge of establishing a bridge between deep
convolutional neural networks and conventional object detection frameworks for
accurate and efficient generic object detection. We introduce Dense Neural
Patterns, short for DNPs, which are dense local features derived from
discriminatively trained deep convolutional neural networks. DNPs can be easily
plugged into conventional detection frameworks in the same way as other dense
local features(like HOG or LBP). The effectiveness of the proposed approach is
demonstrated with the Regionlets object detection framework. It achieved 46.1%
mean average precision on the PASCAL VOC 2007 dataset, and 44.1% on the PASCAL
VOC 2010 dataset, which dramatically improves the original Regionlets approach
without DNPs.
An Empirical Comparison of Parsing Methods for Stanford Dependencies
Authors: Lingpeng Kong, Noah A. Smith
Date: 2014-04-16 00:00:00
Tasks: Dependency Parsing

Stanford typed dependencies are a widely desired representation of natural
language sentences, but parsing is one of the major computational bottlenecks
in text analysis systems. In light of the evolving definition of the Stanford
dependencies and developments in statistical dependency parsing algorithms,
this paper revisits the question of Cer et al. (2010): what is the tradeoff
between accuracy and speed in obtaining Stanford dependencies in particular? We
also explore the effects of input representations on this tradeoff:
part-of-speech tags, the novel use of an alternative dependency representation
as input, and distributional representaions of words. We find that direct
dependency parsing is a more viable solution than it was found to be in the
past. An accompanying software release can be found at:
http://www.ark.cs.cmu.edu/TBSD
Automated Classification of Airborne Laser Scanning Point Clouds
Authors: Christoph Waldhauser, Ronald Hochreiter, Johannes Otepka, Norbert Pfeifer, Sajid Ghuffar, Karolina Korzeniowska, Gerald Wagner
Date: 2014-04-16 00:00:00
Tasks: Classification, General Classification

Making sense of the physical world has always been at the core of mapping. Up
until recently, this has always dependent on using the human eye. Using
airborne lasers, it has become possible to quickly "see" more of the world in
many more dimensions. The resulting enormous point clouds serve as data sources
for applications far beyond the original mapping purposes ranging from flooding
protection and forestry to threat mitigation. In order to process these large
quantities of data, novel methods are required. In this contribution, we
develop models to automatically classify ground cover and soil types. Using the
logic of machine learning, we critically review the advantages of supervised
and unsupervised methods. Focusing on decision trees, we improve accuracy by
including beam vector components and using a genetic algorithm. We find that
our approach delivers consistently high quality classifications, surpassing
classical methods.
Communication Communities in MOOCs
Authors: Nabeel Gillani, Rebecca Eynon, Michael Osborne, Isis Hjorth, Stephen Roberts
Date: 2014-03-18 00:00:00

Massive Open Online Courses (MOOCs) bring together thousands of people from
different geographies and demographic backgrounds -- but to date, little is
known about how they learn or communicate. We introduce a new content-analysed
MOOC dataset and use Bayesian Non-negative Matrix Factorization (BNMF) to
extract communities of learners based on the nature of their online forum
posts. We see that BNMF yields a superior probabilistic generative model for
online discussions when compared to other models, and that the communities it
learns are differentiated by their composite students' demographic and course
performance indicators. These findings suggest that computationally efficient
probabilistic generative modelling of MOOCs can reveal important insights for
educational researchers and practitioners and help to develop more intelligent
and responsive online learning environments.
MEG Decoding Across Subjects
Authors: Emanuele Olivetti, Seyed Mostafa Kia, Paolo Avesani
Date: 2014-04-16 00:00:00
Tasks: Brain Decoding, Ensemble Learning, Transfer Learning

Brain decoding is a data analysis paradigm for neuroimaging experiments that
is based on predicting the stimulus presented to the subject from the
concurrent brain activity. In order to make inference at the group level, a
straightforward but sometimes unsuccessful approach is to train a classifier on
the trials of a group of subjects and then to test it on unseen trials from new
subjects. The extreme difficulty is related to the structural and functional
variability across the subjects. We call this approach "decoding across
subjects". In this work, we address the problem of decoding across subjects for
magnetoencephalographic (MEG) experiments and we provide the following
contributions: first, we formally describe the problem and show that it belongs
to a machine learning sub-field called transductive transfer learning (TTL).
Second, we propose to use a simple TTL technique that accounts for the
differences between train data and test data. Third, we propose the use of
ensemble learning, and specifically of stacked generalization, to address the
variability across subjects within train data, with the aim of producing more
stable classifiers. On a face vs. scramble task MEG dataset of 16 subjects, we
compare the standard approach of not modelling the differences across subjects,
to the proposed one of combining TTL and ensemble learning. We show that the
proposed approach is consistently more accurate than the standard one.
Dropout Training for Support Vector Machines
Authors: Ning Chen, Jun Zhu, Jianfei Chen, Bo Zhang
Date: 2014-04-16 00:00:00
Tasks: Data Augmentation

Dropout and other feature noising schemes have shown promising results in
controlling over-fitting by artificially corrupting the training data. Though
extensive theoretical and empirical studies have been performed for generalized
linear models, little work has been done for support vector machines (SVMs),
one of the most successful approaches for supervised learning. This paper
presents dropout training for linear SVMs. To deal with the intractable
expectation of the non-smooth hinge loss under corrupting distributions, we
develop an iteratively re-weighted least square (IRLS) algorithm by exploring
data augmentation techniques. Our algorithm iteratively minimizes the
expectation of a re-weighted least square problem, where the re-weights have
closed-form solutions. The similar ideas are applied to develop a new IRLS
algorithm for the expected logistic loss under corrupting distributions. Our
algorithms offer insights on the connection and difference between the hinge
loss and logistic loss in dropout training. Empirical results on several real
datasets demonstrate the effectiveness of dropout training on significantly
boosting the classification accuracy of linear SVMs.
Recover Canonical-View Faces in the Wild with Deep Neural Networks
Authors: Zhenyao Zhu, Ping Luo, Xiaogang Wang, Xiaoou Tang
Date: 2014-04-14 00:00:00
Tasks: Face Reconstruction, Face Verification

Face images in the wild undergo large intra-personal variations, such as poses, illuminations, occlusions, and low resolutions, which cause great challenges to face-related applications. This paper addresses this challenge by proposing a new deep learning framework that can recover the canonical view of face images. It dramatically reduces the intra-person variances, while maintaining the inter-person discriminativeness. Unlike the existing face reconstruction methods that were either evaluated in controlled 2D environment or employed 3D information, our approach directly learns the transformation from the face images with a complex set of variations to their canonical views. At the training stage, to avoid the costly process of labeling canonical-view images from the training set by hand, we have devised a new measurement to automatically select or synthesize a canonical-view image for each identity. As an application, this face recovery approach is used for face verification. Facial features are learned from the recovered canonical-view face images by using a facial component-based convolutional neural network. Our approach achieves the state-of-the-art performance on the LFW dataset.
Budgeted Influence Maximization for Multiple Products
Authors: Nan Du, YIngyu Liang, Maria Florina Balcan, Le Song
Date: 2013-12-08 00:00:00
Tasks: Combinatorial Optimization, Marketing

The typical algorithmic problem in viral marketing aims to identify a set of
influential users in a social network, who, when convinced to adopt a product,
shall influence other users in the network and trigger a large cascade of
adoptions. However, the host (the owner of an online social platform) often
faces more constraints than a single product, endless user attentions,
unlimited budget and unbounded time; in reality, multiple products need to be
advertised, each user can tolerate only a small number of recommendations,
influencing user has a cost and advertisers have only limited budgets, and the
adoptions need to be maximized within a short time window.
  Given theses myriads of user, monetary, and timing constraints, it is
extremely challenging for the host to design principled and efficient viral
market algorithms with provable guarantees. In this paper, we provide a novel
solution by formulating the problem as a submodular maximization in a
continuous-time diffusion model under an intersection of a matroid and multiple
knapsack constraints. We also propose an adaptive threshold greedy algorithm
which can be faster than the traditional greedy algorithm with lazy evaluation,
and scalable to networks with million of nodes. Furthermore, our mathematical
formulation allows us to prove that the algorithm can achieve an approximation
factor of $k_a/(2+2 k)$ when $k_a$ out of the $k$ knapsack constraints are
active, which also improves over previous guarantees from combinatorial
optimization literature. In the case when influencing each user has uniform
cost, the approximation becomes even better to a factor of $1/3$. Extensive
synthetic and real world experiments demonstrate that our budgeted influence
maximization algorithm achieves the-state-of-the-art in terms of both
effectiveness and scalability, often beating the next best by significant
margins.
Bayesian Neural Networks for Genetic Association Studies of Complex Disease
Authors: Andrew L. Beam, Alison Motsinger-Reif, Jon Doyle
Date: 2014-04-15 00:00:00
Tasks: Computational Efficiency

Discovering causal genetic variants from large genetic association studies
poses many difficult challenges. Assessing which genetic markers are involved
in determining trait status is a computationally demanding task, especially in
the presence of gene-gene interactions. A non-parametric Bayesian approach in
the form of a Bayesian neural network is proposed for use in analyzing genetic
association studies. Demonstrations on synthetic and real data reveal they are
able to efficiently and accurately determine which variants are involved in
determining case-control status. Using graphics processing units (GPUs) the
time needed to build these models is decreased by several orders of magnitude.
In comparison with commonly used approaches for detecting genetic interactions,
Bayesian neural networks perform very well across a broad spectrum of possible
genetic relationships while having the computational efficiency needed to
handle large datasets.
Sparse Compositional Metric Learning
Authors: Yuan Shi, Aurélien Bellet, Fei Sha
Date: 2014-04-15 00:00:00
Tasks: General Classification, Metric Learning

We propose a new approach for metric learning by framing it as learning a
sparse combination of locally discriminative metrics that are inexpensive to
generate from the training data. This flexible framework allows us to naturally
derive formulations for global, multi-task and local metric learning. The
resulting algorithms have several advantages over existing methods in the
literature: a much smaller number of parameters to be estimated and a
principled way to generalize learned metrics to new testing data points. To
analyze the approach theoretically, we derive a generalization bound that
justifies the sparse combination. Empirically, we evaluate our algorithms on
several datasets against state-of-the-art metric learning methods. The results
are consistent with our theoretical findings and demonstrate the superiority of
our approach in terms of classification performance and scalability.
Sparse Bilinear Logistic Regression
Authors: Jianing V. Shi, Yangyang Xu, Richard G. Baraniuk
Date: 2014-04-15 00:00:00
Tasks: regression

In this paper, we introduce the concept of sparse bilinear logistic
regression for decision problems involving explanatory variables that are
two-dimensional matrices. Such problems are common in computer vision,
brain-computer interfaces, style/content factorization, and parallel factor
analysis. The underlying optimization problem is bi-convex; we study its
solution and develop an efficient algorithm based on block coordinate descent.
We provide a theoretical guarantee for global convergence and estimate the
asymptotical convergence rate using the Kurdyka-{\L}ojasiewicz inequality. A
range of experiments with simulated and real data demonstrate that sparse
bilinear logistic regression outperforms current techniques in several
important applications.
On the Role of Canonicity in Bottom-up Knowledge Compilation
Authors: Guy Van den Broeck, Adnan Darwiche
Date: 2014-04-15 00:00:00
Tasks: Open-Ended Question Answering

We consider the problem of bottom-up compilation of knowledge bases, which is
usually predicated on the existence of a polytime function for combining
compilations using Boolean operators (usually called an Apply function). While
such a polytime Apply function is known to exist for certain languages (e.g.,
OBDDs) and not exist for others (e.g., DNNF), its existence for certain
languages remains unknown. Among the latter is the recently introduced language
of Sentential Decision Diagrams (SDDs), for which a polytime Apply function
exists for unreduced SDDs, but remains unknown for reduced ones (i.e. canonical
SDDs). We resolve this open question in this paper and consider some of its
theoretical and practical implications. Some of the findings we report question
the common wisdom on the relationship between bottom-up compilation, language
canonicity and the complexity of the Apply function.
Ensemble Classifiers and Their Applications: A Review
Authors: Akhlaqur Rahman, Sumaira Tasnim
Date: 2014-04-15 00:00:00
Tasks: General Classification

Ensemble classifier refers to a group of individual classifiers that are
cooperatively trained on data set in a supervised classification problem. In
this paper we present a review of commonly used ensemble classifiers in the
literature. Some ensemble classifiers are also developed targeting specific
applications. We also present some application driven ensemble classifiers in
this paper.
Efficient Regularization of Squared Curvature
Authors: Claudia Nieuwenhuis, Eno Toeppe, Lena Gorelick, Olga Veksler, Yuri Boykov
Date: 2013-11-07 00:00:00

Curvature has received increased attention as an important alternative to
length based regularization in computer vision. In contrast to length, it
preserves elongated structures and fine details. Existing approaches are either
inefficient, or have low angular resolution and yield results with strong block
artifacts. We derive a new model for computing squared curvature based on
integral geometry. The model counts responses of straight line triple cliques.
The corresponding energy decomposes into submodular and supermodular pairwise
potentials. We show that this energy can be efficiently minimized even for high
angular resolutions using the trust region framework. Our results confirm that
we obtain accurate and visually pleasing solutions without strong artifacts at
reasonable run times.
Submodularization for Quadratic Pseudo-Boolean Optimization
Authors: Lena Gorelick, Yuri Boykov, Olga Veksler, Ismail Ben Ayed, Andrew Delong
Date: 2013-11-08 00:00:00

Many computer vision problems require optimization of binary non-submodular
energies. We propose a general optimization framework based on local submodular
approximations (LSA). Unlike standard LP relaxation methods that linearize the
whole energy globally, our approach iteratively approximates the energies
locally. On the other hand, unlike standard local optimization methods (e.g.
gradient descent or projection techniques) we use non-linear submodular
approximations and optimize them without leaving the domain of integer
solutions. We discuss two specific LSA algorithms based on "trust region" and
"auxiliary function" principles, LSA-TR and LSA-AUX. These methods obtain
state-of-the-art results on a wide range of applications outperforming many
standard techniques such as LBP, QPBO, and TRWS. While our paper is focused on
pairwise energies, our ideas extend to higher-order problems. The code is
available online (http://vision.csd.uwo.ca/code/).
Relative Comparison Kernel Learning with Auxiliary Kernels
Authors: Eric Heim, Hamed Valizadegan, Milos Hauskrecht
Date: 2013-09-02 00:00:00

In this work we consider the problem of learning a positive semidefinite
kernel matrix from relative comparisons of the form: "object A is more similar
to object B than it is to C", where comparisons are given by humans. Existing
solutions to this problem assume many comparisons are provided to learn a high
quality kernel. However, this can be considered unrealistic for many real-world
tasks since relative assessments require human input, which is often costly or
difficult to obtain. Because of this, only a limited number of these
comparisons may be provided. In this work, we explore methods for aiding the
process of learning a kernel with the help of auxiliary kernels built from more
easily extractable information regarding the relationships among objects. We
propose a new kernel learning approach in which the target kernel is defined as
a conic combination of auxiliary kernels and a kernel whose elements are
learned directly. We formulate a convex optimization to solve for this target
kernel that adds only minor overhead to methods that use no auxiliary
information. Empirical results show that in the presence of few training
relative comparisons, our method can learn kernels that generalize to more
out-of-sample comparisons than methods that do not utilize auxiliary
information, as well as similar methods that learn metrics over objects.
An effective AHP-based metaheuristic approach to solve supplier selection problem
Authors: Tamal Ghosh, Tanmoy Chakraborty, Pranab K Dan
Date: 2014-04-15 00:00:00
Tasks: Decision Making, Robust Design

The supplier selection problem is based on electing the best supplier from a
group of pre-specified candidates, is identified as a Multi Criteria Decision
Making (MCDM), is proportionately significant in terms of qualitative and
quantitative attributes. It is a fundamental issue to achieve a trade-off
between such quantifiable and unquantifiable attributes with an aim to
accomplish the best solution to the abovementioned problem. This article
portrays a metaheuristic based optimization model to solve this NP-Complete
problem. Initially the Analytic Hierarchy Process (AHP) is implemented to
generate an initial feasible solution of the problem. Thereafter a Simulated
Annealing (SA) algorithm is exploited to improve the quality of the obtained
solution. The Taguchi robust design method is exploited to solve the critical
issues on the subject of the parameter selection of the SA technique. In order
to verify the proposed methodology the numerical results are demonstrated based
on tangible industry data.
Hybrid Conditional Gradient - Smoothing Algorithms with Applications to Sparse and Low Rank Regularization
Authors: Andreas Argyriou, Marco Signoretto, Johan Suykens
Date: 2014-04-14 00:00:00

We study a hybrid conditional gradient - smoothing algorithm (HCGS) for
solving composite convex optimization problems which contain several terms over
a bounded set. Examples of these include regularization problems with several
norms as penalties and a norm constraint. HCGS extends conditional gradient
methods to cases with multiple nonsmooth terms, in which standard conditional
gradient methods may be difficult to apply. The HCGS algorithm borrows
techniques from smoothing proximal methods and requires first-order
computations (subgradients and proximity operations). Unlike proximal methods,
HCGS benefits from the advantages of conditional gradient methods, which render
it more efficient on certain large scale optimization problems. We demonstrate
these advantages with simulations on two matrix optimization problems:
regularization of matrices with combined $\ell_1$ and trace norm penalties; and
a convex relaxation of sparse PCA.