pwc-archive/papers-with-abstracts huggingface dataset:
Use of the Triangular Fuzzy Numbers for Student Assessment
Authors: Michael Voskoglou
Date: 2015-07-12 00:00:00

In an earlier work we have used the Triangular Fuzzy Numbers (TFNs)as an
assessment tool of student skills.This approach led to an approximate
linguistic characterization of the students' overall performance, but it was
not proved to be sufficient in all cases for comparing the performance of two
different student groups, since tywo TFNs are not always comparable. In the
present paper we complete the above fuzzy assessment approach by presenting a
defuzzification method of TFNS based on the Center of Gravity (COG) technique,
which enables the required comparison. In addition we extend our results by
using the Trapezoidal Fuzzy Numbers (TpFNs) too, which are a generalization of
the TFNs, for student assessment and we present suitable examples illustrating
our new results in practice.
Correlational Neural Networks
Authors: Sarath Chandar, Mitesh M. Khapra, Hugo Larochelle, Balaraman Ravindran
Date: 2015-04-27 00:00:00
Tasks: Representation Learning, Transfer Learning

Common Representation Learning (CRL), wherein different descriptions (or
views) of the data are embedded in a common subspace, is receiving a lot of
attention recently. Two popular paradigms here are Canonical Correlation
Analysis (CCA) based approaches and Autoencoder (AE) based approaches. CCA
based approaches learn a joint representation by maximizing correlation of the
views when projected to the common subspace. AE based methods learn a common
representation by minimizing the error of reconstructing the two views. Each of
these approaches has its own advantages and disadvantages. For example, while
CCA based approaches outperform AE based approaches for the task of transfer
learning, they are not as scalable as the latter. In this work we propose an AE
based approach called Correlational Neural Network (CorrNet), that explicitly
maximizes correlation among the views when projected to the common subspace.
Through a series of experiments, we demonstrate that the proposed CorrNet is
better than the above mentioned approaches with respect to its ability to learn
correlated common representations. Further, we employ CorrNet for several cross
language tasks and show that the representations learned using CorrNet perform
better than the ones learned using other state of the art approaches.
Asymptotic Logical Uncertainty and The Benford Test
Authors: Scott Garrabrant, Siddharth Bhaskar, Abram Demski, Joanna Garrabrant, George Koleszarik, Evan Lloyd
Date: 2015-10-12 00:00:00

We give an algorithm A which assigns probabilities to logical sentences. For
any simple infinite sequence of sentences whose truth-values appear
indistinguishable from a biased coin that outputs "true" with probability p, we
have that the sequence of probabilities that A assigns to these sentences
converges to p.
The Inductive Constraint Programming Loop
Authors: Christian Bessiere, Luc De Raedt, Tias Guns, Lars Kotthoff, Mirco Nanni, Siegfried Nijssen, Barry O'Sullivan, Anastasia Paparrizou, Dino Pedreschi, Helmut Simonis
Date: 2015-10-12 00:00:00
Tasks: BIG-bench Machine Learning, Scheduling

Constraint programming is used for a variety of real-world optimisation
problems, such as planning, scheduling and resource allocation problems. At the
same time, one continuously gathers vast amounts of data about these problems.
Current constraint programming software does not exploit such data to update
schedules, resources and plans. We propose a new framework, that we call the
Inductive Constraint Programming loop. In this approach data is gathered and
analyzed systematically, in order to dynamically revise and adapt constraints
and optimization criteria. Inductive Constraint Programming aims at bridging
the gap between the areas of data mining and machine learning on the one hand,
and constraint programming on the other hand.
On the Robustness of Regularized Pairwise Learning Methods Based on Kernels
Authors: Andreas Christmann, Ding-Xuan Zhou
Date: 2015-10-12 00:00:00
Tasks: BIG-bench Machine Learning, Learning Theory

Regularized empirical risk minimization including support vector machines
plays an important role in machine learning theory. In this paper regularized
pairwise learning (RPL) methods based on kernels will be investigated. One
example is regularized minimization of the error entropy loss which has
recently attracted quite some interest from the viewpoint of consistency and
learning rates. This paper shows that such RPL methods have additionally good
statistical robustness properties, if the loss function and the kernel are
chosen appropriately. We treat two cases of particular interest: (i) a bounded
and non-convex loss function and (ii) an unbounded convex loss function
satisfying a certain Lipschitz type condition.
Using Anatomical Markers for Left Ventricular Segmentation of Long Axis Ultrasound Images
Authors: Yael Petrank, Nahum Smirin, Yossi Tsadok, Zvi Friedman, Peter Lysiansky, Dan Adam
Date: 2015-10-12 00:00:00
Tasks: Segmentation

Left ventricular segmentation is essential for measuring left ventricular
function indices. Segmentation of one or several images requires an initial
guess of the contour. It is hypothesized here that creating an initial guess by
first detecting anatomical markers, would lead to correct detection of the
endocardium. The first step of the algorithm presented here includes automatic
detection of the mitral valve. Next, the apex is detected in the same frame.
The valve is then tracked throughout the cardiac cycle. Contours passing from
the apex to each valve corner are then found using a dynamic programming
algorithm. The resulting contour is used as an input to an active contour
algorithm. The algorithm was tested on 21 long axis ultrasound clips and showed
good agreement with manually traced contours. Thus, this study demonstrates
that detection of anatomic markers leads to a reliable initial guess of the
left ventricle border.
Interactive multiclass segmentation using superpixel classification
Authors: Bérengère Mathieu, Alain Crouzil, Jean-Baptiste Puel
Date: 2015-10-12 00:00:00
Tasks: Classification, General Classification, Interactive Segmentation, Segmentation

This paper adresses the problem of interactive multiclass segmentation. We
propose a fast and efficient new interactive segmentation method called
Superpixel Classification-based Interactive Segmentation (SCIS). From a few
strokes drawn by a human user over an image, this method extracts relevant
semantic objects. To get a fast calculation and an accurate segmentation, SCIS
uses superpixel over-segmentation and support vector machine classification. In
this paper, we demonstrate that SCIS significantly outperfoms competing
algorithms by evaluating its performances on the reference benchmarks of
McGuinness and Santner.
Data structuring for the ontological modelling of wind energy systems
Authors: Adrian Groza
Date: 2015-10-12 00:00:00
Tasks: General Knowledge

Small wind projects encounter difficulties to be efficiently deployed, partly
because wrong way data and information are managed. Ontologies can overcome the
drawbacks of partially available, noisy, inconsistent, and heterogeneous data
sources, by providing a semantic middleware between low level data and more
general knowledge. In this paper, we engineer an ontology for the wind energy
domain using description logic as technical instrumentation. We aim to
integrate corpus of heterogeneous knowledge, both digital and human, in order
to help the interested user to speed-up the initialization of a small-scale
wind project. We exemplify one use case scenario of our ontology, that consists
of automatically checking whether a planned wind project is compliant or not
with the active regulations.
On Correcting Inputs: Inverse Optimization for Online Structured Prediction
Authors: Hal Daumé III, Samir Khuller, Manish Purohit, Gregory Sanders
Date: 2015-10-12 00:00:00
Tasks: Structured Prediction

Algorithm designers typically assume that the input data is correct, and then
proceed to find "optimal" or "sub-optimal" solutions using this input data.
However this assumption of correct data does not always hold in practice,
especially in the context of online learning systems where the objective is to
learn appropriate feature weights given some training samples. Such scenarios
necessitate the study of inverse optimization problems where one is given an
input instance as well as a desired output and the task is to adjust the input
data so that the given output is indeed optimal. Motivated by learning
structured prediction models, in this paper we consider inverse optimization
with a margin, i.e., we require the given output to be better than all other
feasible outputs by a desired margin. We consider such inverse optimization
problems for maximum weight matroid basis, matroid intersection, perfect
matchings, minimum cost maximum flows, and shortest paths and derive the first
known results for such problems with a non-zero margin. The effectiveness of
these algorithmic approaches to online learning for structured prediction is
also discussed.
Fast detection of multiple objects in traffic scenes with a common detection framework
Authors: Qichang Hu, Sakrapee Paisitkriangkrai, Chunhua Shen, Anton Van Den Hengel, Fatih Porikli
Date: 2015-10-12 00:00:00
Tasks: Traffic Sign Detection

Traffic scene perception (TSP) aims to real-time extract accurate on-road
environment information, which in- volves three phases: detection of objects of
interest, recognition of detected objects, and tracking of objects in motion.
Since recognition and tracking often rely on the results from detection, the
ability to detect objects of interest effectively plays a crucial role in TSP.
In this paper, we focus on three important classes of objects: traffic signs,
cars, and cyclists. We propose to detect all the three important objects in a
single learning based detection framework. The proposed framework consists of a
dense feature extractor and detectors of three important classes. Once the
dense features have been extracted, these features are shared with all
detectors. The advantage of using one common framework is that the detection
speed is much faster, since all dense features need only to be evaluated once
in the testing phase. In contrast, most previous works have designed specific
detectors using different features for each of these objects. To enhance the
feature robustness to noises and image deformations, we introduce spatially
pooled features as a part of aggregated channel features. In order to further
improve the generalization performance, we propose an object subcategorization
method as a means of capturing intra-class variation of objects. We
experimentally demonstrate the effectiveness and efficiency of the proposed
framework in three detection applications: traffic sign detection, car
detection, and cyclist detection. The proposed framework achieves the
competitive performance with state-of- the-art approaches on several benchmark
datasets.
Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCA
Authors: Chun-Liang Li, Hsuan-Tien Lin, Chi-Jen Lu
Date: 2015-06-04 00:00:00
Tasks: Vocal Bursts Valence Prediction

We study the problem of recovering the subspace spanned by the first $k$
principal components of $d$-dimensional data under the streaming setting, with
a memory bound of $O(kd)$. Two families of algorithms are known for this
problem. The first family is based on the framework of stochastic gradient
descent. Nevertheless, the convergence rate of the family can be seriously
affected by the learning rate of the descent steps and deserves more serious
study. The second family is based on the power method over blocks of data, but
setting the block size for its existing algorithms is not an easy task. In this
paper, we analyze the convergence rate of a representative algorithm with
decayed learning rate (Oja and Karhunen, 1985) in the first family for the
general $k>1$ case. Moreover, we propose a novel algorithm for the second
family that sets the block sizes automatically and dynamically with faster
convergence rate. We then conduct empirical studies that fairly compare the two
families on real-world data. The studies reveal the advantages and
disadvantages of these two families.
Learning Deep Object Detectors from 3D Models
Authors: Xingchao Peng, Baochen Sun, Karim Ali, Kate Saenko
Date: 2014-12-22 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object

Crowdsourced 3D CAD models are becoming easily accessible online, and can
potentially generate an infinite number of training images for almost any
object category.We show that augmenting the training data of contemporary Deep
Convolutional Neural Net (DCNN) models with such synthetic data can be
effective, especially when real training data is limited or not well matched to
the target domain. Most freely available CAD models capture 3D shape but are
often missing other low level cues, such as realistic object texture, pose, or
background. In a detailed analysis, we use synthetic CAD-rendered images to
probe the ability of DCNN to learn without these cues, with surprising
findings. In particular, we show that when the DCNN is fine-tuned on the target
detection task, it exhibits a large degree of invariance to missing low-level
cues, but, when pretrained on generic ImageNet classification, it learns better
when the low-level cues are simulated. We show that our synthetic DCNN training
approach significantly outperforms previous methods on the PASCAL VOC2007
dataset when learning in the few-shot scenario and improves performance in a
domain shift scenario on the Office benchmark.
A Markov Jump Process for More Efficient Hamiltonian Monte Carlo
Authors: Andrew B. Berger, Mayur Mudigonda, Michael R. DeWeese, Jascha Sohl-Dickstein
Date: 2015-09-13 00:00:00

In most sampling algorithms, including Hamiltonian Monte Carlo, transition
rates between states correspond to the probability of making a transition in a
single time step, and are constrained to be less than or equal to 1. We derive
a Hamiltonian Monte Carlo algorithm using a continuous time Markov jump
process, and are thus able to escape this constraint. Transition rates in a
Markov jump process need only be non-negative. We demonstrate that the new
algorithm leads to improved mixing for several example problems, both by
evaluating the spectral gap of the Markov operator, and by computing
autocorrelation as a function of compute time. We release the algorithm as an
open source Python package.
ParallelPC: an R package for efficient constraint based causal exploration
Authors: Thuc Duy Le, Tao Hoang, Jiuyong Li, Lin Liu, Shu Hu
Date: 2015-10-11 00:00:00

Discovering causal relationships from data is the ultimate goal of many
research areas. Constraint based causal exploration algorithms, such as PC,
FCI, RFCI, PC-simple, IDA and Joint-IDA have achieved significant progress and
have many applications. A common problem with these methods is the high
computational complexity, which hinders their applications in real world high
dimensional datasets, e.g gene expression datasets. In this paper, we present
an R package, ParallelPC, that includes the parallelised versions of these
causal exploration algorithms. The parallelised algorithms help speed up the
procedure of experimenting big datasets and reduce the memory used when running
the algorithms. The package is not only suitable for super-computers or
clusters, but also convenient for researchers using personal computers with
multi core CPUs. Our experiment results on real world datasets show that using
the parallelised algorithms it is now practical to explore causal relationships
in high dimensional datasets with thousands of variables in a single multicore
computer. ParallelPC is available in CRAN repository at
https://cran.rproject.org/web/packages/ParallelPC/index.html.
Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation
Authors: Sylvain Arlot, Matthieu Lerasle
Date: 2012-10-22 00:00:00
Tasks: Density Estimation, Model Selection

This paper studies V-fold cross-validation for model selection in
least-squares density estimation. The goal is to provide theoretical grounds
for choosing V in order to minimize the least-squares loss of the selected
estimator. We first prove a non-asymptotic oracle inequality for V-fold
cross-validation and its bias-corrected version (V-fold penalization). In
particular, this result implies that V-fold penalization is asymptotically
optimal in the nonparametric case. Then, we compute the variance of V-fold
cross-validation and related criteria, as well as the variance of key
quantities for model selection performance. We show that these variances depend
on V like 1+4/(V-1), at least in some particular cases, suggesting that the
performance increases much from V=2 to V=5 or 10, and then is almost constant.
Overall, this can explain the common advice to take V=5---at least in our
setting and when the computational power is limited---, as supported by some
simulation experiments. An oracle inequality and exact formulas for the
variance are also proved for Monte-Carlo cross-validation, also known as
repeated cross-validation, where the parameter V is replaced by the number B of
random splits of the data.
Textual Analysis for Studying Chinese Historical Documents and Literary Novels
Authors: Chao-Lin Liu, Guan-Tao Jin, Hongsu Wang, Qing-Feng Liu, Wen-Huei Cheng, Wei-Yun Chiu, Richard Tzong-Han Tsai, Yu-Chun Wang
Date: 2015-10-11 00:00:00
Tasks: Articles

We analyzed historical and literary documents in Chinese to gain insights
into research issues, and overview our studies which utilized four different
sources of text materials in this paper. We investigated the history of
concepts and transliterated words in China with the Database for the Study of
Modern China Thought and Literature, which contains historical documents about
China between 1830 and 1930. We also attempted to disambiguate names that were
shared by multiple government officers who served between 618 and 1912 and were
recorded in Chinese local gazetteers. To showcase the potentials and challenges
of computer-assisted analysis of Chinese literatures, we explored some
interesting yet non-trivial questions about two of the Four Great Classical
Novels of China: (1) Which monsters attempted to consume the Buddhist monk
Xuanzang in the Journey to the West (JTTW), which was published in the 16th
century, (2) Which was the most powerful monster in JTTW, and (3) Which major
role smiled the most in the Dream of the Red Chamber, which was published in
the 18th century. Similar approaches can be applied to the analysis and study
of modern documents, such as the newspaper articles published about the 228
incident that occurred in 1947 in Taiwan.
OmniGraph: Rich Representation and Graph Kernel Learning
Authors: Boyi Xie, Rebecca J. Passonneau
Date: 2015-10-10 00:00:00
Tasks: Feature Engineering, General Classification

OmniGraph, a novel representation to support a range of NLP classification
tasks, integrates lexical items, syntactic dependencies and frame semantic
parses into graphs. Feature engineering is folded into the learning through
convolution graph kernel learning to explore different extents of the graph. A
high-dimensional space of features includes individual nodes as well as complex
subgraphs. In experiments on a text-forecasting problem that predicts stock
price change from news for company mentions, OmniGraph beats several benchmarks
based on bag-of-words, syntactic dependencies, and semantic trees. The highly
expressive features OmniGraph discovers provide insights into the semantics
across distinct market sectors. To demonstrate the method's generality, we also
report its high performance results on a fine-grained sentiment corpus.
Optimal Piecewise Linear Function Approximation for GPU-based Applications
Authors: Daniel Berjón, Guillermo Gallego, Carlos Cuevas, Francisco Morán, Narciso García
Date: 2015-10-10 00:00:00
Tasks: GPU

Many computer vision and human-computer interaction applications developed in
recent years need evaluating complex and continuous mathematical functions as
an essential step toward proper operation. However, rigorous evaluation of this
kind of functions often implies a very high computational cost, unacceptable in
real-time applications. To alleviate this problem, functions are commonly
approximated by simpler piecewise-polynomial representations. Following this
idea, we propose a novel, efficient, and practical technique to evaluate
complex and continuous functions using a nearly optimal design of two types of
piecewise linear approximations in the case of a large budget of evaluation
subintervals. To this end, we develop a thorough error analysis that yields
asymptotically tight bounds to accurately quantify the approximation
performance of both representations. It provides an improvement upon previous
error estimates and allows the user to control the trade-off between the
approximation error and the number of evaluation subintervals. To guarantee
real-time operation, the method is suitable for, but not limited to, an
efficient implementation in modern Graphics Processing Units (GPUs), where it
outperforms previous alternative approaches by exploiting the fixed-function
interpolation routines present in their texture units. The proposed technique
is a perfect match for any application requiring the evaluation of continuous
functions, we have measured in detail its quality and efficiency on several
functions, and, in particular, the Gaussian function because it is extensively
used in many areas of computer vision and cybernetics, and it is expensive to
evaluate.
On oblivious branching programs with bounded repetition that cannot efficiently compute CNFs of bounded treewidth
Authors: Igor Razgon
Date: 2015-10-10 00:00:00

In this paper we study complexity of an extension of ordered binary decision
diagrams (OBDDs) called $c$-OBDDs on CNFs of bounded (primal graph) treewidth.
In particular, we show that for each $k$ there is a class of CNFs of treewidth
$k \geq 3$ for which the equivalent $c$-OBDDs are of size
$\Omega(n^{k/(8c-4)})$. Moreover, this lower bound holds if $c$-OBDD is
non-deterministic and semantic. Our second result uses the above lower bound to
separate the above model from sentential decision diagrams (SDDs). In order to
obtain the lower bound, we use a structural graph parameter called matching
width. Our third result shows that matching width and pathwidth are linearly
related.
Spatial Semantic Regularisation for Large Scale Object Detection
Authors: Damian Mrowca, Marcus Rohrbach, Judy Hoffman, Ronghang Hu, Kate Saenko, Trevor Darrell
Date: 2015-10-10 00:00:00
Proceeding: ICCV 2015 12
Tasks: Clustering, Object, object-detection, Object Detection

Large scale object detection with thousands of classes introduces the problem
of many contradicting false positive detections, which have to be suppressed.
Class-independent non-maximum suppression has traditionally been used for this
step, but it does not scale well as the number of classes grows. Traditional
non-maximum suppression does not consider label- and instance-level
relationships nor does it allow an exploitation of the spatial layout of
detection proposals. We propose a new multi-class spatial semantic
regularisation method based on affinity propagation clustering, which
simultaneously optimises across all categories and all proposed locations in
the image, to improve both the localisation and categorisation of selected
detection proposals. Constraints are shared across the labels through the
semantic WordNet hierarchy. Our approach proves to be especially useful in
large scale settings with thousands of classes, where spatial and semantic
interactions are very frequent and only weakly supervised detectors can be
built due to a lack of bounding box annotations. Detection experiments are
conducted on the ImageNet and COCO dataset, and in settings with thousands of
detected categories. Our method provides a significant precision improvement by
reducing false positives, while simultaneously improving the recall.