pwc-archive/papers-with-abstracts huggingface dataset:
An Introduction to Convolutional Neural Networks
Authors: Keiron O'Shea, Ryan Nash
Date: 2015-11-26 00:00:00
Tasks: BIG-bench Machine Learning

The field of machine learning has taken a dramatic twist in recent times,
with the rise of the Artificial Neural Network (ANN). These biologically
inspired computational models are able to far exceed the performance of
previous forms of artificial intelligence in common machine learning tasks. One
of the most impressive forms of ANN architecture is that of the Convolutional
Neural Network (CNN). CNNs are primarily used to solve difficult image-driven
pattern recognition tasks and with their precise yet simple architecture,
offers a simplified method of getting started with ANNs.
  This document provides a brief introduction to CNNs, discussing recently
published papers and newly formed techniques in developing these brilliantly
fantastic image recognition models. This introduction assumes you are familiar
with the fundamentals of ANNs and machine learning.
Multi-Image Matching via Fast Alternating Minimization
Authors: Xiaowei Zhou, Menglong Zhu, Kostas Daniilidis
Date: 2015-05-19 00:00:00
Proceeding: ICCV 2015 12
Tasks: global-optimization

In this paper we propose a global optimization-based approach to jointly
matching a set of images. The estimated correspondences simultaneously maximize
pairwise feature affinities and cycle consistency across multiple images.
Unlike previous convex methods relying on semidefinite programming, we
formulate the problem as a low-rank matrix recovery problem and show that the
desired semidefiniteness of a solution can be spontaneously fulfilled. The
low-rank formulation enables us to derive a fast alternating minimization
algorithm in order to handle practical problems with thousands of features.
Both simulation and real experiments demonstrate that the proposed algorithm
can achieve a competitive performance with an order of magnitude speedup
compared to the state-of-the-art algorithm. In the end, we demonstrate the
applicability of the proposed method to match the images of different object
instances and as a result the potential to reconstruct category-specific object
models from those images.
Learning Semantic Similarity for Very Short Texts
Authors: Cedric De Boom, Steven Van Canneyt, Steven Bohez, Thomas Demeester, Bart Dhoedt
Date: 2015-12-02 00:00:00
Tasks: Information Retrieval, Retrieval, Semantic Similarity, Semantic Textual Similarity, Sentence, text similarity, Word Embeddings

Levering data on social media, such as Twitter and Facebook, requires
information retrieval algorithms to become able to relate very short text
fragments to each other. Traditional text similarity methods such as tf-idf
cosine-similarity, based on word overlap, mostly fail to produce good results
in this case, since word overlap is little or non-existent. Recently,
distributed word representations, or word embeddings, have been shown to
successfully allow words to match on the semantic level. In order to pair short
text fragments - as a concatenation of separate words - an adequate distributed
sentence representation is needed, in existing literature often obtained by
naively combining the individual word representations. We therefore
investigated several text representations as a combination of word embeddings
in the context of semantic pair matching. This paper investigates the
effectiveness of several such naive techniques, as well as traditional tf-idf
similarity, for fragments of different lengths. Our main contribution is a
first step towards a hybrid method that combines the strength of dense
distributed representations - as opposed to sparse term matching - with the
strength of tf-idf based methods to automatically reduce the impact of less
informative terms. Our new approach outperforms the existing techniques in a
toy experimental set-up, leading to the conclusion that the combination of word
embeddings and tf-idf information might lead to a better model for semantic
content within very short text fragments.
Active Learning for Delineation of Curvilinear Structures
Authors: Agata Mosinska, Raphael Sznitman, Przemysław Głowacki, Pascal Fua
Date: 2015-12-02 00:00:00
Proceeding: CVPR 2016 6
Tasks: Active Learning, General Classification

Many recent delineation techniques owe much of their increased effectiveness
to path classification algorithms that make it possible to distinguish
promising paths from others. The downside of this development is that they
require annotated training data, which is tedious to produce.
  In this paper, we propose an Active Learning approach that considerably
speeds up the annotation process. Unlike standard ones, it takes advantage of
the specificities of the delineation problem. It operates on a graph and can
reduce the training set size by up to 80% without compromising the
reconstruction quality.
  We will show that our approach outperforms conventional ones on various
biomedical and natural image datasets, thus showing that it is broadly
applicable.
Annotating Character Relationships in Literary Texts
Authors: Philip Massey, Patrick Xia, David Bamman, Noah A. Smith
Date: 2015-12-02 00:00:00
Tasks: Type prediction

We present a dataset of manually annotated relationships between characters
in literary texts, in order to support the training and evaluation of automatic
methods for relation type prediction in this domain (Makazhanov et al., 2014;
Kokkinakis, 2013) and the broader computational analysis of literary character
(Elson et al., 2010; Bamman et al., 2014; Vala et al., 2015; Flekova and
Gurevych, 2015). In this work, we solicit annotations from workers on Amazon
Mechanical Turk for 109 texts ranging from Homer's _Iliad_ to Joyce's _Ulysses_
on four dimensions of interest: for a given pair of characters, we collect
judgments as to the coarse-grained category (professional, social, familial),
fine-grained category (friend, lover, parent, rival, employer), and affinity
(positive, negative, neutral) that describes their primary relationship in a
text. We do not assume that this relationship is static; we also collect
judgments as to whether it changes at any point in the course of the text.
MMSE Estimation for Poisson Noise Removal in Images
Authors: Stanislav Pyatykh, Jürgen Hesser
Date: 2015-12-02 00:00:00
Tasks: Denoising

Poisson noise suppression is an important preprocessing step in several
applications, such as medical imaging, microscopy, and astronomical imaging. In
this work, we propose a novel patch-wise Poisson noise removal strategy, in
which the MMSE estimator is utilized in order to produce the denoising result
for each image patch. Fast and accurate computation of the MMSE estimator is
carried out using k-d tree search followed by search in the K-nearest neighbor
graph. Our experiments show that the proposed method is the preferable choice
for low signal-to-noise ratios.
Duelist Algorithm: An Algorithm Inspired by How Duelist Improve Their Capabilities in a Duel
Authors: Totok Ruki Biyanto, Henokh Yernias Fibrianto, Gunawan Nugroho, Erny Listijorini, Titik Budiati, Hairul Huda
Date: 2015-12-02 00:00:00

This paper proposes an optimization algorithm based on how human fight and
learn from each duelist. Since this algorithm is based on population, the
proposed algorithm starts with an initial set of duelists. The duel is to
determine the winner and loser. The loser learns from the winner, while the
winner try their new skill or technique that may improve their fighting
capabilities. A few duelists with highest fighting capabilities are called as
champion. The champion train a new duelists such as their capabilities. The new
duelist will join the tournament as a representative of each champion. All
duelist are re-evaluated, and the duelists with worst fighting capabilities is
eliminated to maintain the amount of duelists. Two optimization problem is
applied for the proposed algorithm, together with genetic algorithm, particle
swarm optimization and imperialist competitive algorithm. The results show that
the proposed algorithm is able to find the better global optimum and faster
iteration.
Quantifying knowledge with a new calculus for belief functions - a generalization of probability theory
Authors: Timber Kerkvliet, Ronald Meester
Date: 2015-12-02 00:00:00

We first show that there are practical situations in for instance forensic
and gambling settings, in which applying classical probability theory, that is,
based on the axioms of Kolmogorov, is problematic. We then introduce and
discuss Shafer belief functions. Technically, Shafer belief functions
generalize probability distributions. Philosophically, they pertain to
individual or shared knowledge of facts, rather than to facts themselves, and
therefore can be interpreted as generalizing epistemic probability, that is,
probability theory interpreted epistemologically. Belief functions are more
flexible and better suited to deal with certain types of uncertainty than
classical probability distributions. We develop a new calculus for belief
functions which does not use the much criticized Dempster's rule of
combination, by generalizing the classical notions of conditioning and
independence in a natural and uncontroversial way. Using this calculus, we
explain our rejection of Dempster's rule in detail. We apply the new theory to
a number of examples, including a gambling example and an example in a forensic
setting. We prove a law of large numbers for belief functions and offer a
betting interpretation similar to the Dutch Book Theorem for probability
distributions.
Hybrid Approach for Inductive Semi Supervised Learning using Label Propagation and Support Vector Machine
Authors: Aruna Govada, Pravin Joshi, Sahil Mittal, Sanjay K. Sahay
Date: 2015-12-02 00:00:00

Semi supervised learning methods have gained importance in today's world
because of large expenses and time involved in labeling the unlabeled data by
human experts. The proposed hybrid approach uses SVM and Label Propagation to
label the unlabeled data. In the process, at each step SVM is trained to
minimize the error and thus improve the prediction quality. Experiments are
conducted by using SVM and logistic regression(Logreg). Results prove that SVM
performs tremendously better than Logreg. The approach is tested using 12
datasets of different sizes ranging from the order of 1000s to the order of
10000s. Results show that the proposed approach outperforms Label Propagation
by a large margin with F-measure of almost twice on average. The parallel
version of the proposed approach is also designed and implemented, the analysis
shows that the training time decreases significantly when parallel version is
used.
Centroid Based Binary Tree Structured SVM for Multi Classification
Authors: Aruna Govada, Bhavul Gauri, S. K. Sahay
Date: 2015-12-02 00:00:00
Tasks: Classification, General Classification

Support Vector Machines (SVMs) were primarily designed for 2-class
classification. But they have been extended for N-class classification also
based on the requirement of multiclasses in the practical applications.
Although N-class classification using SVM has considerable research attention,
getting minimum number of classifiers at the time of training and testing is
still a continuing research. We propose a new algorithm CBTS-SVM (Centroid
based Binary Tree Structured SVM) which addresses this issue. In this we build
a binary tree of SVM models based on the similarity of the class labels by
finding their distance from the corresponding centroids at the root level. The
experimental results demonstrates the comparable accuracy for CBTS with OVO
with reasonable gamma and cost values. On the other hand when CBTS is compared
with OVA, it gives the better accuracy with reduced training time and testing
time. Furthermore CBTS is also scalable as it is able to handle the large data
sets.
Multiple-Instance Learning: Radon-Nikodym Approach to Distribution Regression Problem
Authors: Vladislav Gennadievich Malyshkin
Date: 2015-11-29 00:00:00
Tasks: Multiple Instance Learning, regression

For distribution regression problem, where a bag of $x$--observations is
mapped to a single $y$ value, a one--step solution is proposed. The problem of
random distribution to random value is transformed to random vector to random
value by taking distribution moments of $x$ observations in a bag as random
vector. Then Radon--Nikodym or least squares theory can be applied, what give
$y(x)$ estimator. The probability distribution of $y$ is also obtained, what
requires solving generalized eigenvalues problem, matrix spectrum (not
depending on $x$) give possible $y$ outcomes and depending on $x$ probabilities
of outcomes can be obtained by projecting the distribution with fixed $x$ value
(delta--function) to corresponding eigenvector. A library providing numerically
stable polynomial basis for these calculations is available, what make the
proposed approach practical.
Continuous and Simultaneous Gesture and Posture Recognition for Commanding a Robotic Wheelchair; Towards Spotting the Signal Patterns
Authors: Ali Boyali, Naohisa Hashimoto, Manolya Kavakli
Date: 2015-12-02 00:00:00
Tasks: Clustering, General Classification, Sparse Representation-based Classification

Spotting signal patterns with varying lengths has been still an open problem
in the literature. In this study, we describe a signal pattern recognition
approach for continuous and simultaneous classification of a tracked hand's
posture and gestures and map them to steering commands for control of a robotic
wheelchair. The developed methodology not only affords 100\% recognition
accuracy on a streaming signal for continuous recognition, but also brings
about a new perspective for building a training dictionary which eliminates
human intervention to spot the gesture or postures on a training signal. In the
training phase we employ a state of art subspace clustering method to find the
most representative state samples. The recognition and training framework
reveal boundaries of the patterns on the streaming signal with a successive
decision tree structure intrinsically. We make use of the Collaborative ans
Block Sparse Representation based classification methods for continuous gesture
and posture recognition.
Multi-user lax communications: a multi-armed bandit approach
Authors: Orly Avner, Shie Mannor
Date: 2015-04-30 00:00:00

Inspired by cognitive radio networks, we consider a setting where multiple
users share several channels modeled as a multi-user multi-armed bandit (MAB)
problem. The characteristics of each channel are unknown and are different for
each user. Each user can choose between the channels, but her success depends
on the particular channel chosen as well as on the selections of other users:
if two users select the same channel their messages collide and none of them
manages to send any data. Our setting is fully distributed, so there is no
central control. As in many communication systems, the users cannot set up a
direct communication protocol, so information exchange must be limited to a
minimum. We develop an algorithm for learning a stable configuration for the
multi-user MAB problem. We further offer both convergence guarantees and
experiments inspired by real communication networks, including comparison to
state-of-the-art algorithms.
Double Sparse Multi-Frame Image Super Resolution
Authors: Toshiyuki Kato, Hideitsu Hino, Noboru Murata
Date: 2015-12-02 00:00:00
Tasks: Image Registration, Image Super-Resolution, Multi-Frame Super-Resolution, Super-Resolution

A large number of image super resolution algorithms based on the sparse
coding are proposed, and some algorithms realize the multi-frame super
resolution. In multi-frame super resolution based on the sparse coding, both
accurate image registration and sparse coding are required. Previous study on
multi-frame super resolution based on sparse coding firstly apply block
matching for image registration, followed by sparse coding to enhance the image
resolution. In this paper, these two problems are solved by optimizing a single
objective function. The results of numerical experiments support the
effectiveness of the proposed approch.
The MegaFace Benchmark: 1 Million Faces for Recognition at Scale
Authors: Ira Kemelmacher-Shlizerman, Steve Seitz, Daniel Miller, Evan Brossard
Date: 2015-12-02 00:00:00
Proceeding: CVPR 2016 6
Tasks: Face Recognition

Recent face recognition experiments on a major benchmark LFW show stunning
performance--a number of algorithms achieve near to perfect score, surpassing
human recognition rates. In this paper, we advocate evaluations at the million
scale (LFW includes only 13K photos of 5K people). To this end, we have
assembled the MegaFace dataset and created the first MegaFace challenge. Our
dataset includes One Million photos that capture more than 690K different
individuals. The challenge evaluates performance of algorithms with increasing
numbers of distractors (going from 10 to 1M) in the gallery set. We present
both identification and verification performance, evaluate performance with
respect to pose and a person's age, and compare as a function of training data
size (number of photos and people). We report results of state of the art and
baseline algorithms. Our key observations are that testing at the million scale
reveals big performance differences (of algorithms that perform similarly well
on smaller scale) and that age invariant recognition as well as pose are still
challenging for most. The MegaFace dataset, baseline code, and evaluation
scripts, are all publicly released for further experimentations at:
megaface.cs.washington.edu.
Klasifikasi Komponen Argumen Secara Otomatis pada Dokumen Teks berbentuk Esai Argumentatif
Authors: Derwin Suhartono
Date: 2015-12-02 00:00:00
Tasks: General Classification

By automatically recognize argument component, essay writers can do some
inspections to texts that they have written. It will assist essay scoring
process objectively and precisely because essay grader is able to see how well
the argument components are constructed. Some reseachers have tried to do
argument detection and classification along with its implementation in some
domains. The common approach is by doing feature extraction to the text.
Generally, the features are structural, lexical, syntactic, indicator, and
contextual. In this research, we add new feature to the existing features. It
adopts keywords list by Knott and Dale (1993). The experiment result shows the
argument classification achieves 72.45% accuracy. Moreover, we still get the
same accuracy without the keyword lists. This concludes that the keyword lists
do not affect significantly to the features. All features are still weak to
classify major claim and claim, so we need other features which are useful to
differentiate those two kind of argument components.
Probabilistic Latent Semantic Analysis (PLSA) untuk Klasifikasi Dokumen Teks Berbahasa Indonesia
Authors: Derwin Suhartono
Date: 2015-12-02 00:00:00
Tasks: Retrieval

One task that is included in managing documents is how to find substantial
information inside. Topic modeling is a technique that has been developed to
produce document representation in form of keywords. The keywords will be used
in the indexing process and document retrieval as needed by users. In this
research, we will discuss specifically about Probabilistic Latent Semantic
Analysis (PLSA). It will cover PLSA mechanism which involves Expectation
Maximization (EM) as the training algorithm, how to conduct testing, and obtain
the accuracy result.
Object-based World Modeling in Semi-Static Environments with Dependent Dirichlet-Process Mixtures
Authors: Lawson L. S. Wong, Thanard Kurutach, Leslie Pack Kaelbling, Tomás Lozano-Pérez
Date: 2015-12-02 00:00:00
Tasks: Attribute, Clustering

To accomplish tasks in human-centric indoor environments, robots need to
represent and understand the world in terms of objects and their attributes. We
refer to this attribute-based representation as a world model, and consider how
to acquire it via noisy perception and maintain it over time, as objects are
added, changed, and removed in the world. Previous work has framed this as
multiple-target tracking problem, where objects are potentially in motion at
all times. Although this approach is general, it is computationally expensive.
We argue that such generality is not needed in typical world modeling tasks,
where objects only change state occasionally. More efficient approaches are
enabled by restricting ourselves to such semi-static environments.
  We consider a previously-proposed clustering-based world modeling approach
that assumed static environments, and extend it to semi-static domains by
applying a dependent Dirichlet-process (DDP) mixture model. We derive a novel
MAP inference algorithm under this model, subject to data association
constraints. We demonstrate our approach improves computational performance in
semi-static environments.
Convergence rates of sub-sampled Newton methods
Authors: Murat A. Erdogdu, Andrea Montanari
Date: 2015-08-12 00:00:00
Proceeding: NeurIPS 2015

We consider the problem of minimizing a sum of $n$ functions over a convex
parameter set $\mathcal{C} \subset \mathbb{R}^p$ where $n\gg p\gg 1$. In this
regime, algorithms which utilize sub-sampling techniques are known to be
effective. In this paper, we use sub-sampling techniques together with low-rank
approximation to design a new randomized batch algorithm which possesses
comparable convergence rate to Newton's method, yet has much smaller
per-iteration cost. The proposed algorithm is robust in terms of starting point
and step size, and enjoys a composite convergence rate, namely, quadratic
convergence at start and linear convergence when the iterate is close to the
minimizer. We develop its theoretical analysis which also allows us to select
near-optimal algorithm parameters. Our theoretical results can be used to
obtain convergence rates of previously proposed sub-sampling based algorithms
as well. We demonstrate how our results apply to well-known machine learning
problems. Lastly, we evaluate the performance of our algorithm on several
datasets under various scenarios.
Seeing the Unseen Network: Inferring Hidden Social Ties from Respondent-Driven Sampling
Authors: Lin Chen, Forrest W. Crawford, Amin Karbasi
Date: 2015-11-13 00:00:00
Tasks: Stochastic Optimization, Time Series, Time Series Analysis

Learning about the social structure of hidden and hard-to-reach populations
--- such as drug users and sex workers --- is a major goal of epidemiological
and public health research on risk behaviors and disease prevention.
Respondent-driven sampling (RDS) is a peer-referral process widely used by many
health organizations, where research subjects recruit other subjects from their
social network. In such surveys, researchers observe who recruited whom, along
with the time of recruitment and the total number of acquaintances (network
degree) of respondents. However, due to privacy concerns, the identities of
acquaintances are not disclosed. In this work, we show how to reconstruct the
underlying network structure through which the subjects are recruited. We
formulate the dynamics of RDS as a continuous-time diffusion process over the
underlying graph and derive the likelihood for the recruitment time series
under an arbitrary recruitment time distribution. We develop an efficient
stochastic optimization algorithm called RENDER (REspoNdent-Driven nEtwork
Reconstruction) that finds the network that best explains the collected data.
We support our analytical results through an exhaustive set of experiments on
both synthetic and real data.