pwc-archive/papers-with-abstracts huggingface dataset:
Random Forests and VGG-NET: An Algorithm for the ISIC 2017 Skin Lesion Classification Challenge
Authors: Songtao Guo, Yixin Luo, Yanzhi Song
Date: 2017-03-15 00:00:00
Tasks: Binary Classification, Classification, General Classification, image-classification, Image Classification, Lesion Classification, Skin Lesion Classification

This manuscript briefly describes an algorithm developed for the ISIC 2017
Skin Lesion Classification Competition. In this task, participants are asked to
complete two independent binary image classification tasks that involve three
unique diagnoses of skin lesions (melanoma, nevus, and seborrheic keratosis).
In the first binary classification task, participants are asked to distinguish
between (a) melanoma and (b) nevus and seborrheic keratosis. In the second
binary classification task, participants are asked to distinguish between (a)
seborrheic keratosis and (b) nevus and melanoma. The other phases of the
competition are not considered. Our proposed algorithm consists of three steps:
preprocessing, classification using VGG-NET and Random Forests, and calculation
of a final score.
Frequency-Domain Stochastic Modeling of Stationary Bivariate or Complex-Valued Signals
Authors: Adam M. Sykulski, Sofia C. Olhede, Jonathan M. Lilly, Jeffrey J. Early
Date: 2013-06-25 00:00:00
Tasks: Time Series, Time Series Analysis

There are three equivalent ways of representing two jointly observed
real-valued signals: as a bivariate vector signal, as a single complex-valued
signal, or as two analytic signals known as the rotary components. Each
representation has unique advantages depending on the system of interest and
the application goals. In this paper we provide a joint framework for all three
representations in the context of frequency-domain stochastic modeling. This
framework allows us to extend many established statistical procedures for
bivariate vector time series to complex-valued and rotary representations.
These include procedures for parametrically modeling signal coherence,
estimating model parameters using the Whittle likelihood, performing
semi-parametric modeling, and choosing between classes of nested models using
model choice. We also provide a new method of testing for impropriety in
complex-valued signals, which tests for noncircular or anisotropic second-order
statistical structure when the signal is represented in the complex plane.
Finally, we demonstrate the usefulness of our methodology in capturing the
anisotropic structure of signals observed from fluid dynamic simulations of
turbulence.
Block Compressive Sensing of Image and Video with Nonlocal Lagrangian Multiplier and Patch-based Sparse Representation
Authors: Trinh Van Chien, Khanh Quoc Dinh, Byeungwoo Jeon, Martin Burger
Date: 2017-03-15 00:00:00
Tasks: Compressive Sensing

Although block compressive sensing (BCS) makes it tractable to sense
large-sized images and video, its recovery performance has yet to be
significantly improved because its recovered images or video usually suffer
from blurred edges, loss of details, and high-frequency oscillatory artifacts,
especially at a low subrate. This paper addresses these problems by designing a
modified total variation technique that employs multi-block gradient
processing, a denoised Lagrangian multiplier, and patch-based sparse
representation. In the case of video, the proposed recovery method is able to
exploit both spatial and temporal similarities. Simulation results confirm the
improved performance of the proposed method for compressive sensing of images
and video in terms of both objective and subjective qualities.
Dynamic landscape models of coevolutionary games
Authors: Hendrik Richter
Date: 2016-11-28 00:00:00

Players of coevolutionary games may update not only their strategies but also
their networks of interaction. Based on interpreting the payoff of players as
fitness, dynamic landscape models are proposed. The modeling procedure is
carried out for Prisoner's Dilemma (PD) and Snowdrift (SD) games that both use
either birth--death (BD) or death--birth (DB) strategy updating. The main focus
is on using dynamic fitness landscapes as a mathematical model of
coevolutionary game dynamics. Hence, an alternative tool for analyzing
coevolutionary games becomes available, and landscape measures such as
modality, ruggedness and information content can be computed and analyzed. In
addition, fixation properties of the games and quantifiers characterizing the
interaction networks are calculated numerically. Relations are established
between landscape properties expressed by landscape measures and quantifiers of
coevolutionary game dynamics such as fixation probabilities, fixation times and
network properties.
Is this word borrowed? An automatic approach to quantify the likeliness of borrowing in social media
Authors: Jasabanta Patro, Bidisha Samanta, Saurabh Singh, Prithwish Mukherjee, Monojit Choudhury, Animesh Mukherjee
Date: 2017-03-15 00:00:00
Tasks: Clustering

Code-mixing or code-switching are the effortless phenomena of natural
switching between two or more languages in a single conversation. Use of a
foreign word in a language; however, does not necessarily mean that the speaker
is code-switching because often languages borrow lexical items from other
languages. If a word is borrowed, it becomes a part of the lexicon of a
language; whereas, during code-switching, the speaker is aware that the
conversation involves foreign words or phrases. Identifying whether a foreign
word used by a bilingual speaker is due to borrowing or code-switching is a
fundamental importance to theories of multilingualism, and an essential
prerequisite towards the development of language and speech technologies for
multilingual communities. In this paper, we present a series of novel
computational methods to identify the borrowed likeliness of a word, based on
the social media signals. We first propose context based clustering method to
sample a set of candidate words from the social media data.Next, we propose
three novel and similar metrics based on the usage of these words by the users
in different tweets; these metrics were used to score and rank the candidate
words indicating their borrowed likeliness. We compare these rankings with a
ground truth ranking constructed through a human judgment experiment. The
Spearman's rank correlation between the two rankings (nearly 0.62 for all the
three metric variants) is more than double the value (0.26) of the most
competitive existing baseline reported in the literature. Some other striking
observations are, (i) the correlation is higher for the ground truth data
elicited from the younger participants (age less than 30) than that from the
older participants, and (ii )those participants who use mixed-language for
tweeting the least, provide the best signals of borrowing.
Bootstrapped Graph Diffusions: Exposing the Power of Nonlinearity
Authors: Eliav Buchnik, Edith Cohen
Date: 2017-03-07 00:00:00

Graph-based semi-supervised learning (SSL) algorithms predict labels for all
nodes based on provided labels of a small set of seed nodes. Classic methods
capture the graph structure through some underlying diffusion process that
propagates through the graph edges. Spectral diffusion, which includes
personalized page rank and label propagation, propagates through random walks.
Social diffusion propagates through shortest paths. A common ground to these
diffusions is their {\em linearity}, which does not distinguish between
contributions of few "strong" relations and many "weak" relations.
  Recently, non-linear methods such as node embeddings and graph convolutional
networks (GCN) demonstrated a large gain in quality for SSL tasks. These
methods introduce multiple components and greatly vary on how the graph
structure, seed label information, and other features are used.
  We aim here to study the contribution of non-linearity, as an isolated
ingredient, to the performance gain. To do so, we place classic linear graph
diffusions in a self-training framework. Surprisingly, we observe that SSL
using the resulting {\em bootstrapped diffusions} not only significantly
improves over the respective non-bootstrapped baselines but also outperform
state-of-the-art non-linear SSL methods. Moreover, since the self-training
wrapper retains the scalability of the base method, we obtain both higher
quality and better scalability.
Convolutional Neural Network Committees for Melanoma Classification with Classical And Expert Knowledge Based Image Transforms Data Augmentation
Authors: Cristina Nader Vasconcelos, Bárbara Nader Vasconcelos
Date: 2017-02-22 00:00:00
Tasks: Data Augmentation, General Classification

Skin cancer is a major public health problem, as is the most common type of
cancer and represents more than half of cancer diagnoses worldwide. Early
detection influences the outcome of the disease and motivates our work. We
investigate the composition of CNN committees and data augmentation for the the
ISBI 2017 Melanoma Classification Challenge (named Skin Lesion Analysis towards
Melanoma Detection) facing the peculiarities of dealing with such a small,
unbalanced, biological database. For that, we explore committees of
Convolutional Neural Networks trained over the ISBI challenge training dataset
artificially augmented by both classical image processing transforms and image
warping guided by specialist knowledge about the lesion axis and improve the
final classifier invariance to common melanoma variations.
Reconstructing undirected graphs from eigenspaces
Authors: Yohann De Castro, Thibault Espinasse, Paul Rochet
Date: 2016-03-26 00:00:00

In this paper, we aim at recovering an undirected weighted graph of $N$
vertices from the knowledge of a perturbed version of the eigenspaces of its
adjacency matrix $W$. For instance, this situation arises for stationary
signals on graphs or for Markov chains observed at random times. Our approach
is based on minimizing a cost function given by the Frobenius norm of the
commutator $\mathsf{A} \mathsf{B}-\mathsf{B} \mathsf{A}$ between symmetric
matrices $\mathsf{A}$ and $\mathsf{B}$.
  In the Erd\H{o}s-R\'enyi model with no self-loops, we show that
identifiability (i.e., the ability to reconstruct $W$ from the knowledge of its
eigenspaces) follows a sharp phase transition on the expected number of edges
with threshold function $N\log N/2$.
  Given an estimation of the eigenspaces based on a $n$-sample, we provide
support selection procedures from theoretical and practical point of views. In
particular, when deleting an edge from the active support, our study unveils
that our test statistic is the order of $\mathcal O(1/n)$ when we overestimate
the true support and lower bounded by a positive constant when the estimated
support is smaller than the true support. This feature leads to a powerful
practical support estimation procedure. Simulated and real life numerical
experiments assert our new methodology.
Learning to Play Guess Who? and Inventing a Grounded Language as a Consequence
Authors: Emilio Jorge, Mikael Kågebäck, Fredrik D. Johansson, Emil Gustavsson
Date: 2016-11-10 00:00:00
Tasks: Image Retrieval

Acquiring your first language is an incredible feat and not easily
duplicated. Learning to communicate using nothing but a few pictureless books,
a corpus, would likely be impossible even for humans. Nevertheless, this is the
dominating approach in most natural language processing today. As an
alternative, we propose the use of situated interactions between agents as a
driving force for communication, and the framework of Deep Recurrent Q-Networks
for evolving a shared language grounded in the provided environment. We task
the agents with interactive image search in the form of the game Guess Who?.
The images from the game provide a non trivial environment for the agents to
discuss and a natural grounding for the concepts they decide to encode in their
communication. Our experiments show that the agents learn not only to encode
physical concepts in their words, i.e. grounding, but also that the agents
learn to hold a multi-step dialogue remembering the state of the dialogue from
step to step.
Selective Harvesting over Networks
Authors: Fabricio Murai, Diogo Rennó, Bruno Ribeiro, Gisele L. Pappa, Don Towsley, Krista Gile
Date: 2017-03-15 00:00:00
Tasks: Multi-Armed Bandits

Active search (AS) on graphs focuses on collecting certain labeled nodes
(targets) given global knowledge of the network topology and its edge weights
under a query budget. However, in most networks, nodes, topology and edge
weights are all initially unknown. We introduce selective harvesting, a variant
of AS where the next node to be queried must be chosen among the neighbors of
the current queried node set; the available training data for deciding which
node to query is restricted to the subgraph induced by the queried set (and
their node attributes) and their neighbors (without any node or edge
attributes). Therefore, selective harvesting is a sequential decision problem,
where we must decide which node to query at each step. A classifier trained in
this scenario suffers from a tunnel vision effect: without recourse to
independent sampling, the urge to query promising nodes forces classifiers to
gather increasingly biased training data, which we show significantly hurts the
performance of AS methods and standard classifiers. We find that it is possible
to collect a much larger set of targets by using multiple classifiers, not by
combining their predictions as an ensemble, but switching between classifiers
used at each step, as a way to ease the tunnel vision effect. We discover that
switching classifiers collects more targets by (a) diversifying the training
data and (b) broadening the choices of nodes that can be queried next. This
highlights an exploration, exploitation, and diversification trade-off in our
problem that goes beyond the exploration and exploitation duality found in
classic sequential decision problems. From these observations we propose D3TS,
a method based on multi-armed bandits for non-stationary stochastic processes
that enforces classifier diversity, matching or exceeding the performance of
competing methods on seven real network datasets in our evaluation.
Tuning Free Orthogonal Matching Pursuit
Authors: Sreejith Kallummil, Sheetal Kalyani
Date: 2017-03-15 00:00:00
Tasks: Compressive Sensing, regression

Orthogonal matching pursuit (OMP) is a widely used compressive sensing (CS)
algorithm for recovering sparse signals in noisy linear regression models. The
performance of OMP depends on its stopping criteria (SC). SC for OMP discussed
in literature typically assumes knowledge of either the sparsity of the signal
to be estimated $k_0$ or noise variance $\sigma^2$, both of which are
unavailable in many practical applications. In this article we develop a
modified version of OMP called tuning free OMP or TF-OMP which does not require
a SC. TF-OMP is proved to accomplish successful sparse recovery under the usual
assumptions on restricted isometry constants (RIC) and mutual coherence of
design matrix. TF-OMP is numerically shown to deliver a highly competitive
performance in comparison with OMP having \textit{a priori} knowledge of $k_0$
or $\sigma^2$. Greedy algorithm for robust de-noising (GARD) is an OMP like
algorithm proposed for efficient estimation in classical overdetermined linear
regression models corrupted by sparse outliers. However, GARD requires the
knowledge of inlier noise variance which is difficult to estimate. We also
produce a tuning free algorithm (TF-GARD) for efficient estimation in the
presence of sparse outliers by extending the operating principle of TF-OMP to
GARD. TF-GARD is numerically shown to achieve a performance comparable to that
of the existing implementation of GARD.
Most Likely Separation of Intensity and Warping Effects in Image Registration
Authors: Line Kühnel, Stefan Sommer, Akshay Pai, Lars Lau Raket
Date: 2016-04-18 00:00:00
Tasks: Image Registration

This paper introduces a class of mixed-effects models for joint modeling of
spatially correlated intensity variation and warping variation in 2D images.
Spatially correlated intensity variation and warp variation are modeled as
random effects, resulting in a nonlinear mixed-effects model that enables
simultaneous estimation of template and model parameters by optimization of the
likelihood function. We propose an algorithm for fitting the model which
alternates estimation of variance parameters and image registration. This
approach avoids the potential estimation bias in the template estimate that
arises when treating registration as a preprocessing step. We apply the model
to datasets of facial images and 2D brain magnetic resonance images to
illustrate the simultaneous estimation and prediction of intensity and warp
effects.
Joint Epipolar Tracking (JET): Simultaneous optimization of epipolar geometry and feature correspondences
Authors: Henry Bradler, Matthias Ochs, Rudolf Mester
Date: 2017-03-15 00:00:00
Tasks: CPU, Pose Estimation

Traditionally, pose estimation is considered as a two step problem. First,
feature correspondences are determined by direct comparison of image patches,
or by associating feature descriptors. In a second step, the relative pose and
the coordinates of corresponding points are estimated, most often by minimizing
the reprojection error (RPE). RPE optimization is based on a loss function that
is merely aware of the feature pixel positions but not of the underlying image
intensities. In this paper, we propose a sparse direct method which introduces
a loss function that allows to simultaneously optimize the unscaled relative
pose, as well as the set of feature correspondences directly considering the
image intensity values. Furthermore, we show how to integrate statistical prior
information on the motion into the optimization process. This constructive
inclusion of a Bayesian bias term is particularly efficient in application
cases with a strongly predictable (short term) dynamic, e.g. in a driving
scenario. In our experiments, we demonstrate that the JET algorithm we propose
outperforms the classical reprojection error optimization on two synthetic
datasets and on the KITTI dataset. The JET algorithm runs in real-time on a
single CPU thread.
Learning Rank Reduced Interpolation with Principal Component Analysis
Authors: Matthias Ochs, Henry Bradler, Rudolf Mester
Date: 2017-03-15 00:00:00
Tasks: Optical Flow Estimation

In computer vision most iterative optimization algorithms, both sparse and
dense, rely on a coarse and reliable dense initialization to bootstrap their
optimization procedure. For example, dense optical flow algorithms profit
massively in speed and robustness if they are initialized well in the basin of
convergence of the used loss function. The same holds true for methods as
sparse feature tracking when initial flow or depth information for new features
at arbitrary positions is needed. This makes it extremely important to have
techniques at hand that allow to obtain from only very few available
measurements a dense but still approximative sketch of a desired 2D structure
(e.g. depth maps, optical flow, disparity maps, etc.). The 2D map is regarded
as sample from a 2D random process. The method presented here exploits the
complete information given by the principal component analysis (PCA) of that
process, the principal basis and its prior distribution. The method is able to
determine a dense reconstruction from sparse measurement. When facing
situations with only very sparse measurements, typically the number of
principal components is further reduced which results in a loss of
expressiveness of the basis. We overcome this problem and inject prior
knowledge in a maximum a posterior (MAP) approach. We test our approach on the
KITTI and the virtual KITTI datasets and focus on the interpolation of depth
maps for driving scenes. The evaluation of the results show good agreement to
the ground truth and are clearly better than results of interpolation by the
nearest neighbor method which disregards statistical information.
Online Learning for Distribution-Free Prediction
Authors: Dave Zachariah, Petre Stoica, Thomas B. Schön
Date: 2017-03-15 00:00:00
Tasks: Prediction

We develop an online learning method for prediction, which is important in
problems with large and/or streaming data sets. We formulate the learning
approach using a covariance-fitting methodology, and show that the resulting
predictor has desirable computational and distribution-free properties: It is
implemented online with a runtime that scales linearly in the number of
samples; has a constant memory requirement; avoids local minima problems; and
prunes away redundant feature dimensions without relying on restrictive
assumptions on the data distribution. In conjunction with the split conformal
approach, it also produces distribution-free prediction confidence intervals in
a computationally efficient manner. The method is demonstrated on both real and
synthetic datasets.
mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions
Authors: Bernd Bischl, Jakob Richter, Jakob Bossek, Daniel Horn, Janek Thomas, Michel Lang
Date: 2017-03-09 00:00:00
Tasks: Bayesian Optimization, regression, SMAC, SMAC+

We present mlrMBO, a flexible and comprehensive R toolbox for model-based
optimization (MBO), also known as Bayesian optimization, which addresses the
problem of expensive black-box optimization by approximating the given
objective function through a surrogate regression model. It is designed for
both single- and multi-objective optimization with mixed continuous,
categorical and conditional parameters. Additional features include multi-point
batch proposal, parallelization, visualization, logging and error-handling.
mlrMBO is implemented in a modular fashion, such that single components can be
easily replaced or adapted by the user for specific use cases, e.g., any
regression learner from the mlr toolbox for machine learning can be used, and
infill criteria and infill optimizers are easily exchangeable. We empirically
demonstrate that mlrMBO provides state-of-the-art performance by comparing it
on different benchmark scenarios against a wide range of other optimizers,
including DiceOptim, rBayesianOptimization, SPOT, SMAC, Spearmint, and
Hyperopt.
Multi-Residual Networks: Improving the Speed and Accuracy of Residual Networks
Authors: Masoud Abdi, Saeid Nahavandi
Date: 2016-09-19 00:00:00

In this article, we take one step toward understanding the learning behavior
of deep residual networks, and supporting the observation that deep residual
networks behave like ensembles. We propose a new convolutional neural network
architecture which builds upon the success of residual networks by explicitly
exploiting the interpretation of very deep networks as an ensemble. The
proposed multi-residual network increases the number of residual functions in
the residual blocks. Our architecture generates models that are wider, rather
than deeper, which significantly improves accuracy. We show that our model
achieves an error rate of 3.73% and 19.45% on CIFAR-10 and CIFAR-100
respectively, that outperforms almost all of the existing models. We also
demonstrate that our model outperforms very deep residual networks by 0.22%
(top-1 error) on the full ImageNet 2012 classification dataset. Additionally,
inspired by the parallel structure of multi-residual networks, a model
parallelism technique has been investigated. The model parallelism method
distributes the computation of residual blocks among the processors, yielding
up to 15% computational complexity improvement.