pwc-archive/papers-with-abstracts huggingface dataset:
Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective
Authors: Shebuti Rayana, Wen Zhong, Leman Akoglu
Date: 2016-09-18 00:00:00
Tasks: Binary Classification, Clustering, Ensemble Learning, General Classification, Outlier Detection, outlier ensembles

Ensemble methods for classification and clustering have been effectively used
for decades, while ensemble learning for outlier detection has only been
studied recently. In this work, we design a new ensemble approach for outlier
detection in multi-dimensional point data, which provides improved accuracy by
reducing error through both bias and variance. Although classification and
outlier detection appear as different problems, their theoretical underpinnings
are quite similar in terms of the bias-variance trade-off [1], where outlier
detection is considered as a binary classification task with unobserved labels
but a similar bias-variance decomposition of error.
  In this paper, we propose a sequential ensemble approach called CARE that
employs a two-phase aggregation of the intermediate results in each iteration
to reach the final outcome. Unlike existing outlier ensembles which solely
incorporate a parallel framework by aggregating the outcomes of independent
base detectors to reduce variance, our ensemble incorporates both the parallel
and sequential building blocks to reduce bias as well as variance by ($i$)
successively eliminating outliers from the original dataset to build a better
data model on which outlierness is estimated (sequentially), and ($ii$)
combining the results from individual base detectors and across iterations
(parallelly). Through extensive experiments on sixteen real-world datasets
mainly from the UCI machine learning repository [2], we show that CARE performs
significantly better than or at least similar to the individual baselines. We
also compare CARE with the state-of-the-art outlier ensembles where it also
provides significant improvement when it is the winner and remains close
otherwise.
Learning camera viewpoint using CNN to improve 3D body pose estimation
Authors: Mona Fathollahi Ghezelghieh, Rangachar Kasturi, Sudeep Sarkar
Date: 2016-09-18 00:00:00
Tasks: 3D Pose Estimation, Pose Estimation

The objective of this work is to estimate 3D human pose from a single RGB
image. Extracting image representations which incorporate both spatial relation
of body parts and their relative depth plays an essential role in accurate3D
pose reconstruction. In this paper, for the first time, we show that camera
viewpoint in combination to 2D joint lo-cations significantly improves 3D pose
accuracy without the explicit use of perspective geometry mathematical
models.To this end, we train a deep Convolutional Neural Net-work (CNN) to
learn categorical camera viewpoint. To make the network robust against clothing
and body shape of the subject in the image, we utilized 3D computer rendering
to synthesize additional training images. We test our framework on the largest
3D pose estimation bench-mark, Human3.6m, and achieve up to 20% error reduction
compared to the state-of-the-art approaches that do not use body part
segmentation.
Optimizing positional scoring rules for rank aggregation
Authors: Ioannis Caragiannis, Xenophon Chatzigeorgiou, George A. Krimpas, Alexandros A. Voudouris
Date: 2016-09-18 00:00:00
Tasks: scoring rule

Nowadays, several crowdsourcing projects exploit social choice methods for
computing an aggregate ranking of alternatives given individual rankings
provided by workers. Motivated by such systems, we consider a setting where
each worker is asked to rank a fixed (small) number of alternatives and, then,
a positional scoring rule is used to compute the aggregate ranking. Among the
apparently infinite such rules, what is the best one to use? To answer this
question, we assume that we have partial access to an underlying true ranking.
Then, the important optimization problem to be solved is to compute the
positional scoring rule whose outcome, when applied to the profile of
individual rankings, is as close as possible to the part of the underlying true
ranking we know. We study this fundamental problem from a theoretical viewpoint
and present positive and negative complexity results and, furthermore,
complement our theoretical findings with experiments on real-world and
synthetic data.
Self-Paced Learning: an Implicit Regularization Perspective
Authors: Yanbo Fan, Ran He, Jian Liang, Bao-Gang Hu
Date: 2016-06-01 00:00:00

Self-paced learning (SPL) mimics the cognitive mechanism of humans and
animals that gradually learns from easy to hard samples. One key issue in SPL
is to obtain better weighting strategy that is determined by minimizer
function. Existing methods usually pursue this by artificially designing the
explicit form of SPL regularizer. In this paper, we focus on the minimizer
function, and study a group of new regularizer, named self-paced implicit
regularizer that is deduced from robust loss function. Based on the convex
conjugacy theory, the minimizer function for self-paced implicit regularizer
can be directly learned from the latent loss function, while the analytic form
of the regularizer can be even known. A general framework (named SPL-IR) for
SPL is developed accordingly. We demonstrate that the learning procedure of
SPL-IR is associated with latent robust loss functions, thus can provide some
theoretical inspirations for its working mechanism. We further analyze the
relation between SPL-IR and half-quadratic optimization. Finally, we implement
SPL-IR to both supervised and unsupervised tasks, and experimental results
corroborate our ideas and demonstrate the correctness and effectiveness of
implicit regularizers.
Deep Survival Analysis
Authors: Rajesh Ranganath, Adler Perotte, Noémie Elhadad, David Blei
Date: 2016-08-06 00:00:00
Tasks: Survival Analysis

The electronic health record (EHR) provides an unprecedented opportunity to
build actionable tools to support physicians at the point of care. In this
paper, we investigate survival analysis in the context of EHR data. We
introduce deep survival analysis, a hierarchical generative approach to
survival analysis. It departs from previous approaches in two primary ways: (1)
all observations, including covariates, are modeled jointly conditioned on a
rich latent structure; and (2) the observations are aligned by their failure
time, rather than by an arbitrary time zero as in traditional survival
analysis. Further, it (3) scalably handles heterogeneous (continuous and
discrete) data types that occur in the EHR. We validate deep survival analysis
model by stratifying patients according to risk of developing coronary heart
disease (CHD). Specifically, we study a dataset of 313,000 patients
corresponding to 5.5 million months of observations. When compared to the
clinically validated Framingham CHD risk score, deep survival analysis is
significantly superior in stratifying patients according to their risk.
Set-Point Regulation of Linear Continuous-Time Systems using Neuromorphic Vision Sensors
Authors: Prince Singh, Sze Zheng Yong, Emilio Frazzoli
Date: 2016-09-18 00:00:00

Recently developed neuromorphic vision sensors have become promising
candidates for agile and autonomous robotic applications primarily due to, in
particular, their high temporal resolution and low latency. Each pixel of this
sensor independently fires an asynchronous stream of "retinal events" once a
change in the light field is detected. Existing computer vision algorithms can
only process periodic frames and so a new class of algorithms needs to be
developed that can efficiently process these events for control tasks. In this
paper, we investigate the problem of regulating a continuous-time linear time
invariant (LTI) system to a desired point using measurements from a
neuromorphic sensor. We present an $H_\infty$ controller that regulates the LTI
system to a desired set-point and provide the set of neuromorphic sensor based
cameras for the given system that fulfill the regulation task. The
effectiveness of our approach is illustrated on an unstable system.
Consistent Discretization and Minimization of the L1 Norm on Manifolds
Authors: Alex Bronstein, Yoni Choukroun, Ron Kimmel, Matan Sela
Date: 2016-09-18 00:00:00

The L1 norm has been tremendously popular in signal and image processing in
the past two decades due to its sparsity-promoting properties. More recently,
its generalization to non-Euclidean domains has been found useful in shape
analysis applications. For example, in conjunction with the minimization of the
Dirichlet energy, it was shown to produce a compactly supported quasi-harmonic
orthonormal basis, dubbed as compressed manifold modes. The continuous L1 norm
on the manifold is often replaced by the vector l1 norm applied to sampled
functions. We show that such an approach is incorrect in the sense that it does
not consistently discretize the continuous norm and warn against its
sensitivity to the specific sampling. We propose two alternative
discretizations resulting in an iteratively-reweighed l2 norm. We demonstrate
the proposed strategy on the compressed modes problem, which reduces to a
sequence of simple eigendecomposition problems not requiring non-convex
optimization on Stiefel manifolds and producing more stable and accurate
results.
Pose from Action: Unsupervised Learning of Pose Features based on Motion
Authors: Senthil Purushwalkam, Abhinav Gupta
Date: 2016-09-18 00:00:00
Tasks: Action Recognition, Action Recognition In Videos, Optical Flow Estimation, Pose Estimation, Temporal Action Localization

Human actions are comprised of a sequence of poses. This makes videos of
humans a rich and dense source of human poses. We propose an unsupervised
method to learn pose features from videos that exploits a signal which is
complementary to appearance and can be used as supervision: motion. The key
idea is that humans go through poses in a predictable manner while performing
actions. Hence, given two poses, it should be possible to model the motion that
caused the change between them. We represent each of the poses as a feature in
a CNN (Appearance ConvNet) and generate a motion encoding from optical flow
maps using a separate CNN (Motion ConvNet). The data for this task is
automatically generated allowing us to train without human supervision. We
demonstrate the strength of the learned representation by finetuning the
trained model for Pose Estimation on the FLIC dataset, for static image action
recognition on PASCAL and for action recognition in videos on UCF101 and
HMDB51.
Medical image denoising using convolutional denoising autoencoders
Authors: Lovedeep Gondara
Date: 2016-08-16 00:00:00
Tasks: Denoising, Image Denoising, Medical Image Analysis, Medical Image Denoising

Image denoising is an important pre-processing step in medical image
analysis. Different algorithms have been proposed in past three decades with
varying denoising performances. More recently, having outperformed all
conventional methods, deep learning based models have shown a great promise.
These methods are however limited for requirement of large training sample size
and high computational costs. In this paper we show that using small sample
size, denoising autoencoders constructed using convolutional layers can be used
for efficient denoising of medical images. Heterogeneous images can be combined
to boost sample size for increased denoising performance. Simplest of networks
can reconstruct images with corruption levels so high that noise and signal are
not differentiable to human eye.
mlr Tutorial
Authors: Julia Schiffner, Bernd Bischl, Michel Lang, Jakob Richter, Zachary M. Jones, Philipp Probst, Florian Pfisterer, Mason Gallo, Dominik Kirchhoff, Tobias Kühn, Janek Thomas, Lars Kotthoff
Date: 2016-09-18 00:00:00
Tasks: BIG-bench Machine Learning

This document provides and in-depth introduction to the mlr framework for
machine learning experiments in R.
Applications of Data Mining (DM) in Science and Engineering: State of the art and perspectives
Authors: Jose A. García Gutiérrez
Date: 2016-09-17 00:00:00
Tasks: Cloud Computing

The continuous increase in the availability of data of any kind, coupled with
the development of networks of high-speed communications, the popularization of
cloud computing and the growth of data centers and the emergence of
high-performance computing does essential the task to develop techniques that
allow more efficient data processing and analyzing of large volumes datasets
and extraction of valuable information. In the following pages we will discuss
about development of this field in recent decades, and its potential and
applicability present in the various branches of scientific research. Also, we
try to review briefly the different families of algorithms that are included in
data mining research area, its scalability with increasing dimensionality of
the input data and how they can be addressed and what behavior different
methods in a scenario in which the information is distributed or decentralized
processed so as to increment performance optimization in heterogeneous
environments.
A Deep Metric for Multimodal Registration
Authors: Martin Simonovsky, Benjamín Gutiérrez-Becker, Diana Mateus, Nassir Navab, Nikos Komodakis
Date: 2016-09-17 00:00:00

Multimodal registration is a challenging problem in medical imaging due the
high variability of tissue appearance under different imaging modalities. The
crucial component here is the choice of the right similarity measure. We make a
step towards a general learning-based solution that can be adapted to specific
situations and present a metric based on a convolutional neural network. Our
network can be trained from scratch even from a few aligned image pairs. The
metric is validated on intersubject deformable registration on a dataset
different from the one used for training, demonstrating good generalization. In
this task, we outperform mutual information by a significant margin.
Predicting Future Shanghai Stock Market Price using ANN in the Period 21-Sep-2016 to 11-Oct-2016
Authors: Barack Wamkaya Wanjawa
Date: 2016-09-17 00:00:00
Tasks: Stock Prediction, Time Series, Time Series Analysis

Predicting the prices of stocks at any stock market remains a quest for many
investors and researchers. Those who trade at the stock market tend to use
technical, fundamental or time series analysis in their predictions. These
methods usually guide on trends and not the exact likely prices. It is for this
reason that Artificial Intelligence systems, such as Artificial Neural Network,
that is feedforward multi-layer perceptron with error backpropagation, can be
used for such predictions. A difficulty in neural network application is the
determination of suitable network parameters. A previous research by the author
already determined the network parameters as 5:21:21:1 with 80% training data
or 4-year of training data as a good enough model for stock prediction. This
model has been put to the test in predicting selected Shanghai Stock Exchange
stocks in the future period of 21-Sep-2016 to 11-Oct-2016, about one week after
the publication of these predictions. The research aims at confirming that
simple neural network systems can be quite powerful in typical stock market
predictions.
ADAGIO: Fast Data-aware Near-Isometric Linear Embeddings
Authors: Jarosław Błasiok, Charalampos E. Tsourakakis
Date: 2016-09-17 00:00:00
Tasks: Computational Efficiency, Dimensionality Reduction, parameter estimation

Many important applications, including signal reconstruction, parameter
estimation, and signal processing in a compressed domain, rely on a
low-dimensional representation of the dataset that preserves {\em all} pairwise
distances between the data points and leverages the inherent geometric
structure that is typically present. Recently Hedge, Sankaranarayanan, Yin and
Baraniuk \cite{hedge2015} proposed the first data-aware near-isometric linear
embedding which achieves the best of both worlds. However, their method NuMax
does not scale to large-scale datasets.
  Our main contribution is a simple, data-aware, near-isometric linear
dimensionality reduction method which significantly outperforms a
state-of-the-art method \cite{hedge2015} with respect to scalability while
achieving high quality near-isometries. Furthermore, our method comes with
strong worst-case theoretical guarantees that allow us to guarantee the quality
of the obtained near-isometry. We verify experimentally the efficiency of our
method on numerous real-world datasets, where we find that our method ($<$10
secs) is more than 3\,000$\times$ faster than the state-of-the-art method
\cite{hedge2015} ($>$9 hours) on medium scale datasets with 60\,000 data points
in 784 dimensions. Finally, we use our method as a preprocessing step to
increase the computational efficiency of a classification application and for
speeding up approximate nearest neighbor queries.
Solving the Wastewater Treatment Plant Problem with SMT
Authors: Miquel Bofill, Víctor Muñoz, Javier Murillo
Date: 2016-09-17 00:00:00
Tasks: Scheduling

In this paper we introduce the Wastewater Treatment Plant Problem, a
real-world scheduling problem, and compare the performance of several tools on
it. We show that, for a naive modeling, state-of-the-art SMT solvers outperform
other tools ranging from mathematical programming to constraint programming. We
use both real and randomly generated benchmarks.
  From this and similar results, we claim for the convenience of developing
compiler front-ends being able to translate from constraint programming
languages to the SMT-LIB standard language.
No-Regret Learnability for Piecewise Linear Losses
Authors: Arthur Flajolet, Patrick Jaillet
Date: 2014-11-20 00:00:00

In the convex optimization approach to online regret minimization, many
methods have been developed to guarantee a $O(\sqrt{T})$ bound on regret for
subdifferentiable convex loss functions with bounded subgradients, by using a
reduction to linear loss functions. This suggests that linear loss functions
tend to be the hardest ones to learn against, regardless of the underlying
decision spaces. We investigate this question in a systematic fashion looking
at the interplay between the set of possible moves for both the decision maker
and the adversarial environment. This allows us to highlight sharp distinctive
behaviors about the learnability of piecewise linear loss functions. On the one
hand, when the decision set of the decision maker is a polyhedron, we establish
$\Omega(\sqrt{T})$ lower bounds on regret for a large class of piecewise linear
loss functions with important applications in online linear optimization,
repeated zero-sum Stackelberg games, online prediction with side information,
and online two-stage optimization. On the other hand, we exhibit $o(\sqrt{T})$
learning rates, achieved by the Follow-The-Leader algorithm, in online linear
optimization when the boundary of the decision maker's decision set is curved
and when $0$ does not lie in the convex hull of the environment's decision set.
Hence, the curvature of the decision maker's decision set is a determining
factor for the optimal learning rate. These results hold in a completely
adversarial setting.
Leveraging Environmental Correlations: The Thermodynamics of Requisite Variety
Authors: Alexander B. Boyd, Dibyendu Mandal, James P. Crutchfield
Date: 2016-09-17 00:00:00

Key to biological success, the requisite variety that confronts an adaptive
organism is the set of detectable, accessible, and controllable states in its
environment. We analyze its role in the thermodynamic functioning of
information ratchets---a form of autonomous Maxwellian Demon capable of
exploiting fluctuations in an external information reservoir to harvest useful
work from a thermal bath. This establishes a quantitative paradigm for
understanding how adaptive agents leverage structured thermal environments for
their own thermodynamic benefit. General ratchets behave as memoryful
communication channels, interacting with their environment sequentially and
storing results to an output. The bulk of thermal ratchets analyzed to date,
however, assume memoryless environments that generate input signals without
temporal correlations. Employing computational mechanics and a new
information-processing Second Law of Thermodynamics (IPSL) we remove these
restrictions, analyzing general finite-state ratchets interacting with
structured environments that generate correlated input signals. On the one
hand, we demonstrate that a ratchet need not have memory to exploit an
uncorrelated environment. On the other, and more appropriate to biological
adaptation, we show that a ratchet must have memory to most effectively
leverage structure and correlation in its environment. The lesson is that to
optimally harvest work a ratchet's memory must reflect the input generator's
memory. Finally, we investigate achieving the IPSL bounds on the amount of work
a ratchet can extract from its environment, discovering that finite-state,
optimal ratchets are unable to reach these bounds. In contrast, we show that
infinite-state ratchets can go well beyond these bounds by utilizing their own
infinite "negentropy". We conclude with an outline of the collective
thermodynamics of information-ratchet swarms.
Using Recurrent Neural Networks to Optimize Dynamical Decoupling for Quantum Memory
Authors: Moritz August, Xiaotong Ni
Date: 2016-04-01 00:00:00

We utilize machine learning models which are based on recurrent neural
networks to optimize dynamical decoupling (DD) sequences. DD is a relatively
simple technique for suppressing the errors in quantum memory for certain noise
models. In numerical simulations, we show that with minimum use of prior
knowledge and starting from random sequences, the models are able to improve
over time and eventually output DD-sequences with performance better than that
of the well known DD-families. Furthermore, our algorithm is easy to implement
in experiments to find solutions tailored to the specific hardware, as it
treats the figure of merit as a black box.
Fast and Effective Algorithms for Symmetric Nonnegative Matrix Factorization
Authors: Reza Borhani, Jeremy Watt, Aggelos Katsaggelos
Date: 2016-09-17 00:00:00
Tasks: Clustering

Symmetric Nonnegative Matrix Factorization (SNMF) models arise naturally as
simple reformulations of many standard clustering algorithms including the
popular spectral clustering method. Recent work has demonstrated that an
elementary instance of SNMF provides superior clustering quality compared to
many classic clustering algorithms on a variety of synthetic and real world
data sets. In this work, we present novel reformulations of this instance of
SNMF based on the notion of variable splitting and produce two fast and
effective algorithms for its optimization using i) the provably convergent
Accelerated Proximal Gradient (APG) procedure and ii) a heuristic version of
the Alternating Direction Method of Multipliers (ADMM) framework. Our two
algorithms present an interesting tradeoff between computational speed and
mathematical convergence guarantee: while the former method is provably
convergent it is considerably slower than the latter approach, for which we
also provide significant but less stringent mathematical proof regarding its
convergence. Through extensive experiments we show not only that the efficacy
of these approaches is equal to that of the state of the art SNMF algorithm,
but also that the latter of our algorithms is extremely fast being one to two
orders of magnitude faster in terms of total computation time than the state of
the art approach, outperforming even spectral clustering in terms of
computation time on large data sets.