pwc-archive/papers-with-abstracts huggingface dataset:
ARREST: A RSSI Based Approach for Mobile Sensing and Tracking of a Moving Object
Authors: Pradipta Ghosh, Jason A. Tran, Bhaskar Krishnamachari
Date: 2017-07-18 00:00:00
Tasks: Position

We present Autonomous Rssi based RElative poSitioning and Tracking (ARREST),
a new robotic sensing system for tracking and following a moving, RF-emitting
object, which we refer to as the Leader, solely based on signal strength
information. This kind of system can expand the horizon of autonomous mobile
tracking and distributed robotics into many scenarios with limited visibility
such as nighttime, dense forests, and cluttered environments. Our proposed
tracking agent, which we refer to as the TrackBot, uses a single rotating,
off-the-shelf, directional antenna, novel angle and relative speed estimation
algorithms, and Kalman filtering to continually estimate the relative position
of the Leader with decimeter level accuracy (which is comparable to a
state-of-the-art multiple access point based RF-localization system) and the
relative speed of the Leader with accuracy on the order of 1 m/s. The TrackBot
feeds the relative position and speed estimates into a Linear Quadratic
Gaussian (LQG) controller to generate a set of control outputs to control the
orientation and the movement of the TrackBot. We perform an extensive set of
real world experiments with a full-fledged prototype to demonstrate that the
TrackBot is able to stay within 5m of the Leader with: (1) more than $99\%$
probability in line of sight scenarios, and (2) more than $70\%$ probability in
no line of sight scenarios, when it moves 1.8X faster than the Leader. For
ground truth estimation in real world experiments, we also developed an
integrated TDoA based distance and angle estimation system with centimeter
level localization accuracy in line of sight scenarios. While providing a first
proof of concept, our work opens the door to future research aimed at further
improvements of autonomous RF-based tracking.
Learning Hidden Quantum Markov Models
Authors: Siddarth Srinivasan, Geoff Gordon, Byron Boots
Date: 2017-10-24 00:00:00

Hidden Quantum Markov Models (HQMMs) can be thought of as quantum
probabilistic graphical models that can model sequential data. We extend
previous work on HQMMs with three contributions: (1) we show how classical
hidden Markov models (HMMs) can be simulated on a quantum circuit, (2) we
reformulate HQMMs by relaxing the constraints for modeling HMMs on quantum
circuits, and (3) we present a learning algorithm to estimate the parameters of
an HQMM from data. While our algorithm requires further optimization to handle
larger datasets, we are able to evaluate our algorithm using several synthetic
datasets. We show that on HQMM generated data, our algorithm learns HQMMs with
the same number of hidden states and predictive accuracy as the true HQMMs,
while HMMs learned with the Baum-Welch algorithm require more states to match
the predictive accuracy.
Learning how to explain neural networks: PatternNet and PatternAttribution
Authors: Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, Sven Dähne
Date: 2017-05-16 00:00:00
Proceeding: ICLR 2018 1

DeConvNet, Guided BackProp, LRP, were invented to better understand deep
neural networks. We show that these methods do not produce the theoretically
correct explanation for a linear model. Yet they are used on multi-layer
networks with millions of parameters. This is a cause for concern since linear
models are simple neural networks. We argue that explanation methods for neural
nets should work reliably in the limit of simplicity, the linear models. Based
on our analysis of linear models we propose a generalization that yields two
explanation techniques (PatternNet and PatternAttribution) that are
theoretically sound for linear models and produce improved explanations for
deep networks.
An Energy-Efficient Mixed-Signal Neuron for Inherently Error-Resilient Neuromorphic Systems
Authors: Baibhab Chatterjee, Priyadarshini Panda, Shovan Maity, Kaushik Roy, Shreyas Sen
Date: 2017-10-24 00:00:00

This work presents the design and analysis of a mixed-signal neuron (MS-N)
for convolutional neural networks (CNN) and compares its performance with a
digital neuron (Dig-N) in terms of operating frequency, power and noise. The
circuit-level implementation of the MS-N in 65 nm CMOS technology exhibits 2-3
orders of magnitude better energy-efficiency over Dig-N for neuromorphic
computing applications - especially at low frequencies due to the high leakage
currents from many transistors in Dig-N. The inherent error-resiliency of CNN
is exploited to handle the thermal and flicker noise of MS-N. A system-level
analysis using a cohesive circuit-algorithmic framework on MNIST and CIFAR-10
datasets demonstrate an increase of 3% in worst-case classification error for
MNIST when the integrated noise power in the bandwidth is ~ 1 {\mu}V2.
A Sequential Approximation Framework for Coded Distributed Optimization
Authors: Jingge Zhu, Ye Pu, Vipul Gupta, Claire Tomlin, Kannan Ramchandran
Date: 2017-10-24 00:00:00
Tasks: Distributed Optimization

Building on the previous work of Lee et al. and Ferdinand et al. on coded
computation, we propose a sequential approximation framework for solving
optimization problems in a distributed manner. In a distributed computation
system, latency caused by individual processors ("stragglers") usually causes a
significant delay in the overall process. The proposed method is powered by a
sequential computation scheme, which is designed specifically for systems with
stragglers. This scheme has the desirable property that the user is guaranteed
to receive useful (approximate) computation results whenever a processor
finishes its subtask, even in the presence of uncertain latency. In this paper,
we give a coding theorem for sequentially computing matrix-vector
multiplications, and the optimality of this coding scheme is also established.
As an application of the results, we demonstrate solving optimization problems
using a sequential approximation approach, which accelerates the algorithm in a
distributed system with stragglers.
Multi-Armed Bandits with Metric Movement Costs
Authors: Tomer Koren, Roi Livni, Yishay Mansour
Date: 2017-10-24 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Multi-Armed Bandits

We consider the non-stochastic Multi-Armed Bandit problem in a setting where
there is a fixed and known metric on the action space that determines a cost
for switching between any pair of actions. The loss of the online learner has
two components: the first is the usual loss of the selected actions, and the
second is an additional loss due to switching between actions. Our main
contribution gives a tight characterization of the expected minimax regret in
this setting, in terms of a complexity measure $\mathcal{C}$ of the underlying
metric which depends on its covering numbers. In finite metric spaces with $k$
actions, we give an efficient algorithm that achieves regret of the form
$\widetilde{O}(\max\{\mathcal{C}^{1/3}T^{2/3},\sqrt{kT}\})$, and show that this
is the best possible. Our regret bound generalizes previous known regret bounds
for some special cases: (i) the unit-switching cost regret
$\widetilde{\Theta}(\max\{k^{1/3}T^{2/3},\sqrt{kT}\})$ where
$\mathcal{C}=\Theta(k)$, and (ii) the interval metric with regret
$\widetilde{\Theta}(\max\{T^{2/3},\sqrt{kT}\})$ where $\mathcal{C}=\Theta(1)$.
For infinite metrics spaces with Lipschitz loss functions, we derive a tight
regret bound of $\widetilde{\Theta}(T^{\frac{d+1}{d+2}})$ where $d \ge 1$ is
the Minkowski dimension of the space, which is known to be tight even when
there are no switching costs.
Multi-focus image fusion using VOL and EOL in DCT domain
Authors: Mostafa Amin-Naji, Ali Aghagolzadeh
Date: 2017-10-17 00:00:00
Tasks: Multi Focus Image Fusion

The purpose of multi-focus image fusion is gathering the essential
information and the focused parts from the input multi-focus images into a
single image. These multi-focused images are captured with different depths of
focus of cameras. Multi-focus image fusion is very time-saving and appropriate
in discrete cosine transform (DCT) domain, especially when JPEG images are used
in visual sensor networks (VSN). The previous works in DCT domain have some
errors in selection of the suitable divided blocks according to their criterion
for measurement of the block contrast. In this paper, we used variance of
Laplacian (VOL) and energy of Laplacian (EOL) as criterion to measure the
contrast of image. Also in this paper, the EOL and VOL calculations directly in
DCT domain are prepared using vector processing. We developed four matrices
which calculate the Laplacian of block easily in DCT domain. Our works greatly
reduce error due to unsuitable block selection. The results of the proposed
algorithms are compared with the previous algorithms in order to demonstrate
the superiority of the output image quality in the proposed methods. The
several JPEG multi-focus images are used in experiments and their fused image
by our proposed methods and the other algorithms are compared with different
measurement criteria.
Scalable Exact Parent Sets Identification in Bayesian Networks Learning with Apache Spark
Authors: Subhadeep Karan, Jaroslaw Zola
Date: 2017-05-18 00:00:00
Tasks: Fraud Detection

In Machine Learning, the parent set identification problem is to find a set
of random variables that best explain selected variable given the data and some
predefined scoring function. This problem is a critical component to structure
learning of Bayesian networks and Markov blankets discovery, and thus has many
practical applications, ranging from fraud detection to clinical decision
support. In this paper, we introduce a new distributed memory approach to the
exact parent sets assignment problem. To achieve scalability, we derive
theoretical bounds to constraint the search space when MDL scoring function is
used, and we reorganize the underlying dynamic programming such that the
computational density is increased and fine-grain synchronization is
eliminated. We then design efficient realization of our approach in the Apache
Spark platform. Through experimental results, we demonstrate that the method
maintains strong scalability on a 500-core standalone Spark cluster, and it can
be used to efficiently process data sets with 70 variables, far beyond the
reach of the currently available solutions.
Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention
Authors: Hideyuki Tachibana, Katsuya Uenoyama, Shunsuke Aihara
Date: 2017-10-24 00:00:00
Tasks: text-to-speech, Text to Speech, Text-To-Speech Synthesis

This paper describes a novel text-to-speech (TTS) technique based on deep convolutional neural networks (CNN), without use of any recurrent units. Recurrent neural networks (RNN) have become a standard technique to model sequential data recently, and this technique has been used in some cutting-edge neural TTS techniques. However, training RNN components often requires a very powerful computer, or a very long time, typically several days or weeks. Recent other studies, on the other hand, have shown that CNN-based sequence synthesis can be much faster than RNN-based techniques, because of high parallelizability. The objective of this paper is to show that an alternative neural TTS based only on CNN alleviate these economic costs of training. In our experiment, the proposed Deep Convolutional TTS was sufficiently trained overnight (15 hours), using an ordinary gaming PC equipped with two GPUs, while the quality of the synthesized speech was almost acceptable.
Scaling Text with the Class Affinity Model
Authors: Patrick O. Perry, Kenneth Benoit
Date: 2017-10-24 00:00:00
Tasks: model, Sentence

Probabilistic methods for classifying text form a rich tradition in machine
learning and natural language processing. For many important problems, however,
class prediction is uninteresting because the class is known, and instead the
focus shifts to estimating latent quantities related to the text, such as
affect or ideology. We focus on one such problem of interest, estimating the
ideological positions of 55 Irish legislators in the 1991 D\'ail confidence
vote. To solve the D\'ail scaling problem and others like it, we develop a text
modeling framework that allows actors to take latent positions on a "gray"
spectrum between "black" and "white" polar opposites. We are able to validate
results from this model by measuring the influences exhibited by individual
words, and we are able to quantify the uncertainty in the scaling estimates by
using a sentence-level block bootstrap. Applying our method to the D\'ail
debate, we are able to scale the legislators between extreme pro-government and
pro-opposition in a way that reveals nuances in their speeches not captured by
their votes or party affiliations.
Estimating the Operating Characteristics of Ensemble Methods
Authors: Anthony Gamst, Jay-Calvin Reyes, Alden Walker
Date: 2017-10-24 00:00:00

In this paper we present a technique for using the bootstrap to estimate the
operating characteristics and their variability for certain types of ensemble
methods. Bootstrapping a model can require a huge amount of work if the
training data set is large. Fortunately in many cases the technique lets us
determine the effect of infinite resampling without actually refitting a single
model. We apply the technique to the study of meta-parameter selection for
random forests. We demonstrate that alternatives to bootstrap aggregation and
to considering \sqrt{d} features to split each node, where d is the number of
features, can produce improvements in predictive accuracy.
Curvature-aided Incremental Aggregated Gradient Method
Authors: Hoi-To Wai, Wei Shi, Angelia Nedic, Anna Scaglione
Date: 2017-10-24 00:00:00

We propose a new algorithm for finite sum optimization which we call the
curvature-aided incremental aggregated gradient (CIAG) method. Motivated by the
problem of training a classifier for a d-dimensional problem, where the number
of training data is $m$ and $m \gg d \gg 1$, the CIAG method seeks to
accelerate incremental aggregated gradient (IAG) methods using aids from the
curvature (or Hessian) information, while avoiding the evaluation of matrix
inverses required by the incremental Newton (IN) method. Specifically, our idea
is to exploit the incrementally aggregated Hessian matrix to trace the full
gradient vector at every incremental step, therefore achieving an improved
linear convergence rate over the state-of-the-art IAG methods. For strongly
convex problems, the fast linear convergence rate requires the objective
function to be close to quadratic, or the initial point to be close to optimal
solution. Importantly, we show that running one iteration of the CIAG method
yields the same improvement to the optimality gap as running one iteration of
the full gradient method, while the complexity is $O(d^2)$ for CIAG and $O(md)$
for the full gradient. Overall, the CIAG method strikes a balance between the
high computation complexity incremental Newton-type methods and the slow IAG
method. Our numerical results support the theoretical findings and show that
the CIAG method often converges with much fewer iterations than IAG, and
requires much shorter running time than IN when the problem dimension is high.
Pre-Processing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learning
Authors: Pei Cao, Shengli Zhang, Jiong Tang
Date: 2017-10-24 00:00:00
Tasks: Fault Diagnosis, Transfer Learning

Early fault diagnosis in complex mechanical systems such as gearbox has
always been a great challenge, even with the recent development in deep neural
networks. The performance of a classic fault diagnosis system predominantly
depends on the features extracted and the classifier subsequently applied.
Although a large number of attempts have been made regarding feature extraction
techniques, the methods require great human involvements are heavily depend on
domain expertise and may thus be non-representative and biased from application
to application. On the other hand, while the deep neural networks based
approaches feature adaptive feature extractions and inherent classifications,
they usually require a substantial set of training data and thus hinder their
usage for engineering applications with limited training data such as gearbox
fault diagnosis. This paper develops a deep convolutional neural network-based
transfer learning approach that not only entertains pre-processing free
adaptive feature extractions, but also requires only a small set of training
data. The proposed approach performs gear fault diagnosis using pre-processing
free raw accelerometer data and experiments with various sizes of training data
were conducted. The superiority of the proposed approach is revealed by
comparing the performance with other methods such as locally trained
convolution neural network and angle-frequency analysis based support vector
machine. The achieved accuracy indicates that the proposed approach is not only
viable and robust, but also has the potential to be readily applicable to other
fault diagnosis practices.
Calibration of Machine Learning Classifiers for Probability of Default Modelling
Authors: Pedro G. Fonseca, Hugo D. Lopes
Date: 2017-10-24 00:00:00
Tasks: BIG-bench Machine Learning, Binary Classification, General Classification, regression, Time Series, Time Series Analysis

Binary classification is highly used in credit scoring in the estimation of
probability of default. The validation of such predictive models is based both
on rank ability, and also on calibration (i.e. how accurately the probabilities
output by the model map to the observed probabilities). In this study we cover
the current best practices regarding calibration for binary classification, and
explore how different approaches yield different results on real world credit
scoring data. The limitations of evaluating credit scoring models using only
rank ability metrics are explored. A benchmark is run on 18 real world
datasets, and results compared. The calibration techniques used are Platt
Scaling and Isotonic Regression. Also, different machine learning models are
used: Logistic Regression, Random Forest Classifiers, and Gradient Boosting
Classifiers. Results show that when the dataset is treated as a time series,
the use of re-calibration with Isotonic Regression is able to improve the long
term calibration better than the alternative methods. Using re-calibration, the
non-parametric models are able to outperform the Logistic Regression on Brier
Score Loss.
Conformal predictive distributions with kernels
Authors: Vladimir Vovk, Ilia Nouretdinov, Valery Manokhin, Alex Gammerman
Date: 2017-10-24 00:00:00
Tasks: BIG-bench Machine Learning

This paper reviews the checkered history of predictive distributions in
statistics and discusses two developments, one from recent literature and the
other new. The first development is bringing predictive distributions into
machine learning, whose early development was so deeply influenced by two
remarkable groups at the Institute of Automation and Remote Control. The second
development is combining predictive distributions with kernel methods, which
were originated by one of those groups, including Emmanuel Braverman.
Avoiding Communication in Proximal Methods for Convex Optimization Problems
Authors: Saeed Soori, Aditya Devarakonda, James Demmel, Mert Gurbuzbalaban, Maryam Mehri Dehnavi
Date: 2017-10-24 00:00:00

The fast iterative soft thresholding algorithm (FISTA) is used to solve
convex regularized optimization problems in machine learning. Distributed
implementations of the algorithm have become popular since they enable the
analysis of large datasets. However, existing formulations of FISTA communicate
data at every iteration which reduces its performance on modern distributed
architectures. The communication costs of FISTA, including bandwidth and
latency costs, is closely tied to the mathematical formulation of the
algorithm. This work reformulates FISTA to communicate data at every k
iterations and reduce data communication when operating on large data sets. We
formulate the algorithm for two different optimization methods on the Lasso
problem and show that the latency cost is reduced by a factor of k while
bandwidth and floating-point operation costs remain the same. The convergence
rates and stability properties of the reformulated algorithms are similar to
the standard formulations. The performance of communication-avoiding FISTA and
Proximal Newton methods is evaluated on 1 to 1024 nodes for multiple benchmarks
and demonstrate average speedups of 3-10x with scaling properties that
outperform the classical algorithms.
Classification on Large Networks: A Quantitative Bound via Motifs and Graphons
Authors: Andreas Haupt, Mohammad Khatami, Thomas Schultz, Ngoc Mai Tran
Date: 2017-10-24 00:00:00
Tasks: General Classification

When each data point is a large graph, graph statistics such as densities of
certain subgraphs (motifs) can be used as feature vectors for machine learning.
While intuitive, motif counts are expensive to compute and difficult to work
with theoretically. Via graphon theory, we give an explicit quantitative bound
for the ability of motif homomorphisms to distinguish large networks under both
generative and sampling noise. Furthermore, we give similar bounds for the
graph spectrum and connect it to homomorphism densities of cycles. This results
in an easily computable classifier on graph data with theoretical performance
guarantee. Our method yields competitive results on classification tasks for
the autoimmune disease Lupus Erythematosus.
A deep level set method for image segmentation
Authors: Min Tang, Sepehr Valipour, Zichen Vincent Zhang, Dana Cobzas, MartinJagersand
Date: 2017-05-17 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

This paper proposes a novel image segmentation approachthat integrates fully
convolutional networks (FCNs) with a level setmodel. Compared with a FCN, the
integrated method can incorporatesmoothing and prior information to achieve an
accurate segmentation.Furthermore, different than using the level set model as
a post-processingtool, we integrate it into the training phase to fine-tune the
FCN. Thisallows the use of unlabeled data during training in a
semi-supervisedsetting. Using two types of medical imaging data (liver CT and
left ven-tricle MRI data), we show that the integrated method achieves
goodperformance even when little training data is available, outperformingthe
FCN or the level set model alone.
A Simple Text Analytics Model To Assist Literary Criticism: comparative approach and example on James Joyce against Shakespeare and the Bible
Authors: Renato Fabbri, Luis Henrique Garcia
Date: 2017-10-24 00:00:00
Tasks: Part-Of-Speech Tagging, Sentence

Literary analysis, criticism or studies is a largely valued field with
dedicated journals and researchers which remains mostly within the humanities
scope. Text analytics is the computer-aided process of deriving information
from texts. In this article we describe a simple and generic model for
performing literary analysis using text analytics. The method relies on
statistical measures of: 1) token and sentence sizes and 2) Wordnet synset
features. These measures are then used in Principal Component Analysis where
the texts to be analyzed are observed against Shakespeare and the Bible,
regarded as reference literature. The model is validated by analyzing selected
works from James Joyce (1882-1941), one of the most important writers of the
20th century. We discuss the consistency of this approach, the reasons why we
did not use other techniques (e.g. part-of-speech tagging) and the ways by
which the analysis model might be adapted and enhanced.