pwc-archive/papers-with-abstracts huggingface dataset:
Towards automation of data quality system for CERN CMS experiment
Authors: Maxim Borisyak, Fedor Ratnikov, Denis Derkach, Andrey Ustyuzhanin
Date: 2017-09-25 00:00:00
Tasks: BIG-bench Machine Learning

Daily operation of a large-scale experiment is a challenging task,
particularly from perspectives of routine monitoring of quality for data being
taken. We describe an approach that uses Machine Learning for the automated
system to monitor data quality, which is based on partial use of data qualified
manually by detector experts. The system automatically classifies marginal
cases: both of good an bad data, and use human expert decision to classify
remaining "grey area" cases.
  This study uses collision data collected by the CMS experiment at LHC in
2010. We demonstrate that proposed workflow is able to automatically process at
least 20\% of samples without noticeable degradation of the result.
Muon Trigger for Mobile Phones
Authors: Maxim Borisyak, Michail Usvyatsov, Michael Mulhearn, Chase Shimmin, Andrey Ustyuzhanin
Date: 2017-09-25 00:00:00

The CRAYFIS experiment proposes to use privately owned mobile phones as a
ground detector array for Ultra High Energy Cosmic Rays. Upon interacting with
Earth's atmosphere, these events produce extensive particle showers which can
be detected by cameras on mobile phones. A typical shower contains
minimally-ionizing particles such as muons. As these particles interact with
CMOS image sensors, they may leave tracks of faintly-activated pixels that are
sometimes hard to distinguish from random detector noise. Triggers that rely on
the presence of very bright pixels within an image frame are not efficient in
this case.
  We present a trigger algorithm based on Convolutional Neural Networks which
selects images containing such tracks and are evaluated in a lazy manner: the
response of each successive layer is computed only if activation of the current
layer satisfies a continuation criterion. Usage of neural networks increases
the sensitivity considerably comparable with image thresholding, while the lazy
evaluation allows for execution of the trigger under the limited computational
power of mobile phones.
Texture Classification of MR Images of the Brain in ALS using CoHOG
Authors: G M Mashrur E Elahi, Sanjay Kalra, Yee-Hong Yang
Date: 2017-03-07 00:00:00
Tasks: General Classification, Specificity, Texture Classification

Texture analysis is a well-known research topic in computer vision and image
processing and has many applications. Gradient-based texture methods have
become popular in classification problems. For the first time we extend a
well-known gradient-based method, Co-occurrence Histograms of Oriented
Gradients (CoHOG) to extract texture features from 2D Magnetic Resonance Images
(MRI). Unlike the original CoHOG method, we use the whole image instead of
sub-regions for feature calculation. Also, we use a larger neighborhood size.
Gradient orientations of the image pixels are calculated using Sobel, Gaussian
Derivative (GD) and Local Frequency Descriptor Gradient (LFDG) operators. The
extracted feature vector size is very large and classification using a large
number of similar features does not provide the best results. In our proposed
method, for the first time to our best knowledge, only a minimum number of
significant features are selected using area under the receiver operator
characteristic (ROC) curve (AUC) thresholds with <= 0.01. In this paper, we
apply the proposed method to classify Amyotrophic Lateral Sclerosis (ALS)
patients from the controls. It is observed that selected texture features from
downsampled images are significantly different between patients and controls.
These features are used in a linear support vector machine (SVM) classifier to
determine the classification accuracy. Optimal sensitivity and specificity are
also calculated. Three different cohort datasets are used in the experiments.
The performance of the proposed method using three gradient operators and two
different neighborhood sizes is analyzed. Region based analysis is performed to
demonstrate that significant changes between patients and controls are limited
to the motor cortex.
The Consciousness Prior
Authors: Yoshua Bengio
Date: 2017-09-25 00:00:00
Tasks: Decision Making, Form, Representation Learning, Sentence

A new prior is proposed for learning representations of high-level concepts of the kind we manipulate with language. This prior can be combined with other priors in order to help disentangling abstract factors from each other. It is inspired by cognitive neuroscience theories of consciousness, seen as a bottleneck through which just a few elements, after having been selected by attention from a broader pool, are then broadcast and condition further processing, both in perception and decision-making. The set of recently selected elements one becomes aware of is seen as forming a low-dimensional conscious state. This conscious state is combining the few concepts constituting a conscious thought, i.e., what one is immediately conscious of at a particular moment. We claim that this architectural and information-processing constraint corresponds to assumptions about the joint distribution between high-level concepts. To the extent that these assumptions are generally true (and the form of natural language seems consistent with them), they can form a useful prior for representation learning. A low-dimensional thought or conscious state is analogous to a sentence: it involves only a few variables and yet can make a statement with very high probability of being true. This is consistent with a joint distribution (over high-level concepts) which has the form of a sparse factor graph, i.e., where the dependencies captured by each factor of the factor graph involve only very few variables while creating a strong dip in the overall energy function. The consciousness prior also makes it natural to map conscious states to natural language utterances or to express classical AI knowledge in a form similar to facts and rules, albeit capturing uncertainty as well as efficient search mechanisms implemented by attention mechanisms.
Evolutionary Acyclic Graph Partitioning
Authors: Orlando Moreira, Merten Popp, Christian Schulz
Date: 2017-09-25 00:00:00
Tasks: graph partitioning, Scheduling

Directed graphs are widely used to model data flow and execution dependencies
in streaming applications. This enables the utilization of graph partitioning
algorithms for the problem of parallelizing computation for multiprocessor
architectures. However due to resource restrictions, an acyclicity constraint
on the partition is necessary when mapping streaming applications to an
embedded multiprocessor. Here, we contribute a multi-level algorithm for the
acyclic graph partitioning problem. Based on this, we engineer an evolutionary
algorithm to further reduce communication cost, as well as to improve load
balancing and the scheduling makespan on embedded multiprocessor architectures.
Attribute Recognition by Joint Recurrent Learning of Context and Correlation
Authors: Jingya Wang, Xiatian Zhu, Shaogang Gong, Wei Li
Date: 2017-09-25 00:00:00
Proceeding: ICCV 2017 10
Tasks: Attribute, Decoder, image-classification, Image Classification, Multi-Label Image Classification, Pedestrian Attribute Recognition

Recognising semantic pedestrian attributes in surveillance images is a
challenging task for computer vision, particularly when the imaging quality is
poor with complex background clutter and uncontrolled viewing conditions, and
the number of labelled training data is small. In this work, we formulate a
Joint Recurrent Learning (JRL) model for exploring attribute context and
correlation in order to improve attribute recognition given small sized
training data with poor quality images. The JRL model learns jointly pedestrian
attribute correlations in a pedestrian image and in particular their sequential
ordering dependencies (latent high-order correlation) in an end-to-end
encoder/decoder recurrent network. We demonstrate the performance advantage and
robustness of the JRL model over a wide range of state-of-the-art deep models
for pedestrian attribute recognition, multi-label image classification, and
multi-person image annotation on two largest pedestrian attribute benchmarks
PETA and RAP.
Exact MAP inference in general higher-order graphical models using linear programming
Authors: Ikhlef Bechar
Date: 2017-09-25 00:00:00

This paper is concerned with the problem of exact MAP inference in general
higher-order graphical models by means of a traditional linear programming
relaxation approach. In fact, the proof that we have developed in this paper is
a rather simple algebraic proof being made straightforward, above all, by the
introduction of two novel algebraic tools. Indeed, on the one hand, we
introduce the notion of delta-distribution which merely stands for the
difference of two arbitrary probability distributions, and which mainly serves
to alleviate the sign constraint inherent to a traditional probability
distribution. On the other hand, we develop an approximation framework of
general discrete functions by means of an orthogonal projection expressing in
terms of linear combinations of function margins with respect to a given
collection of point subsets, though, we rather exploit the latter approach for
the purpose of modeling locally consistent sets of discrete functions from a
global perspective. After that, as a first step, we develop from scratch the
expectation optimization framework which is nothing else than a reformulation,
on stochastic grounds, of the convex-hull approach, as a second step, we
develop the traditional LP relaxation of such an expectation optimization
approach, and we show that it enables to solve the MAP inference problem in
graphical models under rather general assumptions. Last but not least, we
describe an algorithm which allows to compute an exact MAP solution from a
perhaps fractional optimal (probability) solution of the proposed LP
relaxation.
Distributed Mean Estimation with Limited Communication
Authors: Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan
Date: 2016-11-02 00:00:00
Proceeding: ICML 2017 8
Tasks: Quantization

Motivated by the need for distributed learning and optimization algorithms
with low communication cost, we study communication efficient algorithms for
distributed mean estimation. Unlike previous works, we make no probabilistic
assumptions on the data. We first show that for $d$ dimensional data with $n$
clients, a naive stochastic binary rounding approach yields a mean squared
error (MSE) of $\Theta(d/n)$ and uses a constant number of bits per dimension
per client. We then extend this naive algorithm in two ways: we show that
applying a structured random rotation before quantization reduces the error to
$\mathcal{O}((\log d)/n)$ and a better coding strategy further reduces the
error to $\mathcal{O}(1/n)$ and uses a constant number of bits per dimension
per client. We also show that the latter coding strategy is optimal up to a
constant in the minimax sense i.e., it achieves the best MSE for a given
communication cost. We finally demonstrate the practicality of our algorithms
by applying them to distributed Lloyd's algorithm for k-means and power
iteration for PCA.
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net
Authors: Tom Michoel
Date: 2017-09-25 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Bayesian Inference, regression, Variable Selection

The lasso and elastic net linear regression models impose a
double-exponential prior distribution on the model parameters to achieve
regression shrinkage and variable selection, allowing the inference of robust
models from large data sets. However, there has been limited success in
deriving estimates for the full posterior distribution of regression
coefficients in these models, due to a need to evaluate analytically
intractable partition function integrals. Here, the Fourier transform is used
to express these integrals as complex-valued oscillatory integrals over
"regression frequencies". This results in an analytic expansion and stationary
phase approximation for the partition functions of the Bayesian lasso and
elastic net, where the non-differentiability of the double-exponential prior
has so far eluded such an approach. Use of this approximation leads to highly
accurate numerical estimates for the expectation values and marginal posterior
distributions of the regression coefficients, and allows for Bayesian inference
of much higher dimensional models than previously possible.
Multi-view pose estimation with mixtures-of-parts and adaptive viewpoint selection
Authors: Emre Dogan, Gonen Eren, Christian Wolf, Eric Lombardi, Atilla Baskurt
Date: 2017-09-25 00:00:00
Tasks: Pose Estimation

We propose a new method for human pose estimation which leverages information
from multiple views to impose a strong prior on articulated pose. The novelty
of the method concerns the types of coherence modelled. Consistency is
maximised over the different views through different terms modelling classical
geometric information (coherence of the resulting poses) as well as appearance
information which is modelled as latent variables in the global energy
function. Moreover, adequacy of each view is assessed and their contributions
are adjusted accordingly. Experiments on the HumanEva and UMPM datasets show
that the proposed method significantly decreases the estimation error compared
to single-view results.
Generative learning for deep networks
Authors: Boris Flach, Alexander Shekhovtsov, Ondrej Fikar
Date: 2017-09-25 00:00:00
Tasks: Bayesian Inference

Learning, taking into account full distribution of the data, referred to as
generative, is not feasible with deep neural networks (DNNs) because they model
only the conditional distribution of the outputs given the inputs. Current
solutions are either based on joint probability models facing difficult
estimation problems or learn two separate networks, mapping inputs to outputs
(recognition) and vice-versa (generation). We propose an intermediate approach.
First, we show that forward computation in DNNs with logistic sigmoid
activations corresponds to a simplified approximate Bayesian inference in a
directed probabilistic multi-layer model. This connection allows to interpret
DNN as a probabilistic model of the output and all hidden units given the
input. Second, we propose that in order for the recognition and generation
networks to be more consistent with the joint model of the data, weights of the
recognition and generator network should be related by transposition. We
demonstrate in a tentative experiment that such a coupled pair can be learned
generatively, modelling the full distribution of the data, and has enough
capacity to perform well in both recognition and generation.
Using objective words in the reviews to improve the colloquial arabic sentiment analysis
Authors: Omar Al-Harbi
Date: 2017-09-25 00:00:00
Tasks: Arabic Sentiment Analysis, General Classification, Sentiment Analysis, Sentiment Classification

One of the main difficulties in sentiment analysis of the Arabic language is
the presence of the colloquialism. In this paper, we examine the effect of
using objective words in conjunction with sentimental words on sentiment
classification for the colloquial Arabic reviews, specifically Jordanian
colloquial reviews. The reviews often include both sentimental and objective
words, however, the most existing sentiment analysis models ignore the
objective words as they are considered useless. In this work, we created two
lexicons: the first includes the colloquial sentimental words and compound
phrases, while the other contains the objective words associated with values of
sentiment tendency based on a particular estimation method. We used these
lexicons to extract sentiment features that would be training input to the
Support Vector Machines (SVM) to classify the sentiment polarity of the
reviews. The reviews dataset have been collected manually from JEERAN website.
The results of the experiments show that the proposed approach improves the
polarity classification in comparison to two baseline models, with accuracy
95.6%.
Predictive-State Decoders: Encoding the Future into Recurrent Networks
Authors: Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris M. Kitani, J. Andrew Bagnell
Date: 2017-09-25 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Imitation Learning, Reinforcement Learning

Recurrent neural networks (RNNs) are a vital modeling technique that rely on
internal states learned indirectly by optimization of a supervised,
unsupervised, or reinforcement training loss. RNNs are used to model dynamic
processes that are characterized by underlying latent states whose form is
often unknown, precluding its analytic representation inside an RNN. In the
Predictive-State Representation (PSR) literature, latent state processes are
modeled by an internal state representation that directly models the
distribution of future observations, and most recent work in this area has
relied on explicitly representing and targeting sufficient statistics of this
probability distribution. We seek to combine the advantages of RNNs and PSRs by
augmenting existing state-of-the-art recurrent neural networks with
Predictive-State Decoders (PSDs), which add supervision to the network's
internal state representation to target predicting future observations.
Predictive-State Decoders are simple to implement and easily incorporated into
existing training pipelines via additional loss regularization. We demonstrate
the effectiveness of PSDs with experimental results in three different domains:
probabilistic filtering, Imitation Learning, and Reinforcement Learning. In
each, our method improves statistical performance of state-of-the-art recurrent
baselines and does so with fewer iterations and less data.
Enhanced Quantum Synchronization via Quantum Machine Learning
Authors: F. A. Cárdenas-López, M. Sanz, J. C. Retamal, E. Solano
Date: 2017-09-25 00:00:00
Tasks: BIG-bench Machine Learning, Quantum Machine Learning

We study the quantum synchronization between a pair of two-level systems
inside two coupled cavities. By using a digital-analog decomposition of the
master equation that rules the system dynamics, we show that this approach
leads to quantum synchronization between both two-level systems. Moreover, we
can identify in this digital-analog block decomposition the fundamental
elements of a quantum machine learning protocol, in which the agent and the
environment (learning units) interact through a mediating system, namely, the
register. If we can additionally equip this algorithm with a classical feedback
mechanism, which consists of projective measurements in the register,
reinitialization of the register state and local conditional operations on the
agent and environment subspace, a powerful and flexible quantum machine
learning protocol emerges. Indeed, numerical simulations show that this
protocol enhances the synchronization process, even when every subsystem
experience different loss/decoherence mechanisms, and give us the flexibility
to choose the synchronization state. Finally, we propose an implementation
based on current technologies in superconducting circuits.
Sentiment Polarity Detection for Software Development
Authors: Fabio Calefato, Filippo Lanubile, Federico Maiorano, Nicole Novielli
Date: 2017-09-09 00:00:00
Tasks: Sentiment Analysis

The role of sentiment analysis is increasingly emerging to study software
developers' emotions by mining crowd-generated content within social software
engineering tools. However, off-the-shelf sentiment analysis tools have been
trained on non-technical domains and general-purpose social media, thus
resulting in misclassifications of technical jargon and problem reports. Here,
we present Senti4SD, a classifier specifically trained to support sentiment
analysis in developers' communication channels. Senti4SD is trained and
validated using a gold standard of Stack Overflow questions, answers, and
comments manually annotated for sentiment polarity. It exploits a suite of both
lexicon- and keyword-based features, as well as semantic features based on word
embedding. With respect to a mainstream off-the-shelf tool, which we use as a
baseline, Senti4SD reduces the misclassifications of neutral and positive posts
as emotionally negative. To encourage replications, we release a lab package
including the classifier, the word embedding space, and the gold standard with
annotation guidelines.
LADAR-Based Mover Detection from Moving Vehicles
Authors: Daniel D. Morris, Brian Colonna, Paul Haley
Date: 2017-09-25 00:00:00

Detecting moving vehicles and people is crucial for safe operation of UGVs
but is challenging in cluttered, real world environments. We propose a
registration technique that enables objects to be robustly matched and tracked,
and hence movers to be detected even in high clutter. Range data are acquired
using a 2D scanning Ladar from a moving platform. These are automatically
clustered into objects and modeled using a surface density function. A
Bhattacharya similarity is optimized to register subsequent views of each
object enabling good discrimination and tracking, and hence mover detection.
Statistical learning of spatiotemporal patterns from longitudinal manifold-valued networks
Authors: Igor Koval, Jean-Baptiste Schiratti, Alexandre Routier, Michael Bacci, Olivier Colliot, Stéphanie Allassonnière, Stanley Durrleman
Date: 2017-09-25 00:00:00

We introduce a mixed-effects model to learn spatiotempo-ral patterns on a
network by considering longitudinal measures distributed on a fixed graph. The
data come from repeated observations of subjects at different time points which
take the form of measurement maps distributed on a graph such as an image or a
mesh. The model learns a typical group-average trajectory characterizing the
propagation of measurement changes across the graph nodes. The subject-specific
trajectories are defined via spatial and temporal transformations of the
group-average scenario, thus estimating the variability of spatiotemporal
patterns within the group. To estimate population and individual model
parameters, we adapted a stochastic version of the Expectation-Maximization
algorithm, the MCMC-SAEM. The model is used to describe the propagation of
cortical atrophy during the course of Alzheimer's Disease. Model parameters
show the variability of this average pattern of atrophy in terms of
trajectories across brain regions, age at disease onset and pace of
propagation. We show that the personalization of this model yields accurate
prediction of maps of cortical thickness in patients.
Bayesian Filtering for ODEs with Bounded Derivatives
Authors: Emilia Magnani, Hans Kersting, Michael Schober, Philipp Hennig
Date: 2017-09-25 00:00:00

Recently there has been increasing interest in probabilistic solvers for
ordinary differential equations (ODEs) that return full probability measures,
instead of point estimates, over the solution and can incorporate uncertainty
over the ODE at hand, e.g. if the vector field or the initial value is only
approximately known or evaluable. The ODE filter proposed in recent work models
the solution of the ODE by a Gauss-Markov process which serves as a prior in
the sense of Bayesian statistics. While previous work employed a Wiener process
prior on the (possibly multiple times) differentiated solution of the ODE and
established equivalence of the corresponding solver with classical numerical
methods, this paper raises the question whether other priors also yield
practically useful solvers. To this end, we discuss a range of possible priors
which enable fast filtering and propose a new prior--the Integrated Ornstein
Uhlenbeck Process (IOUP)--that complements the existing Integrated Wiener
process (IWP) filter by encoding the property that a derivative in time of the
solution is bounded in the sense that it tends to drift back to zero. We
provide experiments comparing IWP and IOUP filters which support the belief
that IWP approximates better divergent ODE's solutions whereas IOUP is a better
prior for trajectories with bounded derivatives.