pwc-archive/papers-with-abstracts huggingface dataset:
Hierarchical Modeling of Multidimensional Data in Regularly Decomposed Spaces: Main Principles
Authors: Olivier Guye
Date: 2016-05-03 00:00:00
Tasks: Attribute, Decision Making, Management

The described works have been carried out in the framework of a mid-term
study initiated by the Centre Electronique de l'Armement and led by ADERSA, a
French company of research under contract. The aim was to study the techniques
of regular dividing of numerical data sets so as to provide tools for problem
solving enabling to model multidimensional numerical objects and to be used in
computer-aided design and manufacturing, in robotics, in image analysis and
synthesis, in pattern recognition, in decision making, in cartography and
numerical data base management. These tools are relying on the principle of
regular hierarchical decomposition and led to the implementation of a
multidimensional generalization of quaternary and octernary trees: the trees of
order 2**k or 2**k-trees mapped in binary trees. This first tome, dedicated to
the hierarchical modeling of multidimensional numerical data, describes the
principles used for building, transforming, analyzing and recognizing patterns
on which is relying the development of the associated algorithms. The whole so
developed algorithms are detailed in pseudo-code at the end of this document.
The present publication especially describes: - a building method adapted
disordered and overcrowded data streams ; - its extension in inductive limits ;
- the computation of the homographic transformation of a tree ; - the attribute
calculus based on generalized moments and the provision of Eigen trees ; -
perception procedures of objects without any covering in affine geometry ; -
several supervised and unsupervised pattern recognition methods.
ODE - Augmented Training Improves Anomaly Detection in Sensor Data from Machines
Authors: Mohit Yadav, Pankaj Malhotra, Lovekesh Vig, K Sriram, Gautam Shroff
Date: 2016-05-05 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis

Machines of all kinds from vehicles to industrial equipment are increasingly
instrumented with hundreds of sensors. Using such data to detect anomalous
behaviour is critical for safety and efficient maintenance. However, anomalies
occur rarely and with great variety in such systems, so there is often
insufficient anomalous data to build reliable detectors. A standard approach to
mitigate this problem is to use one class methods relying only on data from
normal behaviour. Unfortunately, even these approaches are more likely to fail
in the scenario of a dynamical system with manual control input(s). Normal
behaviour in response to novel control input(s) might look very different to
the learned detector which may be incorrectly detected as anomalous. In this
paper, we address this issue by modelling time-series via Ordinary Differential
Equations (ODE) and utilising such an ODE model to simulate the behaviour of
dynamical systems under varying control inputs. The available data is then
augmented with data generated from the ODE, and the anomaly detector is
retrained on this augmented dataset. Experiments demonstrate that ODE-augmented
training data allows better coverage of possible control input(s) and results
in learning more accurate distinctions between normal and anomalous behaviour
in time-series.
Fitness-based Adaptive Control of Parameters in Genetic Programming: Adaptive Value Setting of Mutation Rate and Flood Mechanisms
Authors: Michal Gregor, Juraj Spalek
Date: 2016-05-05 00:00:00

This paper concerns applications of genetic algorithms and genetic
programming to tasks for which it is difficult to find a representation that
does not map to a highly complex and discontinuous fitness landscape. In such
cases the standard algorithm is prone to getting trapped in local extremes. The
paper proposes several adaptive mechanisms that are useful in preventing the
search from getting trapped.
Multilingual Twitter Sentiment Classification: The Role of Human Annotators
Authors: Igor Mozetic, Miha Grcar, Jasmina Smailovic
Date: 2016-02-24 00:00:00
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification

What are the limits of automated Twitter sentiment classification? We analyze
a large set of manually labeled tweets in different languages, use them as
training data, and construct automated classification models. It turns out that
the quality of classification models depends much more on the quality and size
of training data than on the type of the model trained. Experimental results
indicate that there is no statistically significant difference between the
performance of the top classification models. We quantify the quality of
training data by applying various annotator agreement measures, and identify
the weakest points of different datasets. We show that the model performance
approaches the inter-annotator agreement when the size of the training set is
sufficiently large. However, it is crucial to regularly monitor the self- and
inter-annotator agreements since this improves the training datasets and
consequently the model performance. Finally, we show that there is strong
evidence that humans perceive the sentiment classes (negative, neutral, and
positive) as ordered.
A Large Dataset of Object Scans
Authors: Sungjoon Choi, Qian-Yi Zhou, Stephen Miller, Vladlen Koltun
Date: 2016-02-08 00:00:00
Tasks: Object

We have created a dataset of more than ten thousand 3D scans of real objects.
To create the dataset, we recruited 70 operators, equipped them with
consumer-grade mobile 3D scanning setups, and paid them to scan objects in
their environments. The operators scanned objects of their choosing, outside
the laboratory and without direct supervision by computer vision professionals.
The result is a large and diverse collection of object scans: from shoes, mugs,
and toys to grand pianos, construction vehicles, and large outdoor sculptures.
We worked with an attorney to ensure that data acquisition did not violate
privacy constraints. The acquired data was irrevocably placed in the public
domain and is available freely at http://redwood-data.org/3dscan .
Modeling Rich Contexts for Sentiment Classification with LSTM
Authors: Minlie Huang, Yujie Cao, Chao Dong
Date: 2016-05-05 00:00:00
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification

Sentiment analysis on social media data such as tweets and weibo has become a
very important and challenging task. Due to the intrinsic properties of such
data, tweets are short, noisy, and of divergent topics, and sentiment
classification on these data requires to modeling various contexts such as the
retweet/reply history of a tweet, and the social context about authors and
relationships. While few prior study has approached the issue of modeling
contexts in tweet, this paper proposes to use a hierarchical LSTM to model rich
contexts in tweet, particularly long-range context. Experimental results show
that contexts can help us to perform sentiment classification remarkably
better.
Movement Coordination in Human-Robot Teams: A Dynamical Systems Approach
Authors: Tariq Iqbal, Samantha Rack, Laurel D. Riek
Date: 2016-05-04 00:00:00

In order to be effective teammates, robots need to be able to understand
high-level human behavior to recognize, anticipate, and adapt to human motion.
We have designed a new approach to enable robots to perceive human group motion
in real-time, anticipate future actions, and synthesize their own motion
accordingly. We explore this within the context of joint action, where humans
and robots move together synchronously. In this paper, we present an
anticipation method which takes high-level group behavior into account. We
validate the method within a human-robot interaction scenario, where an
autonomous mobile robot observes a team of human dancers, and then successfully
and contingently coordinates its movements to "join the dance". We compared the
results of our anticipation method to move the robot with another method which
did not rely on high-level group behavior, and found our method performed
better both in terms of more closely synchronizing the robot's motion to the
team, and also exhibiting more contingent and fluent motion. These findings
suggest that the robot performs better when it has an understanding of
high-level group behavior than when it does not. This work will help enable
others in the robotics community to build more fluent and adaptable robots in
the future.
Unified Convergence Analysis of Stochastic Momentum Methods for Convex and Non-convex Optimization
Authors: Tianbao Yang, Qihang Lin, Zhe Li
Date: 2016-04-12 00:00:00

Recently, {\it stochastic momentum} methods have been widely adopted in
training deep neural networks. However, their convergence analysis is still
underexplored at the moment, in particular for non-convex optimization. This
paper fills the gap between practice and theory by developing a basic
convergence analysis of two stochastic momentum methods, namely stochastic
heavy-ball method and the stochastic variant of Nesterov's accelerated gradient
method. We hope that the basic convergence results developed in this paper can
serve the reference to the convergence of stochastic momentum methods and also
serve the baselines for comparison in future development of stochastic momentum
methods. The novelty of convergence analysis presented in this paper is a
unified framework, revealing more insights about the similarities and
differences between different stochastic momentum methods and stochastic
gradient method. The unified framework exhibits a continuous change from the
gradient method to Nesterov's accelerated gradient method and finally the
heavy-ball method incurred by a free parameter, which can help explain a
similar change observed in the testing error convergence behavior for deep
learning. Furthermore, our empirical results for optimizing deep neural
networks demonstrate that the stochastic variant of Nesterov's accelerated
gradient method achieves a good tradeoff (between speed of convergence in
training error and robustness of convergence in testing error) among the three
stochastic methods.
Boltzmann meets Nash: Energy-efficient routing in optical networks under uncertainty
Authors: Panayotis Mertikopoulos, Aris L. Moustakas, Anna Tzanakaki
Date: 2016-05-04 00:00:00

Motivated by the massive deployment of power-hungry data centers for service
provisioning, we examine the problem of routing in optical networks with the
aim of minimizing traffic-driven power consumption. To tackle this issue,
routing must take into account energy efficiency as well as capacity
considerations; moreover, in rapidly-varying network environments, this must be
accomplished in a real-time, distributed manner that remains robust in the
presence of random disturbances and noise. In view of this, we derive a pricing
scheme whose Nash equilibria coincide with the network's socially optimum
states, and we propose a distributed learning method based on the Boltzmann
distribution of statistical mechanics. Using tools from stochastic calculus, we
show that the resulting Boltzmann routing scheme exhibits remarkable
convergence properties under uncertainty: specifically, the long-term average
of the network's power consumption converges within $\varepsilon$ of its
minimum value in time which is at most $\tilde O(1/\varepsilon^2)$,
irrespective of the fluctuations' magnitude; additionally, if the network
admits a strict, non-mixing optimum state, the algorithm converges to it -
again, no matter the noise level. Our analysis is supplemented by extensive
numerical simulations which show that Boltzmann routing can lead to a
significant decrease in power consumption over basic, shortest-path routing
schemes in realistic network conditions.
Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians
Authors: Peiyun Hu, Deva Ramanan
Date: 2015-07-21 00:00:00
Proceeding: CVPR 2016 6
Tasks: GPU, Pose Estimation

Convolutional neural nets (CNNs) have demonstrated remarkable performance in
recent history. Such approaches tend to work in a unidirectional bottom-up
feed-forward fashion. However, practical experience and biological evidence
tells us that feedback plays a crucial role, particularly for detailed spatial
understanding tasks. This work explores bidirectional architectures that also
reason with top-down feedback: neural units are influenced by both lower and
higher-level units.
  We do so by treating units as rectified latent variables in a quadratic
energy function, which can be seen as a hierarchical Rectified Gaussian model
(RGs). We show that RGs can be optimized with a quadratic program (QP), that
can in turn be optimized with a recurrent neural network (with rectified linear
units). This allows RGs to be trained with GPU-optimized gradient descent. From
a theoretical perspective, RGs help establish a connection between CNNs and
hierarchical probabilistic models. From a practical perspective, RGs are well
suited for detailed spatial tasks that can benefit from top-down reasoning. We
illustrate them on the challenging task of keypoint localization under
occlusions, where local bottom-up evidence may be misleading. We demonstrate
state-of-the-art results on challenging benchmarks.
Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)
Authors: David Gutman, Noel C. F. Codella, Emre Celebi, Brian Helba, Michael Marchetti, Nabin Mishra, Allan Halpern
Date: 2016-05-04 00:00:00
Tasks: Lesion Segmentation, Melanoma Diagnosis

In this article, we describe the design and implementation of a publicly
accessible dermatology image analysis benchmark challenge. The goal of the
challenge is to sup- port research and development of algorithms for automated
diagnosis of melanoma, a lethal form of skin cancer, from dermoscopic images.
The challenge was divided into sub-challenges for each task involved in image
analysis, including lesion segmentation, dermoscopic feature detection within a
lesion, and classification of melanoma. Training data included 900 images. A
separate test dataset of 379 images was provided to measure resultant
performance of systems developed with the training data. Ground truth for both
training and test sets was generated by a panel of dermoscopic experts. In
total, there were 79 submissions from a group of 38 participants, making this
the largest standardized and comparative study for melanoma diagnosis in
dermoscopic images to date. While the official challenge duration and ranking
of participants has concluded, the datasets remain available for further
research and development.
The embedding dimension of Laplacian eigenfunction maps
Authors: Jonathan Bates
Date: 2016-05-04 00:00:00

Any closed, connected Riemannian manifold $M$ can be smoothly embedded by its
Laplacian eigenfunction maps into $\mathbb{R}^m$ for some $m$. We call the
smallest such $m$ the maximal embedding dimension of $M$. We show that the
maximal embedding dimension of $M$ is bounded from above by a constant
depending only on the dimension of $M$, a lower bound for injectivity radius, a
lower bound for Ricci curvature, and a volume bound. We interpret this result
for the case of surfaces isometrically immersed in $\mathbb{R}^3$, showing that
the maximal embedding dimension only depends on bounds for the Gaussian
curvature, mean curvature, and surface area. Furthermore, we consider the
relevance of these results for shape registration.
The Hidden Convexity of Spectral Clustering
Authors: James Voss, Mikhail Belkin, Luis Rademacher
Date: 2014-03-04 00:00:00
Tasks: Clustering

In recent years, spectral clustering has become a standard method for data
analysis used in a broad range of applications. In this paper we propose a new
class of algorithms for multiway spectral clustering based on optimization of a
certain "contrast function" over the unit sphere. These algorithms, partly
inspired by certain Independent Component Analysis techniques, are simple, easy
to implement and efficient.
  Geometrically, the proposed algorithms can be interpreted as hidden basis
recovery by means of function optimization. We give a complete characterization
of the contrast functions admissible for provable basis recovery. We show how
these conditions can be interpreted as a "hidden convexity" of our optimization
problem on the sphere; interestingly, we use efficient convex maximization
rather than the more common convex minimization. We also show encouraging
experimental results on real and simulated data.
Learning from the memory of Atari 2600
Authors: Jakub Sygnowski, Henryk Michalewski
Date: 2016-05-04 00:00:00
Tasks: Atari Games

We train a number of neural networks to play games Bowling, Breakout and
Seaquest using information stored in the memory of a video game console Atari
2600. We consider four models of neural networks which differ in size and
architecture: two networks which use only information contained in the RAM and
two mixed networks which use both information in the RAM and information from
the screen. As the benchmark we used the convolutional model proposed in NIPS
and received comparable results in all considered games. Quite surprisingly, in
the case of Seaquest we were able to train RAM-only agents which behave better
than the benchmark screen-only agent. Mixing screen and RAM did not lead to an
improved performance comparing to screen-only and RAM-only agents.
Single Channel Speech Enhancement Using Outlier Detection
Authors: Eunjoon Cho, Bowon Lee, Ronald Schafer, Bernard Widrow
Date: 2016-05-04 00:00:00
Tasks: Noise Estimation, Outlier Detection, Speech Enhancement

Distortion of the underlying speech is a common problem for single-channel
speech enhancement algorithms, and hinders such methods from being used more
extensively. A dictionary based speech enhancement method that emphasizes
preserving the underlying speech is proposed. Spectral patches of clean speech
are sampled and clustered to train a dictionary. Given a noisy speech spectral
patch, the best matching dictionary entry is selected and used to estimate the
noise power at each time-frequency bin. The noise estimation step is formulated
as an outlier detection problem, where the noise at each bin is assumed present
only if it is an outlier to the corresponding bin of the best matching
dictionary entry. This framework assigns higher priority in removing spectral
elements that strongly deviate from a typical spoken unit stored in the trained
dictionary. Even without the aid of a separate noise model, this method can
achieve significant noise reduction for various non-stationary noises, while
effectively preserving the underlying speech in more challenging noisy
environments.
Notes on a model for fuzzy computing
Authors: Vittorio Cafagna, Gianluca Caterina
Date: 2016-05-04 00:00:00
Tasks: model

In these notes we propose a setting for fuzzy computing in a framework
similar to that of well-established theories of computation: boolean, and
quantum computing. Our efforts have been directed towards stressing the formal
similarities: there is a common pattern underlying these three theories. We
tried to conform our approach, as much as possible, to this pattern. This work
was part of a project jointly with Professor Vittorio Cafagna. Professor
Cafagna passed away unexpectedly in 2007. His intellectual breadth and
inspiring passion for mathematics is still very well alive.
Compression and the origins of Zipf's law of abbreviation
Authors: R. Ferrer-i-Cancho, C. Bentz, C. Seguin
Date: 2015-04-19 00:00:00

Languages across the world exhibit Zipf's law of abbreviation, namely more
frequent words tend to be shorter. The generalized version of the law - an
inverse relationship between the frequency of a unit and its magnitude - holds
also for the behaviours of other species and the genetic code. The apparent
universality of this pattern in human language and its ubiquity in other
domains calls for a theoretical understanding of its origins. To this end, we
generalize the information theoretic concept of mean code length as a mean
energetic cost function over the probability and the magnitude of the types of
the repertoire. We show that the minimization of that cost function and a
negative correlation between probability and the magnitude of types are
intimately related.
Video (language) modeling: a baseline for generative models of natural videos
Authors: MarcAurelio Ranzato, Arthur Szlam, Joan Bruna, Michael Mathieu, Ronan Collobert, Sumit Chopra
Date: 2014-12-20 00:00:00
Tasks: Language Modeling, Language Modelling

We propose a strong baseline model for unsupervised feature learning using
video data. By learning to predict missing frames or extrapolate future frames
from an input video sequence, the model discovers both spatial and temporal
correlations which are useful to represent complex deformations and motion
patterns. The models we propose are largely borrowed from the language modeling
literature, and adapted to the vision domain by quantizing the space of image
patches into a large dictionary. We demonstrate the approach on both a filling
and a generation task. For the first time, we show that, after training on
natural videos, such a model can predict non-trivial motions over short video
sequences.
Hierarchical Modeling of Multidimensional Data in Regularly Decomposed Spaces: Applications in Image Analysis
Authors: Olivier Guye
Date: 2016-05-04 00:00:00
Tasks: Descriptive, Face Recognition

This last document is showing the gradual introduction of hierarchical
modeling techniques in image analysis. The first chapter is dealing with the
first works carried out in the field of industrial applications of pattern
recognition. The second chapter is focusing on the usage of these techniques in
satellite imagery and on the development of a satellite data archiving system
in the aim of using it in digital geography. The third chapter is about face
recognition based on planar image analysis and about the recognition of
partially hidden patterns. The present publication is ending with the
description of a future system of self-descriptive coding of still or moving
pictures in relation with the current video coding standards. As in the
previous documents, it will be found in annex algorithms targeted on image
analysis according two complementary approaches: - boundary-based approach for
the industrial applications of artificial vision; - region-based approach for
satellite image analysis.
Classical Statistics and Statistical Learning in Imaging Neuroscience
Authors: Danilo Bzdok
Date: 2016-03-06 00:00:00
Tasks: General Classification, regression, Two-sample testing

Neuroimaging research has predominantly drawn conclusions based on classical
statistics, including null-hypothesis testing, t-tests, and ANOVA. Throughout
recent years, statistical learning methods enjoy increasing popularity,
including cross-validation, pattern classification, and sparsity-inducing
regression. These two methodological families used for neuroimaging data
analysis can be viewed as two extremes of a continuum. Yet, they originated
from different historical contexts, build on different theories, rest on
different assumptions, evaluate different outcome metrics, and permit different
conclusions. This paper portrays commonalities and differences between
classical statistics and statistical learning with their relation to
neuroimaging research. The conceptual implications are illustrated in three
common analysis scenarios. It is thus tried to resolve possible confusion
between classical hypothesis testing and data-guided model estimation by
discussing their ramifications for the neuroimaging access to neurobiology.