pwc-archive/papers-with-abstracts huggingface dataset:
Predictive PAC Learning and Process Decompositions
Authors: Cosma Rohilla Shalizi, Aryeh Kontorovich
Date: 2013-09-19 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: PAC learning

We informally call a stochastic process learnable if it admits a
generalization error approaching zero in probability for any concept class with
finite VC-dimension (IID processes are the simplest example). A mixture of
learnable processes need not be learnable itself, and certainly its
generalization error need not decay at the same rate. In this paper, we argue
that it is natural in predictive PAC to condition not on the past observations
but on the mixture component of the sample path. This definition not only
matches what a realistic learner might demand, but also allows us to sidestep
several otherwise grave problems in learning from dependent data. In
particular, we give a novel PAC generalization bound for mixtures of learnable
processes with a generalization error that is not worse than that of each
mixture component. We also provide a characterization of mixtures of absolutely
regular ($\beta$-mixing) processes, of independent probability-theoretic
interest.
Network Anomaly Detection: A Survey and Comparative Analysis of Stochastic and Deterministic Methods
Authors: Jing Wang, Daniel Rossell, Christos G. Cassandras, Ioannis Ch. Paschalidis
Date: 2013-09-19 00:00:00
Tasks: Anomaly Detection, Clustering

We present five methods to the problem of network anomaly detection. These
methods cover most of the common techniques in the anomaly detection field,
including Statistical Hypothesis Tests (SHT), Support Vector Machines (SVM) and
clustering analysis. We evaluate all methods in a simulated network that
consists of nominal data, three flow-level anomalies and one packet-level
attack. Through analyzing the results, we point out the advantages and
disadvantages of each method and conclude that combining the results of the
individual methods can yield improved anomaly detection results.
Learning joint intensity-depth sparse representations
Authors: Ivana Tosic, Sarah Drewes
Date: 2012-01-03 00:00:00
Tasks: Dictionary Learning

This paper presents a method for learning overcomplete dictionaries composed
of two modalities that describe a 3D scene: image intensity and scene depth. We
propose a novel Joint Basis Pursuit (JBP) algorithm that finds related sparse
features in two modalities using conic programming and integrate it into a
two-step dictionary learning algorithm. JBP differs from related convex
algorithms because it finds joint sparsity models with different atoms and
different coefficient values for intensity and depth. This is crucial for
recovering generative models where the same sparse underlying causes (3D
features) give rise to different signals (intensity and depth). We give a
theoretical bound for the sparse coefficient recovery error obtained by JBP,
and show experimentally that JBP is far superior to the state of the art Group
Lasso algorithm. When applied to the Middlebury depth-intensity database, our
learning algorithm converges to a set of related features, such as pairs of
depth and intensity edges or image textures and depth slants. Finally, we show
that the learned dictionary and JBP achieve the state of the art depth
inpainting performance on time-of-flight 3D data.
Modeling the Role of Context Dependency in the Recognition and Manifestation of Entrepreneurial Opportunity
Authors: Murad A. Mithani, Tomas Veloz, Liane Gabora
Date: 2013-09-18 00:00:00

The paper uses the SCOP theory of concepts to model the role of environmental context on three levels of entrepreneurial opportunity: idea generation, idea development, and entrepreneurial decision. The role of contextual-fit in the generation and development of ideas is modeled as the collapse of their superposition state into one of the potential states that composes this superposition. The projection of this collapsed state on the socio-economic basis results in interference of the developed idea with the perceptions of the supporting community, undergoing an eventual collapse for an entrepreneurial decision that reflects the shared vision of its stakeholders. The developed idea may continue to evolve due to continuous or discontinuous changes in the environment. The model offers unique insights into the effects of external influences on entrepreneurial decisions.
Mapping Mutable Genres in Structurally Complex Volumes
Authors: Ted Underwood, Michael L. Black, Loretta Auvil, Boris Capitanu
Date: 2013-09-12 00:00:00
Tasks: Articles, General Classification

To mine large digital libraries in humanistically meaningful ways, scholars
need to divide them by genre. This is a task that classification algorithms are
well suited to assist, but they need adjustment to address the specific
challenges of this domain. Digital libraries pose two problems of scale not
usually found in the article datasets used to test these algorithms. 1) Because
libraries span several centuries, the genres being identified may change
gradually across the time axis. 2) Because volumes are much longer than
articles, they tend to be internally heterogeneous, and the classification task
needs to begin with segmentation. We describe a multi-layered solution that
trains hidden Markov models to segment volumes, and uses ensembles of
overlapping classifiers to address historical change. We test this approach on
a collection of 469,200 volumes drawn from HathiTrust Digital Library. To
demonstrate the humanistic value of these methods, we extract 32,209 volumes of
fiction from the digital library, and trace the changing proportions of first-
and third-person narration in the corpus. We note that narrative points of view
seem to have strong associations with particular themes and genres.
Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb
Authors: Ann L. Edwards, Alexandra Kearney, Michael Rory Dawson, Richard S. Sutton, Patrick M. Pilarski
Date: 2013-09-18 00:00:00
Tasks: Decision Making, Reinforcement Learning, Reinforcement Learning (RL)

In this work we explore the use of reinforcement learning (RL) to help with
human decision making, combining state-of-the-art RL algorithms with an
application to prosthetics. Managing human-machine interaction is a problem of
considerable scope, and the simplification of human-robot interfaces is
especially important in the domains of biomedical technology and rehabilitation
medicine. For example, amputees who control artificial limbs are often required
to quickly switch between a number of control actions or modes of operation in
order to operate their devices. We suggest that by learning to anticipate
(predict) a user's behaviour, artificial limbs could take on an active role in
a human's control decisions so as to reduce the burden on their users.
Recently, we showed that RL in the form of general value functions (GVFs) could
be used to accurately detect a user's control intent prior to their explicit
control choices. In the present work, we explore the use of temporal-difference
learning and GVFs to predict when users will switch their control influence
between the different motor functions of a robot arm. Experiments were
performed using a multi-function robot arm that was controlled by muscle
signals from a user's body (similar to conventional artificial limb control).
Our approach was able to acquire and maintain forecasts about a user's
switching decisions in real time. It also provides an intuitive and reward-free
way for users to correct or reinforce the decisions made by the machine
learning system. We expect that when a system is certain enough about its
predictions, it can begin to take over switching decisions from the user to
streamline control and potentially decrease the time and effort needed to
complete tasks. This preliminary study therefore suggests a way to naturally
integrate human- and machine-based decision making systems.
Distribution-Dependent Sample Complexity of Large Margin Learning
Authors: Sivan Sabato, Nathan Srebro, Naftali Tishby
Date: 2012-04-05 00:00:00
Tasks: Active Learning, General Classification, L2 Regularization

We obtain a tight distribution-specific characterization of the sample
complexity of large-margin classification with L2 regularization: We introduce
the margin-adapted dimension, which is a simple function of the second order
statistics of the data distribution, and show distribution-specific upper and
lower bounds on the sample complexity, both governed by the margin-adapted
dimension of the data distribution. The upper bounds are universal, and the
lower bounds hold for the rich family of sub-Gaussian distributions with
independent features. We conclude that this new quantity tightly characterizes
the true sample complexity of large-margin classification. To prove the lower
bound, we develop several new tools of independent interest. These include new
connections between shattering and hardness of learning, new properties of
shattering with linear classifiers, and a new lower bound on the smallest
eigenvalue of a random Gram matrix generated by sub-Gaussian variables. Our
results can be used to quantitatively compare large margin learning to other
learning rules, and to improve the effectiveness of methods that use sample
complexity bounds, such as active learning.
Text segmentation with character-level text embeddings
Authors: Grzegorz Chrupała
Date: 2013-09-18 00:00:00
Tasks: Segmentation, Text Segmentation

Learning word representations has recently seen much success in computational
linguistics. However, assuming sequences of word tokens as input to linguistic
analysis is often unjustified. For many languages word segmentation is a
non-trivial task and naturally occurring text is sometimes a mixture of natural
language strings and other character data. We propose to learn text
representations directly from raw character sequences by training a Simple
recurrent Network to predict the next character in text. The network uses its
hidden layer to evolve abstract representations of the character sequences it
sees. To demonstrate the usefulness of the learned text embeddings, we use them
as features in a supervised character level text segmentation and labeling
task: recognizing spans of text containing programming language code. By using
the embeddings as features we are able to substantially improve over a baseline
which uses only surface character n-grams.
A novel approach to nose-tip and eye corners detection using H-K Curvature Analysis in case of 3D images
Authors: Parama Bagchi, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu
Date: 2013-09-18 00:00:00
Tasks: Face Detection

In this paper we present a novel method that combines a HK curvature-based
approach for three-dimensional (3D) face detection in different poses (X-axis,
Y-axis and Z-axis). Salient face features, such as the eyes and nose, are
detected through an analysis of the curvature of the entire facial surface. All
the experiments have been performed on the FRAV3D Database. After applying the
proposed algorithm to the 3D facial surface we have obtained considerably good
results i.e. on 752 3D face images our method detected the eye corners for 543
face images, thus giving a 72.20% of eye corners detection and 743 face images
for nose-tip detection thus giving a 98.80% of good nose tip localization
Detection of pose orientation across single and multiple axes in case of 3D face images
Authors: Parama Bagchi, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu
Date: 2013-09-18 00:00:00

In this paper, we propose a new approach that takes as input a 3D face image across X, Y and Z axes as well as both Y and X axes and gives output as its pose i.e. it tells whether the face is oriented with respect the X, Y or Z axes or is it oriented across multiple axes with angles of rotation up to 42 degree. All the experiments have been performed on the FRAV3D, GAVADB and Bosphorus database which has two figures of each individual across multiple axes. After applying the proposed algorithm to the 3D facial surface from FRAV3D on 848 3D faces, 566 3D faces were correctly recognized for pose thus giving 67% of correct identification rate. We had experimented on 420 images from the GAVADB database, and only 336 images were detected for correct pose identification rate i.e. 80% and from Bosphorus database on 560 images only 448 images were detected for correct pose identification i.e. 80%.abstract goes here.
A novel approach for nose tip detection using smoothing by weighted median filtering applied to 3D face images in variant poses
Authors: Parama Bagchi, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu
Date: 2013-09-18 00:00:00

This paper is based on an application of smoothing of 3D face images followed by feature detection i.e. detecting the nose tip. The present method uses a weighted mesh median filtering technique for smoothing. In this present smoothing technique we have built the neighborhood surrounding a particular point in 3D face and replaced that with the weighted value of the surrounding points in 3D face image. After applying the smoothing technique to the 3D face images our experimental results show that we have obtained considerable improvement as compared to the algorithm without smoothing. We have used here the maximum intensity algorithm for detecting the nose-tip and this method correctly detects the nose-tip in case of any pose i.e. along X, Y, and Z axes. The present technique gave us worked successfully on 535 out of 542 3D face images as compared to the method without smoothing which worked only on 521 3D face images out of 542 face images. Thus we have obtained a 98.70% performance rate over 96.12% performance rate of the algorithm without smoothing. All the experiments have been performed on the FRAV3D database.
Bayesian rules and stochastic models for high accuracy prediction of solar radiation
Authors: Cyril Voyant, C. Darras, Marc Muselli, Christophe Paoli, Marie Laure Nivet, Philippe Poggi
Date: 2013-09-18 00:00:00
Tasks: Vocal Bursts Intensity Prediction

It is essential to find solar predictive methods to massively insert
renewable energies on the electrical distribution grid. The goal of this study
is to find the best methodology allowing predicting with high accuracy the
hourly global radiation. The knowledge of this quantity is essential for the
grid manager or the private PV producer in order to anticipate fluctuations
related to clouds occurrences and to stabilize the injected PV power. In this
paper, we test both methodologies: single and hybrid predictors. In the first
class, we include the multi-layer perceptron (MLP), auto-regressive and moving
average (ARMA), and persistence models. In the second class, we mix these
predictors with Bayesian rules to obtain ad-hoc models selections, and Bayesian
averages of outputs related to single models. If MLP and ARMA are equivalent
(nRMSE close to 40.5% for the both), this hybridization allows a nRMSE gain
upper than 14 percentage points compared to the persistence estimation
(nRMSE=37% versus 51%).
An Intelligent System to Detect, Avoid and Maintain Potholes: A Graph Theoretic Approach
Authors: Shreyas Balakuntala, Sandeep Venkatesh
Date: 2013-05-23 00:00:00

In this paper, we propose a conceptual framework where a centralized system,
classifies the road based upon the level of damage. The centralized system also
identifies the traffic intensity thereby prioritizing the roads that need quick
action to be taken upon. Moreover, the system helps the driver to detect the
level of damage to the road stretch and route the vehicle from an alternative
path to its destination. The system sends a feedback to the concerned
authorities for a quick response to the condition of the roads. The system we
use comprises a laser sensor and pressure sensors in shock absorbers to detect
and quantify the intensity of the pothole, a centralized server which maintains
a database of locations of all the potholes which can be accessed by another
unit inside the vehicle. A point to point connection device is also installed
in vehicles so that, when a vehicle detects a pothole which is not in the
database, all the vehicles within a range of 20 meters are warned about the
pothole. The system computes a route with least number of potholes which is
nearest to the desired destination . If the destination is unknown, then the
system will check for potholes in the current road and displays the level of
damage. The system is flexible enough that the destination can be added,
removed or changed any time during the travel. The best possible route is
suggested by the system upon the alteration. We prove that the algorithm
returns an efficient path with least number of potholes.
Learning Mixtures of Arbitrary Distributions over Large Discrete Domains
Authors: Yuval Rabani, Leonard Schulman, Chaitanya Swamy
Date: 2012-12-07 00:00:00
Tasks: 2k, Dimensionality Reduction, Topic Models

We give an algorithm for learning a mixture of {\em unstructured}
distributions. This problem arises in various unsupervised learning scenarios,
for example in learning {\em topic models} from a corpus of documents spanning
several topics. We show how to learn the constituents of a mixture of $k$
arbitrary distributions over a large discrete domain $[n]=\{1,2,\dots,n\}$ and
the mixture weights, using $O(n\polylog n)$ samples. (In the topic-model
learning setting, the mixture constituents correspond to the topic
distributions.) This task is information-theoretically impossible for $k>1$
under the usual sampling process from a mixture distribution. However, there
are situations (such as the above-mentioned topic model case) in which each
sample point consists of several observations from the same mixture
constituent. This number of observations, which we call the {\em "sampling
aperture"}, is a crucial parameter of the problem. We obtain the {\em first}
bounds for this mixture-learning problem {\em without imposing any assumptions
on the mixture constituents.} We show that efficient learning is possible
exactly at the information-theoretically least-possible aperture of $2k-1$.
Thus, we achieve near-optimal dependence on $n$ and optimal aperture. While the
sample-size required by our algorithm depends exponentially on $k$, we prove
that such a dependence is {\em unavoidable} when one considers general
mixtures. A sequence of tools contribute to the algorithm, such as
concentration results for random matrices, dimension reduction, moment
estimations, and sensitivity analysis.
Lambda Dependency-Based Compositional Semantics
Authors: Percy Liang
Date: 2013-09-17 00:00:00
Proceeding: cs.AL 2013 9
Tasks: Semantic Parsing

This short note presents a new formal language, lambda dependency-based
compositional semantics (lambda DCS) for representing logical forms in semantic
parsing. By eliminating variables and making existential quantification
implicit, lambda DCS logical forms are generally more compact than those in
lambda calculus.
Robust Face Recognition via Block Sparse Bayesian Learning
Authors: Taiyong Li, Zhilin Zhang
Date: 2013-01-29 00:00:00
Tasks: Face Recognition, Robust Face Recognition

Face recognition (FR) is an important task in pattern recognition and computer vision. Sparse representation (SR) has been demonstrated to be a powerful framework for FR. In general, an SR algorithm treats each face in a training dataset as a basis function, and tries to find a sparse representation of a test face under these basis functions. The sparse representation coefficients then provide a recognition hint. Early SR algorithms are based on a basic sparse model. Recently, it has been found that algorithms based on a block sparse model can achieve better recognition rates. Based on this model, in this study we use block sparse Bayesian learning (BSBL) to find a sparse representation of a test face for recognition. BSBL is a recently proposed framework, which has many advantages over existing block-sparse-model based algorithms. Experimental results on the Extended Yale B, the AR and the CMU PIE face databases show that using BSBL can achieve better recognition rates and higher robustness than state-of-the-art algorithms in most cases.
A fully automatic problem solver with human-style output
Authors: M. Ganesalingam, W. T. Gowers
Date: 2013-09-17 00:00:00

This paper describes a program that solves elementary mathematical problems,
mostly in metric space theory, and presents solutions that are hard to
distinguish from solutions that might be written by human mathematicians. The
program is part of a more general project, which we also discuss.
Robust image reconstruction from multi-view measurements
Authors: Gilles Puy, Pierre Vandergheynst
Date: 2012-12-13 00:00:00
Tasks: compressed sensing, Image Reconstruction, Super-Resolution

We propose a novel method to accurately reconstruct a set of images
representing a single scene from few linear multi-view measurements. Each
observed image is modeled as the sum of a background image and a foreground
one. The background image is common to all observed images but undergoes
geometric transformations, as the scene is observed from different viewpoints.
In this paper, we assume that these geometric transformations are represented
by a few parameters, e.g., translations, rotations, affine transformations,
etc.. The foreground images differ from one observed image to another, and are
used to model possible occlusions of the scene. The proposed reconstruction
algorithm estimates jointly the images and the transformation parameters from
the available multi-view measurements. The ideal solution of this multi-view
imaging problem minimizes a non-convex functional, and the reconstruction
technique is an alternating descent method built to minimize this functional.
The convergence of the proposed algorithm is studied, and conditions under
which the sequence of estimated images and parameters converges to a critical
point of the non-convex functional are provided. Finally, the efficiency of the
algorithm is demonstrated using numerical simulations for applications such as
compressed sensing or super-resolution.
GRED: Graph-Regularized 3D Shape Reconstruction from Highly Anisotropic and Noisy Images
Authors: Christian Widmer, Philipp Drewe, Xinghua Lou, Shefali Umrania, Stephanie Heinrich, Gunnar Rätsch
Date: 2013-09-17 00:00:00
Tasks: 3D Shape Reconstruction, BIG-bench Machine Learning, Nuclear Segmentation, Segmentation

Analysis of microscopy images can provide insight into many biological
processes. One particularly challenging problem is cell nuclear segmentation in
highly anisotropic and noisy 3D image data. Manually localizing and segmenting
each and every cell nuclei is very time consuming, which remains a bottleneck
in large scale biological experiments. In this work we present a tool for
automated segmentation of cell nuclei from 3D fluorescent microscopic data. Our
tool is based on state-of-the-art image processing and machine learning
techniques and supports a friendly graphical user interface (GUI). We show that
our tool is as accurate as manual annotation but greatly reduces the time for
the registration.
Photon counting compressive depth mapping
Authors: Gregory A. Howland, Daniel J. Lum, Matthew R. Ware, John C. Howell
Date: 2013-09-17 00:00:00
Tasks: compressed sensing, Object Tracking

We demonstrate a compressed sensing, photon counting lidar system based on
the single-pixel camera. Our technique recovers both depth and intensity maps
from a single under-sampled set of incoherent, linear projections of a scene of
interest at ultra-low light levels around 0.5 picowatts. Only two-dimensional
reconstructions are required to image a three-dimensional scene. We demonstrate
intensity imaging and depth mapping at 256 x 256 pixel transverse resolution
with acquisition times as short as 3 seconds. We also show novelty filtering,
reconstructing only the difference between two instances of a scene. Finally,
we acquire 32 x 32 pixel real-time video for three-dimensional object tracking
at 14 frames-per-second.