pwc-archive/papers-with-abstracts huggingface dataset:
A Category Theory of Communication Theory
Authors: Eric Werner
Date: 2015-05-28 00:00:00
Tasks: Sentence

A theory of how agents can come to understand a language is presented. If
understanding a sentence $\alpha$ is to associate an operator with $\alpha$
that transforms the representational state of the agent as intended by the
sender, then coming to know a language involves coming to know the operators
that correspond to the meaning of any sentence. This involves a higher order
operator that operates on the possible transformations that operate on the
representational capacity of the agent. We formalize these constructs using
concepts and diagrams analogous to category theory.
Invertible Orientation Scores of 3D Images
Authors: Michiel Janssen, Remco Duits, Marcel Breeuwer
Date: 2015-05-28 00:00:00

The enhancement and detection of elongated structures in noisy image data is
relevant for many biomedical applications. To handle complex crossing
structures in 2D images, 2D orientation scores were introduced, which already
showed their use in a variety of applications. Here we extend this work to 3D
orientation scores. First, we construct the orientation score from a given
dataset, which is achieved by an invertible coherent state type of transform.
For this transformation we introduce 3D versions of the 2D cake-wavelets, which
are complex wavelets that can simultaneously detect oriented structures and
oriented edges. For efficient implementation of the different steps in the
wavelet creation we use a spherical harmonic transform. Finally, we show some
first results of practical applications of 3D orientation scores.
Improved Deep Convolutional Neural Network For Online Handwritten Chinese Character Recognition using Domain-Specific Knowledge
Authors: Weixin Yang, Lianwen Jin, Zecheng Xie, Ziyong Feng
Date: 2015-05-28 00:00:00
Tasks: Diversity

Deep convolutional neural networks (DCNNs) have achieved great success in
various computer vision and pattern recognition applications, including those
for handwritten Chinese character recognition (HCCR). However, most current
DCNN-based HCCR approaches treat the handwritten sample simply as an image
bitmap, ignoring some vital domain-specific information that may be useful but
that cannot be learnt by traditional networks. In this paper, we propose an
enhancement of the DCNN approach to online HCCR by incorporating a variety of
domain-specific knowledge, including deformation, non-linear normalization,
imaginary strokes, path signature, and 8-directional features. Our contribution
is twofold. First, these domain-specific technologies are investigated and
integrated with a DCNN to form a composite network to achieve improved
performance. Second, the resulting DCNNs with diversity in their domain
knowledge are combined using a hybrid serial-parallel (HSP) strategy.
Consequently, we achieve a promising accuracy of 97.20% and 96.87% on
CASIA-OLHWDB1.0 and CASIA-OLHWDB1.1, respectively, outperforming the best
results previously reported in the literature.
A Generative Model of Natural Texture Surrogates
Authors: Niklas Ludtke, Debapriya Das, Lucas Theis, Matthias Bethge
Date: 2015-05-28 00:00:00
Tasks: Image Compression, model

Natural images can be viewed as patchworks of different textures, where the
local image statistics is roughly stationary within a small neighborhood but
otherwise varies from region to region. In order to model this variability, we
first applied the parametric texture algorithm of Portilla and Simoncelli to
image patches of 64X64 pixels in a large database of natural images such that
each image patch is then described by 655 texture parameters which specify
certain statistics, such as variances and covariances of wavelet coefficients
or coefficient magnitudes within that patch.
  To model the statistics of these texture parameters, we then developed
suitable nonlinear transformations of the parameters that allowed us to fit
their joint statistics with a multivariate Gaussian distribution. We find that
the first 200 principal components contain more than 99% of the variance and
are sufficient to generate textures that are perceptually extremely close to
those generated with all 655 components. We demonstrate the usefulness of the
model in several ways: (1) We sample ensembles of texture patches that can be
directly compared to samples of patches from the natural image database and can
to a high degree reproduce their perceptual appearance. (2) We further
developed an image compression algorithm which generates surprisingly accurate
images at bit rates as low as 0.14 bits/pixel. Finally, (3) We demonstrate how
our approach can be used for an efficient and objective evaluation of samples
generated with probabilistic models of natural images.
A trust-region method for stochastic variational inference with applications to streaming data
Authors: Lucas Theis, Matthew D. Hoffman
Date: 2015-05-28 00:00:00
Tasks: Sensitivity, Variational Inference

Stochastic variational inference allows for fast posterior inference in
complex Bayesian models. However, the algorithm is prone to local optima which
can make the quality of the posterior approximation sensitive to the choice of
hyperparameters and initialization. We address this problem by replacing the
natural gradient step of stochastic varitional inference with a trust-region
update. We show that this leads to generally better results and reduced
sensitivity to hyperparameters. We also describe a new strategy for variational
inference on streaming data and show that here our trust-region method is
crucial for getting good performance.
Learning with Symmetric Label Noise: The Importance of Being Unhinged
Authors: Brendan van Rooyen, Aditya Krishna Menon, Robert C. Williamson
Date: 2015-05-28 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Binary Classification, Classification, General Classification

Convex potential minimisation is the de facto approach to binary
classification. However, Long and Servedio [2010] proved that under symmetric
label noise (SLN), minimisation of any convex potential over a linear function
class can result in classification performance equivalent to random guessing.
This ostensibly shows that convex losses are not SLN-robust. In this paper, we
propose a convex, classification-calibrated loss and prove that it is
SLN-robust. The loss avoids the Long and Servedio [2010] result by virtue of
being negatively unbounded. The loss is a modification of the hinge loss, where
one does not clamp at zero; hence, we call it the unhinged loss. We show that
the optimal unhinged solution is equivalent to that of a strongly regularised
SVM, and is the limiting solution for any convex potential; this implies that
strong l2 regularisation makes most standard learners SLN-robust. Experiments
confirm the SLN-robustness of the unhinged loss.
Spectral MLE: Top-$K$ Rank Aggregation from Pairwise Comparisons
Authors: Yuxin Chen, Changho Suh
Date: 2015-04-27 00:00:00

This paper explores the preference-based top-$K$ rank aggregation problem.
Suppose that a collection of items is repeatedly compared in pairs, and one
wishes to recover a consistent ordering that emphasizes the top-$K$ ranked
items, based on partially revealed preferences. We focus on the
Bradley-Terry-Luce (BTL) model that postulates a set of latent preference
scores underlying all items, where the odds of paired comparisons depend only
on the relative scores of the items involved.
  We characterize the minimax limits on identifiability of top-$K$ ranked
items, in the presence of random and non-adaptive sampling. Our results
highlight a separation measure that quantifies the gap of preference scores
between the $K^{\text{th}}$ and $(K+1)^{\text{th}}$ ranked items. The minimum
sample complexity required for reliable top-$K$ ranking scales inversely with
the separation measure irrespective of other preference distribution metrics.
To approach this minimax limit, we propose a nearly linear-time ranking scheme,
called \emph{Spectral MLE}, that returns the indices of the top-$K$ items in
accordance to a careful score estimate. In a nutshell, Spectral MLE starts with
an initial score estimate with minimal squared loss (obtained via a spectral
method), and then successively refines each component with the assistance of
coordinate-wise MLEs. Encouragingly, Spectral MLE allows perfect top-$K$ item
identification under minimal sample complexity. The practical applicability of
Spectral MLE is further corroborated by numerical experiments.
Multidefender Security Games
Authors: Jian Lou, Andrew M. Smith, Yevgeniy Vorobeychik
Date: 2015-05-28 00:00:00

Stackelberg security game models and associated computational tools have seen
deployment in a number of high-consequence security settings, such as LAX
canine patrols and Federal Air Marshal Service. These models focus on isolated
systems with only one defender, despite being part of a more complex system
with multiple players. Furthermore, many real systems such as transportation
networks and the power grid exhibit interdependencies between targets and,
consequently, between decision makers jointly charged with protecting them. To
understand such multidefender strategic interactions present in security, we
investigate game theoretic models of security games with multiple defenders.
Unlike most prior analysis, we focus on the situations in which each defender
must protect multiple targets, so that even a single defender's best response
decision is, in general, highly non-trivial. We start with an analytical
investigation of multidefender security games with independent targets,
offering an equilibrium and price-of-anarchy analysis of three models with
increasing generality. In all models, we find that defenders have the incentive
to over-protect targets, at times significantly. Additionally, in the simpler
models, we find that the price of anarchy is unbounded, linearly increasing
both in the number of defenders and the number of targets per defender.
Considering interdependencies among targets, we develop a novel mixed-integer
linear programming formulation to compute a defender's best response, and make
use of this formulation in approximating Nash equilibria of the game. We apply
this approach towards computational strategic analysis of several models of
networks representing interdependencies, including real-world power networks.
Our analysis shows how network structure and the probability of failure spread
determine the propensity of defenders to over- or under-invest in security.
Like Partying? Your Face Says It All. Predicting the Ambiance of Places with Profile Pictures
Authors: Miriam Redi, Daniele Quercia, Lindsay T. Graham, Samuel D. Gosling
Date: 2015-05-28 00:00:00
Tasks: All

To choose restaurants and coffee shops, people are increasingly relying on
social-networking sites. In a popular site such as Foursquare or Yelp, a place
comes with descriptions and reviews, and with profile pictures of people who
frequent them. Descriptions and reviews have been widely explored in the
research area of data mining. By contrast, profile pictures have received
little attention. Previous work showed that people are able to partly guess a
place's ambiance, clientele, and activities not only by observing the place
itself but also by observing the profile pictures of its visitors. Here we
further that work by determining which visual cues people may have relied upon
to make their guesses; showing that a state-of-the-art algorithm could make
predictions more accurately than humans at times; and demonstrating that the
visual cues people relied upon partly differ from those of the algorithm.
A deep-structured fully-connected random field model for structured inference
Authors: Alexander Wong, Mohammad Javad Shafiee, Parthipan Siva, Xiao Yu Wang
Date: 2014-12-20 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

There has been significant interest in the use of fully-connected graphical
models and deep-structured graphical models for the purpose of structured
inference. However, fully-connected and deep-structured graphical models have
been largely explored independently, leaving the unification of these two
concepts ripe for exploration. A fundamental challenge with unifying these two
types of models is in dealing with computational complexity. In this study, we
investigate the feasibility of unifying fully-connected and deep-structured
models in a computationally tractable manner for the purpose of structured
inference. To accomplish this, we introduce a deep-structured fully-connected
random field (DFRF) model that integrates a series of intermediate sparse
auto-encoding layers placed between state layers to significantly reduce
computational complexity. The problem of image segmentation was used to
illustrate the feasibility of using the DFRF for structured inference in a
computationally tractable manner. Results in this study show that it is
feasible to unify fully-connected and deep-structured models in a
computationally tractable manner for solving structured inference problems such
as image segmentation.
Robots that can adapt like animals
Authors: Antoine Cully, Jeff Clune, Danesh Tarapore, Jean-Baptiste Mouret
Date: 2014-07-13 00:00:00

As robots leave the controlled environments of factories to autonomously
function in more complex, natural environments, they will have to respond to
the inevitable fact that they will become damaged. However, while animals can
quickly adapt to a wide variety of injuries, current robots cannot "think
outside the box" to find a compensatory behavior when damaged: they are limited
to their pre-specified self-sensing abilities, can diagnose only anticipated
failure modes, and require a pre-programmed contingency plan for every type of
potential damage, an impracticality for complex robots. Here we introduce an
intelligent trial and error algorithm that allows robots to adapt to damage in
less than two minutes, without requiring self-diagnosis or pre-specified
contingency plans. Before deployment, a robot exploits a novel algorithm to
create a detailed map of the space of high-performing behaviors: This map
represents the robot's intuitions about what behaviors it can perform and their
value. If the robot is damaged, it uses these intuitions to guide a
trial-and-error learning algorithm that conducts intelligent experiments to
rapidly discover a compensatory behavior that works in spite of the damage.
Experiments reveal successful adaptations for a legged robot injured in five
different ways, including damaged, broken, and missing legs, and for a robotic
arm with joints broken in 14 different ways. This new technique will enable
more robust, effective, autonomous robots, and suggests principles that animals
may use to adapt to injury.
Supervised detection of anomalous light-curves in massive astronomical catalogs
Authors: Isadora Nun, Karim Pichara, Pavlos Protopapas, Dae-Won Kim
Date: 2014-04-18 00:00:00

The development of synoptic sky surveys has led to a massive amount of data
for which resources needed for analysis are beyond human capabilities. To
process this information and to extract all possible knowledge, machine
learning techniques become necessary. Here we present a new method to
automatically discover unknown variable objects in large astronomical catalogs.
With the aim of taking full advantage of all the information we have about
known objects, our method is based on a supervised algorithm. In particular, we
train a random forest classifier using known variability classes of objects and
obtain votes for each of the objects in the training set. We then model this
voting distribution with a Bayesian network and obtain the joint voting
distribution among the training objects. Consequently, an unknown object is
considered as an outlier insofar it has a low joint probability. Our method is
suitable for exploring massive datasets given that the training process is
performed offline. We tested our algorithm on 20 millions light-curves from the
MACHO catalog and generated a list of anomalous candidates. We divided the
candidates into two main classes of outliers: artifacts and intrinsic outliers.
Artifacts were principally due to air mass variation, seasonal variation, bad
calibration or instrumental errors and were consequently removed from our
outlier list and added to the training set. After retraining, we selected about
4000 objects, which we passed to a post analysis stage by perfoming a
cross-match with all publicly available catalogs. Within these candidates we
identified certain known but rare objects such as eclipsing Cepheids, blue
variables, cataclysmic variables and X-ray sources. For some outliers there
were no additional information. Among them we identified three unknown
variability types and few individual outliers that will be followed up for a
deeper analysis.
Compositional Vector Space Models for Knowledge Base Completion
Authors: Arvind Neelakantan, Benjamin Roth, Andrew McCallum
Date: 2015-04-24 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Knowledge Base Completion, Relation, Zero-Shot Learning

Knowledge base (KB) completion adds new facts to a KB by making inferences
from existing facts, for example by inferring with high likelihood
nationality(X,Y) from bornIn(X,Y). Most previous methods infer simple one-hop
relational synonyms like this, or use as evidence a multi-hop relational path
treated as an atomic feature, like bornIn(X,Z) -> containedIn(Z,Y). This paper
presents an approach that reasons about conjunctions of multi-hop relations
non-atomically, composing the implications of a path using a recursive neural
network (RNN) that takes as inputs vector embeddings of the binary relation in
the path. Not only does this allow us to generalize to paths unseen at training
time, but also, with a single high-capacity RNN, to predict new relation types
not seen when the compositional model was trained (zero-shot learning). We
assemble a new dataset of over 52M relational triples, and show that our method
improves over a traditional classifier by 11%, and a method leveraging
pre-trained embeddings by 7%.
Post-acquisition image based compensation for thickness variation in microscopy section series
Authors: Philipp Hanslovsky, John A. Bogovic, Stephan Saalfeld
Date: 2014-11-25 00:00:00
Tasks: Anatomy, Position

Serial section Microscopy is an established method for volumetric anatomy
reconstruction. Section series imaged with Electron Microscopy are currently
vital for the reconstruction of the synaptic connectivity of entire animal
brains such as that of Drosophila melanogaster. The process of removing
ultrathin layers from a solid block containing the specimen, however, is a
fragile procedure and has limited precision with respect to section thickness.
We have developed a method to estimate the relative z-position of each
individual section as a function of signal change across the section series.
First experiments show promising results on both serial section Transmission
Electron Microscopy (ssTEM) data and Focused Ion Beam Scanning Electron
Microscopy (FIB-SEM) series. We made our solution available as Open Source
plugins for the TrakEM2 software and the ImageJ distribution Fiji.
Training a Convolutional Neural Network for Appearance-Invariant Place Recognition
Authors: Ruben Gomez-Ojeda, Manuel Lopez-Antequera, Nicolai Petkov, Javier Gonzalez-Jimenez
Date: 2015-05-27 00:00:00
Tasks: Autonomous Driving

Place recognition is one of the most challenging problems in computer vision,
and has become a key part in mobile robotics and autonomous driving
applications for performing loop closure in visual SLAM systems. Moreover, the
difficulty of recognizing a revisited location increases with appearance
changes caused, for instance, by weather or illumination variations, which
hinders the long-term application of such algorithms in real environments. In
this paper we present a convolutional neural network (CNN), trained for the
first time with the purpose of recognizing revisited locations under severe
appearance changes, which maps images to a low dimensional space where
Euclidean distances represent place dissimilarity. In order for the network to
learn the desired invariances, we train it with triplets of images selected
from datasets which present a challenging variability in visual appearance. The
triplets are selected in such way that two samples are from the same location
and the third one is taken from a different place. We validate our system
through extensive experimentation, where we demonstrate better performance than
state-of-art algorithms in a number of popular datasets.
Improving Spatial Codification in Semantic Segmentation
Authors: Carles Ventura, Xavier Giró-i-Nieto, Verónica Vilaplana, Kevin McGuinness, Ferran Marqués, Noel E. O'Connor
Date: 2015-05-27 00:00:00
Tasks: Object, Segmentation, Semantic Segmentation

This paper explores novel approaches for improving the spatial codification
for the pooling of local descriptors to solve the semantic segmentation
problem. We propose to partition the image into three regions for each object
to be described: Figure, Border and Ground. This partition aims at minimizing
the influence of the image context on the object description and vice versa by
introducing an intermediate zone around the object contour. Furthermore, we
also propose a richer visual descriptor of the object by applying a Spatial
Pyramid over the Figure region. Two novel Spatial Pyramid configurations are
explored: Cartesian-based and crown-based Spatial Pyramids. We test these
approaches with state-of-the-art techniques and show that they improve the
Figure-Ground based pooling in the Pascal VOC 2011 and 2012 semantic
segmentation challenges.
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling
Authors: Vijay Badrinarayanan, Ankur Handa, Roberto Cipolla
Date: 2015-05-27 00:00:00
Tasks: Decoder, Object Categorization

We propose a novel deep architecture, SegNet, for semantic pixel wise image
labelling. SegNet has several attractive properties; (i) it only requires
forward evaluation of a fully learnt function to obtain smooth label
predictions, (ii) with increasing depth, a larger context is considered for
pixel labelling which improves accuracy, and (iii) it is easy to visualise the
effect of feature activation(s) in the pixel label space at any depth. SegNet
is composed of a stack of encoders followed by a corresponding decoder stack
which feeds into a soft-max classification layer. The decoders help map low
resolution feature maps at the output of the encoder stack to full input image
size feature maps. This addresses an important drawback of recent deep learning
approaches which have adopted networks designed for object categorization for
pixel wise labelling. These methods lack a mechanism to map deep layer feature
maps to input dimensions. They resort to ad hoc methods to upsample features,
e.g. by replication. This results in noisy predictions and also restricts the
number of pooling layers in order to avoid too much upsampling and thus reduces
spatial context. SegNet overcomes these problems by learning to map encoder
outputs to image pixel labels. We test the performance of SegNet on outdoor RGB
scenes from CamVid, KITTI and indoor scenes from the NYU dataset. Our results
show that SegNet achieves state-of-the-art performance even without use of
additional cues such as depth, video frames or post-processing with CRF models.