pwc-archive/papers-with-abstracts huggingface dataset:
Plücker Correction Problem: Analysis and Improvements in Efficiency
Authors: João R. Cardoso, Pedro Miraldo, Helder Araujo
Date: 2016-02-18 00:00:00

A given six dimensional vector represents a 3D straight line in Plucker
coordinates if its coordinates satisfy the Klein quadric constraint. In many
problems aiming to find the Plucker coordinates of lines, noise in the data
and other type of errors contribute for obtaining 6D vectors that do not
correspond to lines, because of that constraint. A common procedure to overcome
this drawback is to find the Plucker coordinates of the lines that are closest
to those vectors. This is known as the Plucker correction problem. In this
article we propose a simple, closed-form, and global solution for this problem.
When compared with the state-of-the-art method, one can conclude that our
algorithm is easier and requires much less operations than previous techniques
(it does not require Singular Value Decomposition techniques).
The Interaction of Memory and Attention in Novel Word Generalization: A Computational Investigation
Authors: Erin Grant, Aida Nematzadeh, Suzanne Stevenson
Date: 2016-02-18 00:00:00

People exhibit a tendency to generalize a novel noun to the basic-level in a
hierarchical taxonomy -- a cognitively salient category such as "dog" -- with
the degree of generalization depending on the number and type of exemplars.
Recently, a change in the presentation timing of exemplars has also been shown
to have an effect, surprisingly reversing the prior observed pattern of
basic-level generalization. We explore the precise mechanisms that could lead
to such behavior by extending a computational model of word learning and word
generalization to integrate cognitive processes of memory and attention. Our
results show that the interaction of forgetting and attention to novelty, as
well as sensitivity to both type and token frequencies of exemplars, enables
the model to replicate the empirical results from different presentation
timings. Our results reinforce the need to incorporate general cognitive
processes within word learning models to better understand the range of
observed behaviors in vocabulary acquisition.
Multi-resolution Compressive Sensing Reconstruction
Authors: Adriana Gonzalez, Hong Jiang, Gang Huang, Laurent Jacques
Date: 2016-02-18 00:00:00
Tasks: Compressive Sensing

We consider the problem of reconstructing an image from compressive
measurements using a multi-resolution grid. In this context, the reconstructed
image is divided into multiple regions, each one with a different resolution.
This problem arises in situations where the image to reconstruct contains a
certain region of interest (RoI) that is more important than the rest. Through
a theoretical analysis and simulation experiments we show that the
multi-resolution reconstruction provides a higher quality of the RoI compared
to the traditional single-resolution approach.
Multimodal Transfer Deep Learning with Applications in Audio-Visual Recognition
Authors: Seungwhan Moon, Suyoun Kim, Haohan Wang
Date: 2014-12-09 00:00:00
Tasks: Deep Learning, Video Recognition

We propose a transfer deep learning (TDL) framework that can transfer the
knowledge obtained from a single-modal neural network to a network with a
different modality. Specifically, we show that we can leverage speech data to
fine-tune the network trained for video recognition, given an initial set of
audio-video parallel dataset within the same semantics. Our approach first
learns the analogy-preserving embeddings between the abstract representations
learned from intermediate layers of each network, allowing for semantics-level
transfer between the source and target modalities. We then apply our neural
network operation that fine-tunes the target network with the additional
knowledge transferred from the source network, while keeping the topology of
the target network unchanged. While we present an audio-visual recognition task
as an application of our approach, our framework is flexible and thus can work
with any multimodal dataset, or with any already-existing deep networks that
share the common underlying semantics. In this work in progress report, we aim
to provide comprehensive results of different configurations of the proposed
approach on two widely used audio-visual datasets, and we discuss potential
applications of the proposed approach.
Encoding Data for HTM Systems
Authors: Scott Purdy
Date: 2016-02-18 00:00:00

Hierarchical Temporal Memory (HTM) is a biologically inspired machine
intelligence technology that mimics the architecture and processes of the
neocortex. In this white paper we describe how to encode data as Sparse
Distributed Representations (SDRs) for use in HTM systems. We explain several
existing encoders, which are available through the open source project called
NuPIC, and we discuss requirements for creating encoders for new types of data.
Mismatch in the Classification of Linear Subspaces: Sufficient Conditions for Reliable Classification
Authors: Jure Sokolic, Francesco Renna, Robert Calderbank, Miguel R. D. Rodrigues
Date: 2015-08-07 00:00:00
Tasks: Classification, General Classification, Motion Segmentation

This paper considers the classification of linear subspaces with mismatched
classifiers. In particular, we assume a model where one observes signals in the
presence of isotropic Gaussian noise and the distribution of the signals
conditioned on a given class is Gaussian with a zero mean and a low-rank
covariance matrix. We also assume that the classifier knows only a mismatched
version of the parameters of input distribution in lieu of the true parameters.
By constructing an asymptotic low-noise expansion of an upper bound to the
error probability of such a mismatched classifier, we provide sufficient
conditions for reliable classification in the low-noise regime that are able to
sharply predict the absence of a classification error floor. Such conditions
are a function of the geometry of the true signal distribution, the geometry of
the mismatched signal distributions as well as the interplay between such
geometries, namely, the principal angles and the overlap between the true and
the mismatched signal subspaces. Numerical results demonstrate that our
conditions for reliable classification can sharply predict the behavior of a
mismatched classifier both with synthetic data and in a motion segmentation and
a hand-written digit classification applications.
Efficient approaches for escaping higher order saddle points in non-convex optimization
Authors: Anima Anandkumar, Rong Ge
Date: 2016-02-18 00:00:00

Local search heuristics for non-convex optimizations are popular in applied
machine learning. However, in general it is hard to guarantee that such
algorithms even converge to a local minimum, due to the existence of
complicated saddle point structures in high dimensions. Many functions have
degenerate saddle points such that the first and second order derivatives
cannot distinguish them with local optima. In this paper we use higher order
derivatives to escape these saddle points: we design the first efficient
algorithm guaranteed to converge to a third order local optimum (while existing
techniques are at most second order). We also show that it is NP-hard to extend
this further to finding fourth order local optima.
Noisy Tensor Completion via the Sum-of-Squares Hierarchy
Authors: Boaz Barak, Ankur Moitra
Date: 2015-01-26 00:00:00
Tasks: Matrix Completion

In the noisy tensor completion problem we observe $m$ entries (whose location
is chosen uniformly at random) from an unknown $n_1 \times n_2 \times n_3$
tensor $T$. We assume that $T$ is entry-wise close to being rank $r$. Our goal
is to fill in its missing entries using as few observations as possible. Let $n
= \max(n_1, n_2, n_3)$. We show that if $m = n^{3/2} r$ then there is a
polynomial time algorithm based on the sixth level of the sum-of-squares
hierarchy for completing it. Our estimate agrees with almost all of $T$'s
entries almost exactly and works even when our observations are corrupted by
noise. This is also the first algorithm for tensor completion that works in the
overcomplete case when $r > n$, and in fact it works all the way up to $r =
n^{3/2-\epsilon}$.
  Our proofs are short and simple and are based on establishing a new
connection between noisy tensor completion (through the language of Rademacher
complexity) and the task of refuting random constant satisfaction problems.
This connection seems to have gone unnoticed even in the context of matrix
completion. Furthermore, we use this connection to show matching lower bounds.
Our main technical result is in characterizing the Rademacher complexity of the
sequence of norms that arise in the sum-of-squares relaxations to the tensor
nuclear norm. These results point to an interesting new direction: Can we
explore computational vs. sample complexity tradeoffs through the
sum-of-squares hierarchy?
Metric learning approach for graph-based label propagation
Authors: Pauline Wauquier, Mikaela Keller
Date: 2015-11-18 00:00:00
Tasks: Metric Learning

The efficiency of graph-based semi-supervised algorithms depends on the graph
of instances on which they are applied. The instances are often in a vectorial
form before a graph linking them is built. The construction of the graph relies
on a metric over the vectorial space that help define the weight of the
connection between entities. The classic choice for this metric is usually a
distance measure or a similarity measure based on the euclidean norm. We claim
that in some cases the euclidean norm on the initial vectorial space might not
be the more appropriate to solve the task efficiently. We propose an algorithm
that aims at learning the most appropriate vectorial representation for
building a graph on which the task at hand is solved efficiently.
A General Modifier-based Framework for Inconsistency-Tolerant Query Answering
Authors: Jean Francois Baget, Salem Benferhat, Zied Bouraoui, Madalina Croitoru, Marie-Laure Mugnier, Odile Papini, Swan Rocher, Karim Tabia
Date: 2016-02-18 00:00:00

We propose a general framework for inconsistency-tolerant query answering
within existential rule setting. This framework unifies the main semantics
proposed by the state of art and introduces new ones based on cardinality and
majority principles. It relies on two key notions: modifiers and inference
strategies. An inconsistency-tolerant semantics is seen as a composite modifier
plus an inference strategy. We compare the obtained semantics from a
productivity point of view.
What is the distribution of the number of unique original items in a bootstrap sample?
Authors: Alex F. Mendelson, Maria A. Zuluaga, Brian F. Hutton, Sébastien Ourselin
Date: 2016-02-18 00:00:00
Tasks: BIG-bench Machine Learning

Sampling with replacement occurs in many settings in machine learning,
notably in the bagging ensemble technique and the .632+ validation scheme. The
number of unique original items in a bootstrap sample can have an important
role in the behaviour of prediction models learned on it. Indeed, there are
uncontrived examples where duplicate items have no effect. The purpose of this
report is to present the distribution of the number of unique original items in
a bootstrap sample clearly and concisely, with a view to enabling other machine
learning researchers to understand and control this quantity in existing and
future resampling techniques. We describe the key characteristics of this
distribution along with the generalisation for the case where items come from
distinct categories, as in classification. In both cases we discuss the normal
limit, and conduct an empirical investigation to derive a heuristic for when a
normal approximation is permissible.
PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
Authors: Alex Kendall, Matthew Grimes, Roberto Cipolla
Date: 2015-05-27 00:00:00
Proceeding: ICCV 2015 12
Tasks: Camera Relocalization, Transfer Learning

We present a robust and real-time monocular six degree of freedom
relocalization system. Our system trains a convolutional neural network to
regress the 6-DOF camera pose from a single RGB image in an end-to-end manner
with no need of additional engineering or graph optimisation. The algorithm can
operate indoors and outdoors in real time, taking 5ms per frame to compute. It
obtains approximately 2m and 6 degree accuracy for large scale outdoor scenes
and 0.5m and 10 degree accuracy indoors. This is achieved using an efficient 23
layer deep convnet, demonstrating that convnets can be used to solve
complicated out of image plane regression problems. This was made possible by
leveraging transfer learning from large scale classification data. We show the
convnet localizes from high level features and is robust to difficult lighting,
motion blur and different camera intrinsics where point based SIFT registration
fails. Furthermore we show how the pose feature that is produced generalizes to
other scenes allowing us to regress pose with only a few dozen training
examples. PoseNet code, dataset and an online demonstration is available on our
project webpage, at http://mi.eng.cam.ac.uk/projects/relocalisation/
Modelling Uncertainty in Deep Learning for Camera Relocalization
Authors: Alex Kendall, Roberto Cipolla
Date: 2015-09-19 00:00:00
Tasks: Camera Relocalization, Deep Learning

We present a robust and real-time monocular six degree of freedom visual
relocalization system. We use a Bayesian convolutional neural network to
regress the 6-DOF camera pose from a single RGB image. It is trained in an
end-to-end manner with no need of additional engineering or graph optimisation.
The algorithm can operate indoors and outdoors in real time, taking under 6ms
to compute. It obtains approximately 2m and 6 degrees accuracy for very large
scale outdoor scenes and 0.5m and 10 degrees accuracy indoors. Using a Bayesian
convolutional neural network implementation we obtain an estimate of the
model's relocalization uncertainty and improve state of the art localization
accuracy on a large scale outdoor dataset. We leverage the uncertainty measure
to estimate metric relocalization error and to detect the presence or absence
of the scene in the input image. We show that the model's uncertainty is caused
by images being dissimilar to the training dataset in either pose or
appearance.
Corpus analysis without prior linguistic knowledge - unsupervised mining of phrases and subphrase structure
Authors: Stefan Gerdjikov, Klaus U. Schulz
Date: 2016-02-18 00:00:00

When looking at the structure of natural language, "phrases" and "words" are
central notions. We consider the problem of identifying such "meaningful
subparts" of language of any length and underlying composition principles in a
completely corpus-based and language-independent way without using any kind of
prior linguistic knowledge. Unsupervised methods for identifying "phrases",
mining subphrase structure and finding words in a fully automated way are
described. This can be considered as a step towards automatically computing a
"general dictionary and grammar of the corpus". We hope that in the long run
variants of our approach turn out to be useful for other kind of sequence data
as well, such as, e.g., speech, genom sequences, or music annotation. Even if
we are not primarily interested in immediate applications, results obtained for
a variety of languages show that our methods are interesting for many practical
tasks in text mining, terminology extraction and lexicography, search engine
technology, and related fields.
Overview of Annotation Creation: Processes & Tools
Authors: Mark A. Finlayson, Tomaž Erjavec
Date: 2016-02-18 00:00:00

Creating linguistic annotations requires more than just a reliable annotation
scheme. Annotation can be a complex endeavour potentially involving many
people, stages, and tools. This chapter outlines the process of creating
end-to-end linguistic annotations, identifying specific tasks that researchers
often perform. Because tool support is so central to achieving high quality,
reusable annotations with low cost, the focus is on identifying capabilities
that are necessary or useful for annotation tools, as well as common problems
these tools present that reduce their utility. Although examples of specific
tools are provided in many cases, this chapter concentrates more on abstract
capabilities and problems because new tools appear continuously, while old
tools disappear into disuse or disrepair. The two core capabilities tools must
have are support for the chosen annotation scheme and the ability to work on
the language under study. Additional capabilities are organized into three
categories: those that are widely provided; those that often useful but found
in only a few tools; and those that have as yet little or no available tool
support.
An improved analysis of the ER-SpUD dictionary learning algorithm
Authors: Jarosław Błasiok, Jelani Nelson
Date: 2016-02-18 00:00:00
Tasks: Dictionary Learning

In "dictionary learning" we observe $Y = AX + E$ for some
$Y\in\mathbb{R}^{n\times p}$, $A \in\mathbb{R}^{m\times n}$, and
$X\in\mathbb{R}^{m\times p}$. The matrix $Y$ is observed, and $A, X, E$ are
unknown. Here $E$ is "noise" of small norm, and $X$ is column-wise sparse. The
matrix $A$ is referred to as a {\em dictionary}, and its columns as {\em
atoms}. Then, given some small number $p$ of samples, i.e.\ columns of $Y$, the
goal is to learn the dictionary $A$ up to small error, as well as $X$. The
motivation is that in many applications data is expected to sparse when
represented by atoms in the "right" dictionary $A$ (e.g.\ images in the Haar
wavelet basis), and the goal is to learn $A$ from the data to then use it for
other applications.
  Recently, [SWW12] proposed the dictionary learning algorithm ER-SpUD with
provable guarantees when $E = 0$ and $m = n$. They showed if $X$ has
independent entries with an expected $s$ non-zeroes per column for $1 \lesssim
s \lesssim \sqrt{n}$, and with non-zero entries being subgaussian, then for
$p\gtrsim n^2\log^2 n$ with high probability ER-SpUD outputs matrices $A', X'$
which equal $A, X$ up to permuting and scaling columns (resp.\ rows) of $A$
(resp.\ $X$). They conjectured $p\gtrsim n\log n$ suffices, which they showed
was information theoretically necessary for {\em any} algorithm to succeed when
$s \simeq 1$. Significant progress was later obtained in [LV15].
  We show that for a slight variant of ER-SpUD, $p\gtrsim n\log(n/\delta)$
samples suffice for successful recovery with probability $1-\delta$. We also
show that for the unmodified ER-SpUD, $p\gtrsim n^{1.99}$ samples are required
even to learn $A, X$ with polynomially small success probability. This resolves
the main conjecture of [SWW12], and contradicts the main result of [LV15],
which claimed that $p\gtrsim n\log^4 n$ guarantees success whp.
A theory of contemplation
Authors: Jonathan Darren Nix
Date: 2016-02-18 00:00:00
Tasks: Form, Probabilistic Programming

In this paper you can explore the application of some notable Boolean-derived methods, namely the Disjunctive Normal Form representation of logic table expansions, and extend them to a real-valued logic model which is able to utilize quantities on the range [0,1], [-1,1], [a,b], (x,y), (x,y,z), and etc. so as to produce a logical programming of arbitrary range, precision, and dimensionality, thereby enabling contemplation at a logical level in notions of arbitrary data, colors, and spatial constructs, with an example of the production of a game character's logic in mathematical form.
Query Answering with Inconsistent Existential Rules under Stable Model Semantics
Authors: Hai Wan, Heng Zhang, Peng Xiao, Haoran Huang, Yan Zhang
Date: 2016-02-18 00:00:00

Traditional inconsistency-tolerent query answering in ontology-based data
access relies on selecting maximal components of an ABox/database which are
consistent with the ontology. However, some rules in ontologies might be
unreliable if they are extracted from ontology learning or written by
unskillful knowledge engineers. In this paper we present a framework of
handling inconsistent existential rules under stable model semantics, which is
defined by a notion called rule repairs to select maximal components of the
existential rules. Surprisingly, for R-acyclic existential rules with
R-stratified or guarded existential rules with stratified negations, both the
data complexity and combined complexity of query answering under the rule
{repair semantics} remain the same as that under the conventional query
answering semantics. This leads us to propose several approaches to handle the
rule {repair semantics} by calling answer set programming solvers. An
experimental evaluation shows that these approaches have good scalability of
query answering under rule repairs on realistic cases.
Multi-Sensor Slope Change Detection
Authors: Yang Cao, Yao Xie, Nagi Gebraeel
Date: 2015-09-01 00:00:00
Tasks: Change Detection

We develop a mixture procedure for multi-sensor systems to monitor data
streams for a change-point that causes a gradual degradation to a subset of the
streams. Observations are assumed to be initially normal random variables with
known constant means and variances. After the change-point, observations in the
subset will have increasing or decreasing means. The subset and the
rate-of-changes are unknown. Our procedure uses a mixture statistics, which
assumes that each sensor is affected by the change-point with probability
$p_0$. Analytic expressions are obtained for the average run length (ARL) and
the expected detection delay (EDD) of the mixture procedure, which are
demonstrated to be quite accurate numerically. We establish the asymptotic
optimality of the mixture procedure. Numerical examples demonstrate the good
performance of the proposed procedure. We also discuss an adaptive mixture
procedure using empirical Bayes. This paper extends our earlier work on
detecting an abrupt change-point that causes a mean-shift, by tackling the
challenges posed by the non-stationarity of the slope-change problem.
Feature-Area Optimization: A Novel SAR Image Registration Method
Authors: Fuqiang Liu, Fukun Bi, Liang Chen, Hao Shi, Wei Liu
Date: 2016-02-18 00:00:00
Tasks: Image Registration

This letter proposes a synthetic aperture radar (SAR) image registration
method named Feature-Area Optimization (FAO). First, the traditional area-based
optimization model is reconstructed and decomposed into three key but uncertain
factors: initialization, slice set and regularization. Next, structural
features are extracted by scale invariant feature transform (SIFT) in
dual-resolution space (SIFT-DRS), a novel SIFT-Like method dedicated to FAO.
Then, the three key factors are determined based on these features. Finally,
solving the factor-determined optimization model can get the registration
result. A series of experiments demonstrate that the proposed method can
register multi-temporal SAR images accurately and efficiently.
Visual Tracking via Reliable Memories
Authors: Shu Wang, Shaoting Zhang, Wei Liu, Dimitris N. Metaxas
Date: 2016-02-04 00:00:00
Tasks: Clustering, Visual Tracking

In this paper, we propose a novel visual tracking framework that
intelligently discovers reliable patterns from a wide range of video to resist
drift error for long-term tracking tasks. First, we design a Discrete Fourier
Transform (DFT) based tracker which is able to exploit a large number of
tracked samples while still ensures real-time performance. Second, we propose a
clustering method with temporal constraints to explore and memorize consistent
patterns from previous frames, named as reliable memories. By virtue of this
method, our tracker can utilize uncontaminated information to alleviate
drifting issues. Experimental results show that our tracker performs favorably
against other state of-the-art methods on benchmark datasets. Furthermore, it
is significantly competent in handling drifts and able to robustly track
challenging long videos over 4000 frames, while most of others lose track at
early frames.