pwc-archive/papers-with-abstracts huggingface dataset:
Multilevel Monte Carlo methods for the approximation of invariant measures of stochastic differential equations
Authors: Michael B. Giles, Mateusz B. Majka, Lukasz Szpruch, Sebastian Vollmer, Konstantinos Zygalakis
Date: 2016-05-04 00:00:00

We develop a framework that allows the use of the multi-level Monte Carlo (MLMC) methodology (Giles2015) to calculate expectations with respect to the invariant measure of an ergodic SDE. In that context, we study the (over-damped) Langevin equations with a strongly concave potential. We show that, when appropriate contracting couplings for the numerical integrators are available, one can obtain a uniform in time estimate of the MLMC variance in contrast to the majority of the results in the MLMC literature. As a consequence, a root mean square error of $\mathcal{O}(\varepsilon)$ is achieved with $\mathcal{O}(\varepsilon^{-2})$ complexity on par with Markov Chain Monte Carlo (MCMC) methods, which however can be computationally intensive when applied to large data sets. Finally, we present a multi-level version of the recently introduced Stochastic Gradient Langevin Dynamics (SGLD) method (Welling and Teh, 2011) built for large datasets applications. We show that this is the first stochastic gradient MCMC method with complexity $\mathcal{O}(\varepsilon^{-2}|\log {\varepsilon}|^{3})$, in contrast to the complexity $\mathcal{O}(\varepsilon^{-3})$ of currently available methods. Numerical experiments confirm our theoretical findings.
A Data-driven Approach for Human Pose Tracking Based on Spatio-temporal Pictorial Structure
Authors: Soumitra Samanta, Bhabatosh Chanda
Date: 2016-07-31 00:00:00
Tasks: Object Tracking, Pose Estimation, Pose Tracking

In this paper, we present a data-driven approach for human pose tracking in
video data. We formulate the human pose tracking problem as a discrete
optimization problem based on spatio-temporal pictorial structure model and
solve this problem in a greedy framework very efficiently. We propose the model
to track the human pose by combining the human pose estimation from single
image and traditional object tracking in a video. Our pose tracking objective
function consists of the following terms: likeliness of appearance of a part
within a frame, temporal displacement of the part from previous frame to the
current frame, and the spatial dependency of a part with its parent in the
graph structure. Experimental evaluation on benchmark datasets (VideoPose2,
Poses in the Wild and Outdoor Pose) as well as on our newly build ICDPose
dataset shows the usefulness of our proposed method.
Visual Relationship Detection with Language Priors
Authors: Cewu Lu, Ranjay Krishna, Michael Bernstein, Li Fei-Fei
Date: 2016-07-31 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Relationship Detection, Retrieval, Visual Relationship Detection, Word Embeddings

Visual relationships capture a wide variety of interactions between pairs of
objects in images (e.g. "man riding bicycle" and "man pushing bicycle").
Consequently, the set of possible relationships is extremely large and it is
difficult to obtain sufficient training examples for all possible
relationships. Because of this limitation, previous work on visual relationship
detection has concentrated on predicting only a handful of relationships.
Though most relationships are infrequent, their objects (e.g. "man" and
"bicycle") and predicates (e.g. "riding" and "pushing") independently occur
more frequently. We propose a model that uses this insight to train visual
models for objects and predicates individually and later combines them together
to predict multiple relationships per image. We improve on prior work by
leveraging language priors from semantic word embeddings to finetune the
likelihood of a predicted relationship. Our model can scale to predict
thousands of types of relationships from a few examples. Additionally, we
localize the objects in the predicted relationships as bounding boxes in the
image. We further demonstrate that understanding relationships can improve
content based image retrieval.
Deep FisherNet for Object Classification
Authors: Peng Tang, Xinggang Wang, Baoguang Shi, Xiang Bai, Wenyu Liu, Zhuowen Tu
Date: 2016-07-31 00:00:00
Tasks: Classification, Computational Efficiency, General Classification, image-classification, Image Classification, Object

Despite the great success of convolutional neural networks (CNN) for the
image classification task on datasets like Cifar and ImageNet, CNN's
representation power is still somewhat limited in dealing with object images
that have large variation in size and clutter, where Fisher Vector (FV) has
shown to be an effective encoding strategy. FV encodes an image by aggregating
local descriptors with a universal generative Gaussian Mixture Model (GMM). FV
however has limited learning capability and its parameters are mostly fixed
after constructing the codebook. To combine together the best of the two
worlds, we propose in this paper a neural network structure with FV layer being
part of an end-to-end trainable system that is differentiable; we name our
network FisherNet that is learnable using backpropagation. Our proposed
FisherNet combines convolutional neural network training and Fisher Vector
encoding in a single end-to-end structure. We observe a clear advantage of
FisherNet over plain CNN and standard FV in terms of both classification
accuracy and computational efficiency on the challenging PASCAL VOC object
classification task.
Sparse vs. Non-sparse: Which One Is Better for Practical Visual Tracking?
Authors: Yashar Deldjoo, Shengping Zhang, Bahman Zanj, Paolo Cremonesi, Matteo Matteucci
Date: 2016-07-30 00:00:00
Tasks: Visual Tracking

Recently, sparse representation based visual tracking methods have attracted
increasing attention in the computer vision community. Although achieve
superior performance to traditional tracking methods, however, a basic problem
has not been answered yet --- that whether the sparsity constrain is really
needed for visual tracking? To answer this question, in this paper, we first
propose a robust non-sparse representation based tracker and then conduct
extensive experiments to compare it against several state-of-the-art sparse
representation based trackers. Our experiment results and analysis indicate
that the proposed non-sparse tracker achieved competitive tracking accuracy
with sparse trackers while having faster running speed, which support our
non-sparse tracker to be used in practical applications.
Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation
Authors: Golnaz Ghiasi, Charless C. Fowlkes
Date: 2016-05-08 00:00:00
Tasks: Segmentation, Semantic Segmentation

CNN architectures have terrific recognition performance but rely on spatial
pooling which makes it difficult to adapt them to tasks that require dense,
pixel-accurate labeling. This paper makes two contributions: (1) We demonstrate
that while the apparent spatial resolution of convolutional feature maps is
low, the high-dimensional feature representation contains significant sub-pixel
localization information. (2) We describe a multi-resolution reconstruction
architecture based on a Laplacian pyramid that uses skip connections from
higher resolution feature maps and multiplicative gating to successively refine
segment boundaries reconstructed from lower-resolution maps. This approach
yields state-of-the-art semantic segmentation results on the PASCAL VOC and
Cityscapes segmentation benchmarks without resorting to more complex
random-field inference or instance detection driven architectures.
Multi-task Learning with Weak Class Labels: Leveraging iEEG to Detect Cortical Lesions in Cryptogenic Epilepsy
Authors: Bilal Ahmed, Thomas Thesen, Karen E. Blackmon, Ruben Kuzniecky, Orrin Devinsky, Jennifer G. Dy, Carla E. Brodley
Date: 2016-07-30 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Multi-Task Learning

Multi-task learning (MTL) is useful for domains in which data originates from
multiple sources that are individually under-sampled. MTL methods are able to
learn classification models that have higher performance as compared to
learning a single model by aggregating all the data together or learning a
separate model for each data source. The performance of these methods relies on
label accuracy. We address the problem of simultaneously learning multiple
classifiers in the MTL framework when the training data has imprecise labels.
We assume that there is an additional source of information that provides a
score for each instance which reflects the certainty about its label. Modeling
this score as being generated by an underlying ranking function, we augment the
MTL framework with an added layer of supervision. This results in new MTL
methods that are able to learn accurate classifiers while preserving the domain
structure provided through the rank information. We apply these methods to the
task of detecting abnormal cortical regions in the MRIs of patients suffering
from focal epilepsy whose MRI were read as normal by expert neuroradiologists.
In addition to the noisy labels provided by the results of surgical resection,
we employ the results of an invasive intracranial-EEG exam as an additional
source of label information. Our proposed methods are able to successfully
detect abnormal regions for all patients in our dataset and achieve a higher
performance as compared to baseline methods.
Heterogeneous Strategy Particle Swarm Optimization
Authors: Wen-Bo Du, Wen Ying, Gang Yan, Yan-Bo Zhu, Xian-Bin Cao
Date: 2016-07-30 00:00:00
Tasks: Diversity

PSO is a widely recognized optimization algorithm inspired by social swarm.
In this brief we present a heterogeneous strategy particle swarm optimization
(HSPSO), in which a proportion of particles adopt a fully informed strategy to
enhance the converging speed while the rest are singly informed to maintain the
diversity. Our extensive numerical experiments show that HSPSO algorithm is
able to obtain satisfactory solutions, outperforming both PSO and the fully
informed PSO. The evolution process is examined from both structural and
microscopic points of view. We find that the cooperation between two types of
particles can facilitate a good balance between exploration and exploitation,
yielding better performance. We demonstrate the applicability of HSPSO on the
filter design problem.
Processing Natural Language About Ongoing Actions
Authors: Steve Doubleday, Sean Trott, Jerome Feldman
Date: 2016-07-23 00:00:00
Tasks: Natural Language Understanding

Actions may not proceed as planned; they may be interrupted, resumed or
overridden. This is a challenge to handle in a natural language understanding
system. We describe extensions to an existing implementation for the control of
autonomous systems by natural language, to enable such systems to handle
incoming language requests regarding actions. Language Communication with
Autonomous Systems (LCAS) has been extended with support for X-nets,
parameterized executable schemas representing actions. X-nets enable the system
to control actions at a desired level of granularity, while providing a
mechanism for language requests to be processed asynchronously. Standard
semantics supported include requests to stop, continue, or override the
existing action. The specific domain demonstrated is the control of motion of a
simulated robot, but the approach is general, and could be applied to other
domains.
Segmentation of Soft atherosclerotic plaques using active contour models
Authors: Muhammad Moazzam Jawaid
Date: 2016-07-30 00:00:00
Tasks: Segmentation

Detection of non-calcified plaques in the coronary tree is a challenging
problem due to the nature of comprising substances. Hard plaques are easily
discernible in CTA data cloud due to apparent bright behaviour, therefore many
approaches have been proposed for automatic segmentation of calcified plaques.
In contrast soft plaques show very small difference in intensity with respect
to surrounding heart tissues & blood voxels. This similarity in intensity makes
the isolation and detection of soft plaques very difficult. This work aims to
develop framework for segmentation of vulnerable plaques with minimal user
dependency. In first step automatic seed point has been established based on
the fact that coronary artery behaves as tubular structure through axial
slices. In the following step the behaviour of contrast agent has been modelled
mathematically to reflect the dye diffusion in respective CTA volume.
Consequently based on detected seed point & intensity behaviour, localized
active contour segmentation has been applied to extract complete coronary tree.
Bidirectional segmentation has been applied to avoid loss of coronary
information due to the seed point location whereas auto adjustment feature of
contour grabs new emerging branches. Medial axis for extracted coronary tree is
generated using fast marching method for obtaining curve planar reformation for
validation of contrast agent behaviour. Obtained coronary tree is to be
evaluated for soft plaques in second phase of this research.
Supervised Attentions for Neural Machine Translation
Authors: Haitao Mi, Zhiguo Wang, Abe Ittycheriah
Date: 2016-07-30 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Machine Translation, Sentence, Translation

In this paper, we improve the attention or alignment accuracy of neural
machine translation by utilizing the alignments of training sentence pairs. We
simply compute the distance between the machine attentions and the "true"
alignments, and minimize this cost in the training procedure. Our experiments
on large-scale Chinese-to-English task show that our model improves both
translation and alignment qualities significantly over the large-vocabulary
neural machine translation system, and even beats a state-of-the-art
traditional syntax-based system.
World Knowledge as Indirect Supervision for Document Clustering
Authors: Chenguang Wang, Yangqiu Song, Dan Roth, Ming Zhang, Jiawei Han
Date: 2016-07-30 00:00:00
Tasks: Clustering, World Knowledge

One of the key obstacles in making learning protocols realistic in
applications is the need to supervise them, a costly process that often
requires hiring domain experts. We consider the framework to use the world
knowledge as indirect supervision. World knowledge is general-purpose
knowledge, which is not designed for any specific domain. Then the key
challenges are how to adapt the world knowledge to domains and how to represent
it for learning. In this paper, we provide an example of using world knowledge
for domain dependent document clustering. We provide three ways to specify the
world knowledge to domains by resolving the ambiguity of the entities and their
types, and represent the data with world knowledge as a heterogeneous
information network. Then we propose a clustering algorithm that can cluster
multiple types and incorporate the sub-type information as constraints. In the
experiments, we use two existing knowledge bases as our sources of world
knowledge. One is Freebase, which is collaboratively collected knowledge about
entities and their organizations. The other is YAGO2, a knowledge base
automatically extracted from Wikipedia and maps knowledge to the linguistic
knowledge base, WordNet. Experimental results on two text benchmark datasets
(20newsgroups and RCV1) show that incorporating world knowledge as indirect
supervision can significantly outperform the state-of-the-art clustering
algorithms as well as clustering algorithms enhanced with world knowledge
features.
Online Learning of Event Definitions
Authors: Nikos Katzouris, Alexander Artikis, Georgios Paliouras
Date: 2016-07-30 00:00:00
Tasks: Activity Recognition, Inductive logic programming

Systems for symbolic event recognition infer occurrences of events in time
using a set of event definitions in the form of first-order rules. The Event
Calculus is a temporal logic that has been used as a basis in event recognition
applications, providing among others, direct connections to machine learning,
via Inductive Logic Programming (ILP). We present an ILP system for online
learning of Event Calculus theories. To allow for a single-pass learning
strategy, we use the Hoeffding bound for evaluating clauses on a subset of the
input stream. We employ a decoupling scheme of the Event Calculus axioms during
the learning process, that allows to learn each clause in isolation. Moreover,
we use abductive-inductive logic programming techniques to handle unobserved
target predicates. We evaluate our approach on an activity recognition
application and compare it to a number of batch learning techniques. We obtain
results of comparable predicative accuracy with significant speed-ups in
training time. We also outperform hand-crafted rules and match the performance
of a sound incremental learner that can only operate on noise-free datasets.
This paper is under consideration for acceptance in TPLP.
Updating Formulas and Algorithms for Computing Entropy and Gini Index from Time-Changing Data Streams
Authors: Blaz Sovdat
Date: 2014-03-25 00:00:00
Tasks: Attribute

Despite growing interest in data stream mining the most successful
incremental learners, such as VFDT, still use periodic recomputation to update
attribute information gains and Gini indices. This note provides simple
incremental formulas and algorithms for computing entropy and Gini index from
time-changing data streams.
DeepSoft: A vision for a deep model of software
Authors: Hoa Khanh Dam, Truyen Tran, John Grundy, Aditya Ghose
Date: 2016-07-30 00:00:00
Tasks: Feature Engineering

Although software analytics has experienced rapid growth as a research area,
it has not yet reached its full potential for wide industrial adoption. Most of
the existing work in software analytics still relies heavily on costly manual
feature engineering processes, and they mainly address the traditional
classification problems, as opposed to predicting future events. We present a
vision for \emph{DeepSoft}, an \emph{end-to-end} generic framework for modeling
software and its development process to predict future risks and recommend
interventions. DeepSoft, partly inspired by human memory, is built upon the
powerful deep learning-based Long Short Term Memory architecture that is
capable of learning long-term temporal dependencies that occur in software
evolution. Such deep learned patterns of software can be used to address a
range of challenging problems such as code and task recommendation and
prediction. DeepSoft provides a new approach for research into modeling of
source code, risk prediction and mitigation, developer modeling, and
automatically generating code patches from bug reports.
VolumeDeform: Real-time Volumetric Non-rigid Reconstruction
Authors: Matthias Innmann, Michael Zollhöfer, Matthias Nießner, Christian Theobalt, Marc Stamminger
Date: 2016-03-27 00:00:00

We present a novel approach for the reconstruction of dynamic geometric
shapes using a single hand-held consumer-grade RGB-D sensor at real-time rates.
Our method does not require a pre-defined shape template to start with and
builds up the scene model from scratch during the scanning process. Geometry
and motion are parameterized in a unified manner by a volumetric representation
that encodes a distance field of the surface geometry as well as the non-rigid
space deformation. Motion tracking is based on a set of extracted sparse color
features in combination with a dense depth-based constraint formulation. This
enables accurate tracking and drastically reduces drift inherent to standard
model-to-depth alignment. We cast finding the optimal deformation of space as a
non-linear regularized variational optimization problem by enforcing local
smoothness and proximity to the input constraints. The problem is tackled in
real-time at the camera's capture rate using a data-parallel flip-flop
optimization strategy. Our results demonstrate robust tracking even for fast
motion and scenes that lack geometric features.
LOH and behold: Web-scale visual search, recommendation and clustering using Locally Optimized Hashing
Authors: Yannis Kalantidis, Lyndon Kennedy, Huy Nguyen, Clayton Mellina, David A. Shamma
Date: 2016-04-21 00:00:00
Tasks: Clustering, Distributed Computing, Quantization

We propose a novel hashing-based matching scheme, called Locally Optimized
Hashing (LOH), based on a state-of-the-art quantization algorithm that can be
used for efficient, large-scale search, recommendation, clustering, and
deduplication. We show that matching with LOH only requires set intersections
and summations to compute and so is easily implemented in generic distributed
computing systems. We further show application of LOH to: a) large-scale search
tasks where performance is on par with other state-of-the-art hashing
approaches; b) large-scale recommendation where queries consisting of thousands
of images can be used to generate accurate recommendations from collections of
hundreds of millions of images; and c) efficient clustering with a graph-based
algorithm that can be scaled to massive collections in a distributed
environment or can be used for deduplication for small collections, like search
results, performing better than traditional hashing approaches while only
requiring a few milliseconds to run. In this paper we experiment on datasets of
up to 100 million images, but in practice our system can scale to larger
collections and can be used for other types of data that have a vector
representation in a Euclidean space.
Cross-dimensional Weighting for Aggregated Deep Convolutional Features
Authors: Yannis Kalantidis, Clayton Mellina, Simon Osindero
Date: 2015-12-13 00:00:00
Tasks: Image Retrieval

We propose a simple and straightforward way of creating powerful image
representations via cross-dimensional weighting and aggregation of deep
convolutional neural network layer outputs. We first present a generalized
framework that encompasses a broad family of approaches and includes
cross-dimensional pooling and weighting steps. We then propose specific
non-parametric schemes for both spatial- and channel-wise weighting that boost
the effect of highly active spatial responses and at the same time regulate
burstiness effects. We experiment on different public datasets for image search
and show that our approach outperforms the current state-of-the-art for
approaches based on pre-trained networks. We also provide an easy-to-use, open
source implementation that reproduces our results.
gLOP: the global and Local Penalty for Capturing Predictive Heterogeneity
Authors: Rhiannon V. Rose, Daniel J. Lizotte
Date: 2016-07-29 00:00:00

When faced with a supervised learning problem, we hope to have rich enough
data to build a model that predicts future instances well. However, in
practice, problems can exhibit predictive heterogeneity: most instances might
be relatively easy to predict, while others might be predictive outliers for
which a model trained on the entire dataset does not perform well. Identifying
these can help focus future data collection. We present gLOP, the global and
Local Penalty, a framework for capturing predictive heterogeneity and
identifying predictive outliers. gLOP is based on penalized regression for
multitask learning, which improves learning by leveraging training signal
information from related tasks. We give two optimization algorithms for gLOP,
one space-efficient, and another giving the full regularization path. We also
characterize uniqueness in terms of the data and tuning parameters, and present
empirical results on synthetic data and on two health research problems.