pwc-archive/papers-with-abstracts huggingface dataset:
Hand Gesture Recognition with Leap Motion
Authors: Youchen Du, Shenglan Liu, Lin Feng, Menghui Chen, Jie Wu
Date: 2017-11-12 00:00:00
Tasks: Dimensionality Reduction, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

The recent introduction of depth cameras like Leap Motion Controller allows
researchers to exploit the depth information to recognize hand gesture more
robustly. This paper proposes a novel hand gesture recognition system with Leap
Motion Controller. A series of features are extracted from Leap Motion tracking
data, we feed these features along with HOG feature extracted from sensor
images into a multi-class SVM classifier to recognize performed gesture,
dimension reduction and feature weighted fusion are also discussed. Our results
show that our model is much more accurate than previous work.
Foundations of Declarative Data Analysis Using Limit Datalog Programs
Authors: Mark Kaminski, Bernardo Cuenca Grau, Egor V. Kostylev, Boris Motik, Ian Horrocks
Date: 2017-05-19 00:00:00

Motivated by applications in declarative data analysis, we study
$\mathit{Datalog}_{\mathbb{Z}}$---an extension of positive Datalog with
arithmetic functions over integers. This language is known to be undecidable,
so we propose two fragments. In $\mathit{limit}~\mathit{Datalog}_{\mathbb{Z}}$
predicates are axiomatised to keep minimal/maximal numeric values, allowing us
to show that fact entailment is coNExpTime-complete in combined, and
coNP-complete in data complexity. Moreover, an additional $\mathit{stability}$
requirement causes the complexity to drop to ExpTime and PTime, respectively.
Finally, we show that stable $\mathit{Datalog}_{\mathbb{Z}}$ can express many
useful data analysis tasks, and so our results provide a sound foundation for
the development of advanced information systems.
Streaming Sparse Gaussian Process Approximations
Authors: Thang D. Bui, Cuong V. Nguyen, Richard E. Turner
Date: 2017-05-19 00:00:00
Proceeding: NeurIPS 2017 12

Sparse pseudo-point approximations for Gaussian process (GP) models provide a
suite of methods that support deployment of GPs in the large data regime and
enable analytic intractabilities to be sidestepped. However, the field lacks a
principled method to handle streaming data in which both the posterior
distribution over function values and the hyperparameter estimates are updated
in an online fashion. The small number of existing approaches either use
suboptimal hand-crafted heuristics for hyperparameter learning, or suffer from
catastrophic forgetting or slow updating when new data arrive. This paper
develops a new principled framework for deploying Gaussian process
probabilistic models in the streaming setting, providing methods for learning
hyperparameters and optimising pseudo-input locations. The proposed framework
is assessed using synthetic and real-world datasets.
Classification with many classes: challenges and pluses
Authors: Felix Abramovich, Marianna Pensky
Date: 2015-06-04 00:00:00
Tasks: Classification, feature selection, General Classification, Multi-class Classification

The objective of the paper is to study accuracy of multi-class classification in high-dimensional setting, where the number of classes is also large ("large $L$, large $p$, small $n$" model). While this problem arises in many practical applications and many techniques have been recently developed for its solution, to the best of our knowledge nobody provided a rigorous theoretical analysis of this important setup. The purpose of the present paper is to fill in this gap. We consider one of the most common settings, classification of high-dimensional normal vectors where, unlike standard assumptions, the number of classes could be large. We derive non-asymptotic conditions on effects of significant features, and the low and the upper bounds for distances between classes required for successful feature selection and classification with a given accuracy. Furthermore, we study an asymptotic setup where the number of classes is diverging with the dimension of feature space and while the number of samples per class is possibly limited. We point out on an interesting and, at first glance, somewhat counter-intuitive phenomenon that a large number of classes may be a "blessing" rather than a "curse" since, in certain settings, the precision of classification can improve as the number of classes grows. This is due to more accurate feature selection since even weaker significant features, which are not sufficiently strong to be manifested in a coarse classification, being shared across the classes, have a stronger impact as the number of classes increases. We supplement our theoretical investigation by a simulation study and a real data example where we again observe the above phenomenon.
Online Learning for Structured Loss Spaces
Authors: Siddharth Barman, Aditya Gopalan, Aadirupa Saha
Date: 2017-06-13 00:00:00

We consider prediction with expert advice when the loss vectors are assumed
to lie in a set described by the sum of atomic norm balls. We derive a regret
bound for a general version of the online mirror descent (OMD) algorithm that
uses a combination of regularizers, each adapted to the constituent atomic
norms. The general result recovers standard OMD regret bounds, and yields
regret bounds for new structured settings where the loss vectors are (i) noisy
versions of points from a low-rank subspace, (ii) sparse vectors corrupted with
noise, and (iii) sparse perturbations of low-rank vectors. For the problem of
online learning with structured losses, we also show lower bounds on regret in
terms of rank and sparsity of the source set of the loss vectors, which implies
lower bounds for the above additive loss settings as well.
Multi-kernel learning of deep convolutional features for action recognition
Authors: Biswa Sengupta, Yu Qian
Date: 2017-07-21 00:00:00
Tasks: Action Recognition, Activity Recognition, Temporal Action Localization, Video Understanding

Image understanding using deep convolutional network has reached human-level
performance, yet a closely related problem of video understanding especially,
action recognition has not reached the requisite level of maturity. We combine
multi-kernels based support-vector-machines (SVM) with a multi-stream deep
convolutional neural network to achieve close to state-of-the-art performance
on a 51-class activity recognition problem (HMDB-51 dataset); this specific
dataset has proved to be particularly challenging for deep neural networks due
to the heterogeneity in camera viewpoints, video quality, etc. The resulting
architecture is named pillar networks as each (very) deep neural network acts
as a pillar for the hierarchical classifiers. In addition, we illustrate that
hand-crafted features such as improved dense trajectories (iDT) and Multi-skip
Feature Stacking (MIFS), as additional pillars, can further supplement the
performance.
Algorithmic clothing: hybrid recommendation, from street-style-to-shop
Authors: Y Qian, P Giaccone, M Sasdelli, E Vasquez, B Sengupta
Date: 2017-05-26 00:00:00
Tasks: General Classification, Recommendation Systems, Segmentation

In this paper we detail Cortexica's (https://www.cortexica.com)
recommendation framework -- particularly, we describe how a hybrid visual
recommender system can be created by combining conditional random fields for
segmentation and deep neural networks for object localisation and feature
representation. The recommendation system that is built after localisation,
segmentation and classification has two properties -- first, it is knowledge
based in the sense that it learns pairwise preference/occurrence matrix by
utilising knowledge from experts (images from fashion blogs) and second, it is
content-based as it utilises a deep learning based framework for learning
feature representation. Such a construct is especially useful when there is a
scarcity of user preference data, that forms the foundation of many
collaborative recommendation algorithms.
Evaluation of trackers for Pan-Tilt-Zoom Scenarios
Authors: Yucao Tang, Guillaume-Alexandre Bilodeau
Date: 2017-11-12 00:00:00
Tasks: Position

Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in
computer vision for many years. Compared to tracking with a still camera, the
images captured with a PTZ camera are highly dynamic in nature because the
camera can perform large motion resulting in quickly changing capture
conditions. Furthermore, tracking with a PTZ camera involves camera control to
position the camera on the target. For successful tracking and camera control,
the tracker must be fast enough, or has to be able to predict accurately the
next position of the target. Therefore, standard benchmarks do not allow to
assess properly the quality of a tracker for the PTZ scenario. In this work, we
use a virtual PTZ framework to evaluate different tracking algorithms and
compare their performances. We also extend the framework to add target position
prediction for the next frame, accounting for camera motion and processing
delays. By doing this, we can assess if predicting can make long-term tracking
more robust as it may help slower algorithms for keeping the target in the
field of view of the camera. Results confirm that both speed and robustness are
required for tracking under the PTZ scenario.
On the Synthesis of Guaranteed-Quality Plans for Robot Fleets in Logistics Scenarios via Optimization Modulo Theories
Authors: Francesco Leofante, Erika Ábrahám, Tim Niemueller, Gerhard Lakemeyer, Armando Tacchella
Date: 2017-11-12 00:00:00
Tasks: Management, Scheduling

In manufacturing, the increasing involvement of autonomous robots in
production processes poses new challenges on the production management. In this
paper we report on the usage of Optimization Modulo Theories (OMT) to solve
certain multi-robot scheduling problems in this area. Whereas currently
existing methods are heuristic, our approach guarantees optimality for the
computed solution. We do not only present our final method but also its
chronological development, and draw some general observations for the
development of OMT-based approaches.
Bayesian Belief Updating of Spatiotemporal Seizure Dynamics
Authors: Gerald K Cooray, Richard Rosch, Torsten Baldeweg, Louis Lemieux, Karl Friston, Biswa Sengupta
Date: 2017-05-20 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Epileptic seizure activity shows complicated dynamics in both space and time.
To understand the evolution and propagation of seizures spatially extended sets
of data need to be analysed. We have previously described an efficient
filtering scheme using variational Laplace that can be used in the Dynamic
Causal Modelling (DCM) framework [Friston, 2003] to estimate the temporal
dynamics of seizures recorded using either invasive or non-invasive electrical
recordings (EEG/ECoG). Spatiotemporal dynamics are modelled using a partial
differential equation -- in contrast to the ordinary differential equation used
in our previous work on temporal estimation of seizure dynamics [Cooray, 2016].
We provide the requisite theoretical background for the method and test the
ensuing scheme on simulated seizure activity data and empirical invasive ECoG
data. The method provides a framework to assimilate the spatial and temporal
dynamics of seizure activity, an aspect of great physiological and clinical
importance.
Unified Spectral Clustering with Optimal Graph
Authors: Zhao Kang, Chong Peng, Qiang Cheng, Zenglin Xu
Date: 2017-11-12 00:00:00
Tasks: Clustering, graph construction

Spectral clustering has found extensive use in many areas. Most traditional
spectral clustering algorithms work in three separate steps: similarity graph
construction; continuous labels learning; discretizing the learned labels by
k-means clustering. Such common practice has two potential flaws, which may
lead to severe information loss and performance degradation. First, predefined
similarity graph might not be optimal for subsequent clustering. It is
well-accepted that similarity graph highly affects the clustering results. To
this end, we propose to automatically learn similarity information from data
and simultaneously consider the constraint that the similarity matrix has exact
c connected components if there are c clusters. Second, the discrete solution
may deviate from the spectral solution since k-means method is well-known as
sensitive to the initialization of cluster centers. In this work, we transform
the candidate solution into a new one that better approximates the discrete
one. Finally, those three subtasks are integrated into a unified framework,
with each subtask iteratively boosted by using the results of the others
towards an overall optimal solution. It is known that the performance of a
kernel method is largely determined by the choice of kernels. To tackle this
practical problem of how to select the most suitable kernel for a particular
data set, we further extend our model to incorporate multiple kernel learning
ability. Extensive experiments demonstrate the superiority of our proposed
method as compared to existing clustering approaches.
Approximate Bayesian inference as a gauge theory
Authors: Biswa Sengupta, Karl Friston
Date: 2017-05-17 00:00:00
Tasks: Bayesian Inference, Variational Inference

In a published paper [Sengupta, 2016], we have proposed that the brain (and
other self-organized biological and artificial systems) can be characterized
via the mathematical apparatus of a gauge theory. The picture that emerges from
this approach suggests that any biological system (from a neuron to an
organism) can be cast as resolving uncertainty about its external milieu,
either by changing its internal states or its relationship to the environment.
Using formal arguments, we have shown that a gauge theory for neuronal dynamics
-- based on approximate Bayesian inference -- has the potential to shed new
light on phenomena that have thus far eluded a formal description, such as
attention and the link between action and perception. Here, we describe the
technical apparatus that enables such a variational inference on manifolds.
Particularly, the novel contribution of this paper is an algorithm that utlizes
a Schild's ladder for parallel transport of sufficient statistics (means,
covariances, etc.) on a statistical manifold.
Deep Tensor Encoding
Authors: B Sengupta, E Vasquez, Y Qian
Date: 2017-03-18 00:00:00
Tasks: Information Retrieval, Retrieval

Learning an encoding of feature vectors in terms of an over-complete
dictionary or a information geometric (Fisher vectors) construct is wide-spread
in statistical signal processing and computer vision. In content based
information retrieval using deep-learning classifiers, such encodings are
learnt on the flattened last layer, without adherence to the multi-linear
structure of the underlying feature tensor. We illustrate a variety of feature
encodings incl. sparse dictionary coding and Fisher vectors along with
proposing that a structured tensor factorization scheme enables us to perform
retrieval that can be at par, in terms of average precision, with Fisher vector
encoded image signatures. In short, we illustrate how structural constraints
increase retrieval fidelity.
Differential Geometric Retrieval of Deep Features
Authors: Y Qian, E Vazquez, B Sengupta
Date: 2017-02-21 00:00:00
Tasks: Content-Based Image Retrieval, GPU, Image Retrieval, Retrieval

Comparing images to recommend items from an image-inventory is a subject of
continued interest. Added with the scalability of deep-learning architectures
the once `manual' job of hand-crafting features have been largely alleviated,
and images can be compared according to features generated from a deep
convolutional neural network. In this paper, we compare distance metrics (and
divergences) to rank features generated from a neural network, for
content-based image retrieval. Specifically, after modelling individual images
using approximations of mixture models or sparse covariance estimators, we
resort to their information-theoretic and Riemann geometric comparisons. We
show that using approximations of mixture models enable us to compute a
distance measure based on the Wasserstein metric that requires less effort than
other computationally intensive optimal transport plans; finally, an affine
invariant metric is used to compare the optimal transport metric to its Riemann
geometric counterpart -- we conclude that although expensive, retrieval metric
based on Wasserstein geometry is more suitable than information theoretic
comparison of images. In short, we combine GPU scalability in learning deep
feature vectors with statistically efficient metrics that we foresee being
utilised in a commercial setting.
Feature Enhancement Network: A Refined Scene Text Detector
Authors: Sheng Zhang, Yuliang Liu, Lianwen Jin, Canjie Luo
Date: 2017-11-12 00:00:00
Tasks: object-detection, Object Detection, Position, Region Proposal, Text Detection

In this paper, we propose a refined scene text detector with a \textit{novel}
Feature Enhancement Network (FEN) for Region Proposal and Text Detection
Refinement. Retrospectively, both region proposal with \textit{only} $3\times
3$ sliding-window feature and text detection refinement with \textit{single
scale} high level feature are insufficient, especially for smaller scene text.
Therefore, we design a new FEN network with \textit{task-specific},
\textit{low} and \textit{high} level semantic features fusion to improve the
performance of text detection. Besides, since \textit{unitary}
position-sensitive RoI pooling in general object detection is unreasonable for
variable text regions, an \textit{adaptively weighted} position-sensitive RoI
pooling layer is devised for further enhancing the detecting accuracy. To
tackle the \textit{sample-imbalance} problem during the refinement stage, we
also propose an effective \textit{positives mining} strategy for efficiently
training our network. Experiments on ICDAR 2011 and 2013 robust text detection
benchmarks demonstrate that our method can achieve state-of-the-art results,
outperforming all reported methods in terms of F-measure.
Linking Sequences of Events with Sparse or No Common Occurrence across Data Sets
Authors: Yunsung Kim
Date: 2017-11-12 00:00:00

Data of practical interest - such as personal records, transaction logs, and
medical histories - are sequential collections of events relevant to a
particular source entity. Recent studies have attempted to link sequences that
represent a common entity across data sets to allow more comprehensive
statistical analyses and to identify potential privacy failures. Yet, current
approaches remain tailored to their specific domains of application, and they
fail when co-referent sequences in different data sets contain sparse or no
common events, which occurs frequently in many cases.
  To address this, we formalize the general problem of "sequence linkage" and
describe "LDA-Link," a generic solution that is applicable even when
co-referent event sequences contain no common items at all. LDA-Link is built
upon "Split-Document" model, a new mixed-membership probabilistic model for the
generation of event sequence collections. It detects the latent similarity of
sequences and thus achieves robustness particularly when co-referent sequences
share sparse or no event overlap. We apply LDA-Link in the context of social
media profile reconciliation where users make no common posts across platforms,
comparing to the state-of-the-art generic solution to sequence linkage.
Robust registration of medical images in the presence of spatially-varying noise
Authors: Reza Abbasi-Asl, Aboozar Ghaffari, Emad Fatemizadeh
Date: 2017-11-12 00:00:00
Tasks: Image Registration

Spatially-varying intensity noise is a common source of distortion in medical images. Bias field noise is one example of such a distortion that is often present in the magnetic resonance (MR) images or other modalities such as retina images. In this paper, we first show that the bias field noise can be considerably reduced using Empirical Mode Decomposition (EMD) technique. EMD is a multi-resolution tool that decomposes a signal into several principle patterns and residual components. We show that the spatially-varying noise is highly expressed in the residual component of the EMD and could be filtered out. Then, we propose two hierarchical multi-resolution EMD-based algorithms for robust registration of images in the presence of spatially varying noise. One algorithm (LR-EMD) is based on registration of EMD feature-maps from both floating and reference images in various resolution levels. In the second algorithm (AFR-EMD), we first extract an average feature-map based on EMD from both floating and reference images. Then, we use a simple hierarchical multi-resolution algorithm to register the average feature-maps. For the brain MR images, both algorithms achieve lower error rate and higher convergence percentage compared to the intensity-based hierarchical registration. Specifically, using mutual information as the similarity measure, AFR-EMD achieves 42% lower error rate in intensity and 52% lower error rate in transformation compared to intensity-based hierarchical registration. For LR-EMD, the error rate is 32% lower for the intensity and 41% lower for the transformation. Furthermore, we demonstrate that our proposed algorithms improve the registration of retina images in the presence of spatially varying noise.
Syntax-Directed Attention for Neural Machine Translation
Authors: Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao
Date: 2017-11-12 00:00:00
Tasks: Machine Translation, NMT, Translation

Attention mechanism, including global attention and local attention, plays a key role in neural machine translation (NMT). Global attention attends to all source words for word prediction. In comparison, local attention selectively looks at fixed-window source words. However, alignment weights for the current target word often decrease to the left and right by linear distance centering on the aligned source position and neglect syntax-directed distance constraints. In this paper, we extend local attention with syntax-distance constraint, to focus on syntactically related source words with the predicted target word, thus learning a more effective context vector for word prediction. Moreover, we further propose a double context NMT architecture, which consists of a global context vector and a syntax-directed context vector over the global attention, to provide more translation performance for NMT from source representation. The experiments on the large-scale Chinese-to-English and English-to-Germen translation tasks show that the proposed approach achieves a substantial and significant improvement over the baseline system.
SimDoc: Topic Sequence Alignment based Document Similarity Framework
Authors: Gaurav Maheshwari, Priyansh Trivedi, Harshita Sahijwani, Kunal Jha, Sourish Dasgupta, Jens Lehmann
Date: 2016-11-15 00:00:00
Tasks: Clustering, Question Answering, Semantic Similarity, Semantic Textual Similarity

Document similarity is the problem of estimating the degree to which a given
pair of documents has similar semantic content. An accurate document similarity
measure can improve several enterprise relevant tasks such as document
clustering, text mining, and question-answering. In this paper, we show that a
document's thematic flow, which is often disregarded by bag-of-word techniques,
is pivotal in estimating their similarity. To this end, we propose a novel
semantic document similarity framework, called SimDoc. We model documents as
topic-sequences, where topics represent latent generative clusters of related
words. Then, we use a sequence alignment algorithm to estimate their semantic
similarity. We further conceptualize a novel mechanism to compute topic-topic
similarity to fine tune our system. In our experiments, we show that SimDoc
outperforms many contemporary bag-of-words techniques in accurately computing
document similarity, and on practical applications such as document clustering.