pwc-archive/papers-with-abstracts huggingface dataset:
Semantic homophily in online communication: evidence from Twitter
Authors: Sanja Šćepanović, Igor Mishkovski, Bruno Gonçalves, Nguyen Trung Hieu, Pan Hui
Date: 2016-06-27 00:00:00
Tasks: Sociology

People are observed to assortatively connect on a set of traits. This
phenomenon, termed assortative mixing or sometimes homophily, can be quantified
through assortativity coefficient in social networks. Uncovering the exact
causes of strong assortative mixing found in social networks has been a
research challenge. Among the main suggested causes from sociology are the
tendency of similar individuals to connect (often itself referred as homophily)
and the social influence among already connected individuals. An important
question to researchers and in practice can be tackled, as we present here:
understanding the exact mechanisms of interplay between these tendencies and
the underlying social network structure. Namely, in addition to the mentioned
assortativity coefficient, there are several other static and temporal network
properties and substructures that can be linked to the tendencies of homophily
and social influence in the social network and we herein investigate those.
Concretely, we tackle a computer-mediated \textit{communication network} (based
on Twitter mentions) and a particular type of assortative mixing that can be
inferred from the semantic features of communication content that we term
\textit{semantic homophily}. Our work, to the best of our knowledge, is the
first to offer an in-depth analysis on semantic homophily in a communication
network and the interplay between them. We quantify diverse levels of semantic
homophily, identify the semantic aspects that are the drivers of observed
homophily, show insights in its temporal evolution and finally, we present its
intricate interplay with the communication network on Twitter. By analyzing
these mechanisms we increase understanding on what are the semantic aspects
that shape and how they shape the human computer-mediated communication.
Second-order Convolutional Neural Networks
Authors: Kaicheng Yu, Mathieu Salzmann
Date: 2017-03-20 00:00:00
Tasks: image-classification, Image Classification

Convolutional Neural Networks (CNNs) have been successfully applied to many
computer vision tasks, such as image classification. By performing linear
combinations and element-wise nonlinear operations, these networks can be
thought of as extracting solely first-order information from an input image. In
the past, however, second-order statistics computed from handcrafted features,
e.g., covariances, have proven highly effective in diverse recognition tasks.
In this paper, we introduce a novel class of CNNs that exploit second-order
statistics. To this end, we design a series of new layers that (i) extract a
covariance matrix from convolutional activations, (ii) compute a parametric,
second-order transformation of a matrix, and (iii) perform a parametric
vectorization of a matrix. These operations can be assembled to form a
Covariance Descriptor Unit (CDU), which replaces the fully-connected layers of
standard CNNs. Our experiments demonstrate the benefits of our new
architecture, which outperform the first-order CNNs, while relying on up to 90%
fewer parameters.
Convolutional Spike Timing Dependent Plasticity based Feature Learning in Spiking Neural Networks
Authors: Priyadarshini Panda, Gopalakrishnan Srinivasan, Kaushik Roy
Date: 2017-03-10 00:00:00
Tasks: Object Recognition

Brain-inspired learning models attempt to mimic the cortical architecture and
computations performed in the neurons and synapses constituting the human brain
to achieve its efficiency in cognitive tasks. In this work, we present
convolutional spike timing dependent plasticity based feature learning with
biologically plausible leaky-integrate-and-fire neurons in Spiking Neural
Networks (SNNs). We use shared weight kernels that are trained to encode
representative features underlying the input patterns thereby improving the
sparsity as well as the robustness of the learning model. We demonstrate that
the proposed unsupervised learning methodology learns several visual categories
for object recognition with fewer number of examples and outperforms
traditional fully-connected SNN architectures while yielding competitive
accuracy. Additionally, we observe that the learning model performs out-of-set
generalization further making the proposed biologically plausible framework a
viable and efficient architecture for future neuromorphic applications.
On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms
Authors: Anthony Bagnall, Gavin C. Cawley
Date: 2017-03-20 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Model Selection

We demonstrate that, for a range of state-of-the-art machine learning
algorithms, the differences in generalisation performance obtained using
default parameter settings and using parameters tuned via cross-validation can
be similar in magnitude to the differences in performance observed between
state-of-the-art and uncompetitive learning systems. This means that fair and
rigorous evaluation of new learning algorithms requires performance comparison
against benchmark methods with best-practice model selection procedures, rather
than using default parameter settings. We investigate the sensitivity of three
key machine learning algorithms (support vector machine, random forest and
rotation forest) to their default parameter settings, and provide guidance on
determining sensible default parameter values for implementations of these
algorithms. We also conduct an experimental comparison of these three
algorithms on 121 classification problems and find that, perhaps surprisingly,
rotation forest is significantly more accurate on average than both random
forest and a support vector machine.
Phase Unmixing : Multichannel Source Separation with Magnitude Constraints
Authors: Antoine Deleforge, Yann Traonmilin
Date: 2016-09-30 00:00:00

We consider the problem of estimating the phases of K mixed complex signals
from a multichannel observation, when the mixing matrix and signal magnitudes
are known. This problem can be cast as a non-convex quadratically constrained
quadratic program which is known to be NP-hard in general. We propose three
approaches to tackle it: a heuristic method, an alternate minimization method,
and a convex relaxation into a semi-definite program. The last two approaches
are showed to outperform the oracle multichannel Wiener filter in
under-determined informed source separation tasks, using simulated and speech
signals. The convex relaxation approach yields best results, including the
potential for exact source separation in under-determined settings.
On the effect of pooling on the geometry of representations
Authors: Gary Bécigneul
Date: 2017-03-20 00:00:00

In machine learning and neuroscience, certain computational structures and
algorithms are known to yield disentangled representations without us
understanding why, the most striking examples being perhaps convolutional
neural networks and the ventral stream of the visual cortex in humans and
primates. As for the latter, it was conjectured that representations may be
disentangled by being flattened progressively and at a local scale. An attempt
at a formalization of the role of invariance in learning representations was
made recently, being referred to as I-theory. In this framework and using the
language of differential geometry, we show that pooling over a group of
transformations of the input contracts the metric and reduces its curvature,
and provide quantitative bounds, in the aim of moving towards a theoretical
understanding on how to disentangle representations.
Independence clustering (without a matrix)
Authors: Daniil Ryabko
Date: 2017-03-20 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Clustering, Time Series, Time Series Analysis

The independence clustering problem is considered in the following
formulation: given a set $S$ of random variables, it is required to find the
finest partitioning $\{U_1,\dots,U_k\}$ of $S$ into clusters such that the
clusters $U_1,\dots,U_k$ are mutually independent. Since mutual independence is
the target, pairwise similarity measurements are of no use, and thus
traditional clustering algorithms are inapplicable. The distribution of the
random variables in $S$ is, in general, unknown, but a sample is available.
Thus, the problem is cast in terms of time series. Two forms of sampling are
considered: i.i.d.\ and stationary time series, with the main emphasis being on
the latter, more general, case. A consistent, computationally tractable
algorithm for each of the settings is proposed, and a number of open directions
for further research are outlined.
A Systematic Study of Online Class Imbalance Learning with Concept Drift
Authors: Shuo Wang, Leandro L. Minku, Xin Yao
Date: 2017-03-20 00:00:00

As an emerging research topic, online class imbalance learning often combines
the challenges of both class imbalance and concept drift. It deals with data
streams having very skewed class distributions, where concept drift may occur.
It has recently received increased research attention; however, very little
work addresses the combined problem where both class imbalance and concept
drift coexist. As the first systematic study of handling concept drift in
class-imbalanced data streams, this paper first provides a comprehensive review
of current research progress in this field, including current research focuses
and open challenges. Then, an in-depth experimental study is performed, with
the goal of understanding how to best overcome concept drift in online learning
with class imbalance. Based on the analysis, a general guideline is proposed
for the development of an effective algorithm.
Empirical Analysis of the Necessary and Sufficient Conditions of the Echo State Property
Authors: Sebastián Basterrech
Date: 2017-03-20 00:00:00

The Echo State Network (ESN) is a specific recurrent network, which has
gained popularity during the last years. The model has a recurrent network
named reservoir, that is fixed during the learning process. The reservoir is
used for transforming the input space in a larger space. A fundamental property
that provokes an impact on the model accuracy is the Echo State Property (ESP).
There are two main theoretical results related to the ESP. First, a sufficient
condition for the ESP existence that involves the singular values of the
reservoir matrix. Second, a necessary condition for the ESP. The ESP can be
violated according to the spectral radius value of the reservoir matrix. There
is a theoretical gap between these necessary and sufficient conditions. This
article presents an empirical analysis of the accuracy and the projections of
reservoirs that satisfy this theoretical gap. It gives some insights about the
generation of the reservoir matrix. From previous works, it is already known
that the optimal accuracy is obtained near to the border of stability control
of the dynamics. Then, according to our empirical results, we can see that this
border seems to be closer to the sufficient conditions than to the necessary
conditions of the ESP.
Automatic Text Summarization Approaches to Speed up Topic Model Learning Process
Authors: Mohamed Morchid, Juan-Manuel Torres-Moreno, Richard Dufour, Javier Ramírez-Rodríguez, Georges Linarès
Date: 2017-03-20 00:00:00
Tasks: Articles, CPU, Information Retrieval, Retrieval, Text Summarization

The number of documents available into Internet moves each day up. For this
reason, processing this amount of information effectively and expressibly
becomes a major concern for companies and scientists. Methods that represent a
textual document by a topic representation are widely used in Information
Retrieval (IR) to process big data such as Wikipedia articles. One of the main
difficulty in using topic model on huge data collection is related to the
material resources (CPU time and memory) required for model estimate. To deal
with this issue, we propose to build topic spaces from summarized documents. In
this paper, we present a study of topic space representation in the context of
big data. The topic space representation behavior is analyzed on different
languages. Experiments show that topic spaces estimated from text summaries are
as relevant as those estimated from the complete documents. The real advantage
of such an approach is the processing time gain: we showed that the processing
time can be drastically reduced using summarized documents (more than 60\% in
general). This study finally points out the differences between thematic
representations of documents depending on the targeted languages such as
English or latin languages.
Twitter100k: A Real-world Dataset for Weakly Supervised Cross-Media Retrieval
Authors: Yuting Hu, Liang Zheng, Yi Yang, Yongfeng Huang
Date: 2017-03-20 00:00:00
Tasks: Optical Character Recognition (OCR), Retrieval, Weakly-supervised Learning

This paper contributes a new large-scale dataset for weakly supervised
cross-media retrieval, named Twitter100k. Current datasets, such as Wikipedia,
NUS Wide and Flickr30k, have two major limitations. First, these datasets are
lacking in content diversity, i.e., only some pre-defined classes are covered.
Second, texts in these datasets are written in well-organized language, leading
to inconsistency with realistic applications. To overcome these drawbacks, the
proposed Twitter100k dataset is characterized by two aspects: 1) it has 100,000
image-text pairs randomly crawled from Twitter and thus has no constraint in
the image categories; 2) text in Twitter100k is written in informal language by
the users.
  Since strongly supervised methods leverage the class labels that may be
missing in practice, this paper focuses on weakly supervised learning for
cross-media retrieval, in which only text-image pairs are exploited during
training. We extensively benchmark the performance of four subspace learning
methods and three variants of the Correspondence AutoEncoder, along with
various text features on Wikipedia, Flickr30k and Twitter100k. Novel insights
are provided. As a minor contribution, inspired by the characteristic of
Twitter100k, we propose an OCR-based cross-media retrieval method. In
experiment, we show that the proposed OCR-based method improves the baseline
performance.
Artificial Intelligence and Economic Theories
Authors: Tshilidzi Marwala, Evan Hurwitz
Date: 2017-03-20 00:00:00
Tasks: counterfactual

The advent of artificial intelligence has changed many disciplines such as
engineering, social science and economics. Artificial intelligence is a
computational technique which is inspired by natural intelligence such as the
swarming of birds, the working of the brain and the pathfinding of the ants.
These techniques have impact on economic theories. This book studies the impact
of artificial intelligence on economic theories, a subject that has not been
extensively studied. The theories that are considered are: demand and supply,
asymmetrical information, pricing, rational choice, rational expectation, game
theory, efficient market hypotheses, mechanism design, prospect, bounded
rationality, portfolio theory, rational counterfactual and causality. The
benefit of this book is that it evaluates existing theories of economics and
update them based on the developments in artificial intelligence field.
TopicResponse: A Marriage of Topic Modelling and Rasch Modelling for Automatic Measurement in MOOCs
Authors: Jiazhen He, Benjamin I. P. Rubinstein, James Bailey, Rui Zhang, Sandra Milligan
Date: 2016-07-29 00:00:00

This paper explores the suitability of using automatically discovered topics
from MOOC discussion forums for modelling students' academic abilities. The
Rasch model from psychometrics is a popular generative probabilistic model that
relates latent student skill, latent item difficulty, and observed student-item
responses within a principled, unified framework. According to scholarly
educational theory, discovered topics can be regarded as appropriate
measurement items if (1) students' participation across the discovered topics
is well fit by the Rasch model, and if (2) the topics are interpretable to
subject-matter experts as being educationally meaningful. Such Rasch-scaled
topics, with associated difficulty levels, could be of potential benefit to
curriculum refinement, student assessment and personalised feedback. The
technical challenge that remains, is to discover meaningful topics that
simultaneously achieve good statistical fit with the Rasch model. To address
this challenge, we combine the Rasch model with non-negative matrix
factorisation based topic modelling, jointly fitting both models. We
demonstrate the suitability of our approach with quantitative experiments on
data from three Coursera MOOCs, and with qualitative survey results on topic
interpretability on a Discrete Optimisation MOOC.
Large Margin Object Tracking with Circulant Feature Maps
Authors: Mengmeng Wang, Yong liu, Zeyi Huang
Date: 2017-03-15 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object, Object Tracking

Structured output support vector machine (SVM) based tracking algorithms have
shown favorable performance recently. Nonetheless, the time-consuming candidate
sampling and complex optimization limit their real-time applications. In this
paper, we propose a novel large margin object tracking method which absorbs the
strong discriminative ability from structured output SVM and speeds up by the
correlation filter algorithm significantly. Secondly, a multimodal target
detection technique is proposed to improve the target localization precision
and prevent model drift introduced by similar objects or background noise.
Thirdly, we exploit the feedback from high-confidence tracking results to avoid
the model corruption problem. We implement two versions of the proposed tracker
with the representations from both conventional hand-crafted and deep
convolution neural networks (CNNs) based features to validate the strong
compatibility of the algorithm. The experimental results demonstrate that the
proposed tracker performs superiorly against several state-of-the-art
algorithms on the challenging benchmark sequences while runs at speed in excess
of 80 frames per second. The source code and experimental results will be made
publicly available.
Zero-Shot Recognition using Dual Visual-Semantic Mapping Paths
Authors: Yanan Li, Donghui Wang, Huanhang Hu, Yuetan Lin, Yueting Zhuang
Date: 2017-03-15 00:00:00
Proceeding: CVPR 2017 7
Tasks: Zero-Shot Learning

Zero-shot recognition aims to accurately recognize objects of unseen classes
by using a shared visual-semantic mapping between the image feature space and
the semantic embedding space. This mapping is learned on training data of seen
classes and is expected to have transfer ability to unseen classes. In this
paper, we tackle this problem by exploiting the intrinsic relationship between
the semantic space manifold and the transfer ability of visual-semantic
mapping. We formalize their connection and cast zero-shot recognition as a
joint optimization problem. Motivated by this, we propose a novel framework for
zero-shot recognition, which contains dual visual-semantic mapping paths. Our
analysis shows this framework can not only apply prior semantic knowledge to
infer underlying semantic manifold in the image feature space, but also
generate optimized semantic embedding space, which can enhance the transfer
ability of the visual-semantic mapping to unseen classes. The proposed method
is evaluated for zero-shot recognition on four benchmark datasets, achieving
outstanding results.
Disruptive Event Classification using PMU Data in Distribution Networks
Authors: Iman Niazazari, Hanif Livani
Date: 2017-03-20 00:00:00
Tasks: Classification, Diagnostic, General Classification

Proliferation of advanced metering devices with high sampling rates in
distribution grids, e.g., micro-phasor measurement units ({\mu}PMU), provides
unprecedented potentials for wide-area monitoring and diagnostic applications,
e.g., situational awareness, health monitoring of distribution assets.
Unexpected disruptive events interrupting the normal operation of assets in
distribution grids can eventually lead to permanent failure with expensive
replacement cost over time. Therefore, disruptive event classification provides
useful information for preventive maintenance of the assets in distribution
networks. Preventive maintenance provides wide range of benefits in terms of
time, avoiding unexpected outages, maintenance crew utilization, and equipment
replacement cost. In this paper, a PMU-data-driven framework is proposed for
classification of disruptive events in distribution networks. The two
disruptive events, i.e., malfunctioned capacitor bank switching and
malfunctioned regulator on-load tap changer (OLTC) switching are considered and
distinguished from the normal abrupt load change in distribution grids. The
performance of the proposed framework is verified using the simulation of the
events in the IEEE 13-bus distribution network. The event classification is
formulated using two different algorithms as; i) principle component analysis
(PCA) together with multi-class support vector machine (SVM), and ii)
autoencoder along with softmax classifier. The results demonstrate the
effectiveness of the proposed algorithms and satisfactory classification
accuracies.
Object category understanding via eye fixations on freehand sketches
Authors: Ravi Kiran Sarvadevabhatla, Sudharshan Suresh, R. Venkatesh Babu
Date: 2017-03-20 00:00:00
Tasks: Object

The study of eye gaze fixations on photographic images is an active research
area. In contrast, the image subcategory of freehand sketches has not received
as much attention for such studies. In this paper, we analyze the results of a
free-viewing gaze fixation study conducted on 3904 freehand sketches
distributed across 160 object categories. Our analysis shows that fixation
sequences exhibit marked consistency within a sketch, across sketches of a
category and even across suitably grouped sets of categories. This multi-level
consistency is remarkable given the variability in depiction and extreme image
content sparsity that characterizes hand-drawn object sketches. In our paper,
we show that the multi-level consistency in the fixation data can be exploited
to (a) predict a test sketch's category given only its fixation sequence and
(b) build a computational model which predicts part-labels underlying fixations
on objects. We hope that our findings motivate the community to deem
sketch-like representations worthy of gaze-based studies vis-a-vis photographic
images.
Learning Robust Visual-Semantic Embeddings
Authors: Yao-Hung Hubert Tsai, Liang-Kang Huang, Ruslan Salakhutdinov
Date: 2017-03-17 00:00:00
Proceeding: ICCV 2017 10
Tasks: Generalized Few-Shot Learning, Representation Learning, Retrieval

Many of the existing methods for learning joint embedding of images and text
use only supervised information from paired images and its textual attributes.
Taking advantage of the recent success of unsupervised learning in deep neural
networks, we propose an end-to-end learning framework that is able to extract
more robust multi-modal representations across domains. The proposed method
combines representation learning models (i.e., auto-encoders) together with
cross-domain learning criteria (i.e., Maximum Mean Discrepancy loss) to learn
joint embeddings for semantic and visual features. A novel technique of
unsupervised-data adaptation inference is introduced to construct more
comprehensive embeddings for both labeled and unlabeled data. We evaluate our
method on Animals with Attributes and Caltech-UCSD Birds 200-2011 dataset with
a wide range of applications, including zero and few-shot image recognition and
retrieval, from inductive to transductive settings. Empirically, we show that
our framework improves over the current state of the art on many of the
considered tasks.