pwc-archive/papers-with-abstracts huggingface dataset:
Real-Time Community Detection in Large Social Networks on a Laptop
Authors: Benjamin Paul Chamberlain, Josh Levy-Kramer, Clive Humby, Marc Peter Deisenroth
Date: 2016-01-15 00:00:00
Tasks: Community Detection, Distributed Computing

For a broad range of research, governmental and commercial applications it is
important to understand the allegiances, communities and structure of key
players in society. One promising direction towards extracting this information
is to exploit the rich relational data in digital social networks (the social
graph). As social media data sets are very large, most approaches make use of
distributed computing systems for this purpose. Distributing graph processing
requires solving many difficult engineering problems, which has lead some
researchers to look at single-machine solutions that are faster and easier to
maintain. In this article, we present a single-machine real-time system for
large-scale graph processing that allows analysts to interactively explore
graph structures. The key idea is that the aggregate actions of large numbers
of users can be compressed into a data structure that encapsulates user
similarities while being robust to noise and queryable in real-time. We achieve
single machine real-time performance by compressing the neighbourhood of each
vertex using minhash signatures and facilitate rapid queries through Locality
Sensitive Hashing. These techniques reduce query times from hours using
industrial desktop machines operating on the full graph to milliseconds on
standard laptops. Our method allows exploration of strongly associated regions
(i.e. communities) of large graphs in real-time on a laptop. It has been
deployed in software that is actively used by social network analysts and
offers another channel for media owners to monetise their data, helping them to
continue to provide free services that are valued by billions of people
globally.
Vanishing point detection with convolutional neural networks
Authors: Ali Borji
Date: 2016-09-04 00:00:00

Inspired by the finding that vanishing point (road tangent) guides driver's
gaze, in our previous work we showed that vanishing point attracts gaze during
free viewing of natural scenes as well as in visual search (Borji et al.,
Journal of Vision 2016). We have also introduced improved saliency models using
vanishing point detectors (Feng et al., WACV 2016). Here, we aim to predict
vanishing points in naturalistic environments by training convolutional neural
networks in an end-to-end manner over a large set of road images downloaded
from Youtube with vanishing points annotated. Results demonstrate effectiveness
of our approach compared to classic approaches of vanishing point detection in
the literature.
Deep Neural Networks for HDR imaging
Authors: Kshiteej Sheth
Date: 2016-09-04 00:00:00
Tasks: Tone Mapping

We propose novel methods of solving two tasks using Convolutional Neural
Networks, firstly the task of generating HDR map of a static scene using
differently exposed LDR images of the scene captured using conventional cameras
and secondly the task of finding an optimal tone mapping operator that would
give a better score on the TMQI metric compared to the existing methods. We
quantitatively show the performance of our networks and illustrate the cases
where our networks performs good as well as bad.
Decoding visual stimuli in human brain by using Anatomical Pattern Analysis on fMRI images
Authors: Muhammad Yousefnezhad, Daoqiang Zhang
Date: 2016-09-04 00:00:00
Tasks: Binary Classification, General Classification

A universal unanswered question in neuroscience and machine learning is
whether computers can decode the patterns of the human brain. Multi-Voxels
Pattern Analysis (MVPA) is a critical tool for addressing this question.
However, there are two challenges in the previous MVPA methods, which include
decreasing sparsity and noises in the extracted features and increasing the
performance of prediction. In overcoming mentioned challenges, this paper
proposes Anatomical Pattern Analysis (APA) for decoding visual stimuli in the
human brain. This framework develops a novel anatomical feature extraction
method and a new imbalance AdaBoost algorithm for binary classification.
Further, it utilizes an Error-Correcting Output Codes (ECOC) method for
multi-class prediction. APA can automatically detect active regions for each
category of the visual stimuli. Moreover, it enables us to combine homogeneous
datasets for applying advanced classification. Experimental studies on 4 visual
categories (words, consonants, objects and scrambled photos) demonstrate that
the proposed approach achieves superior performance to state-of-the-art
methods.
High Dimensional Human Guided Machine Learning
Authors: Eric Holloway, Robert Marks II
Date: 2016-09-04 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Vocal Bursts Intensity Prediction

Have you ever looked at a machine learning classification model and thought,
I could have made that? Well, that is what we test in this project, comparing
XGBoost trained on human engineered features to training directly on data. The
human engineered features do not outperform XGBoost trained di- rectly on the
data, but they are comparable. This project con- tributes a novel method for
utilizing human created classifi- cation models on high dimensional datasets.
Multiview Cauchy Estimator Feature Embedding for Depth and Inertial Sensor-Based Human Action Recognition
Authors: Yanan Guo, Lei LI, Weifeng Liu, Jun Cheng, Dapeng Tao
Date: 2016-08-07 00:00:00
Tasks: Action Recognition, Temporal Action Localization

The ever-growing popularity of Kinect and inertial sensors has prompted
intensive research efforts on human action recognition. Since human actions can
be characterized by multiple feature representations extracted from Kinect and
inertial sensors, multiview features must be encoded into a unified space
optimal for human action recognition. In this paper, we propose a new
unsupervised feature fusion method termed Multiview Cauchy Estimator Feature
Embedding (MCEFE) for human action recognition. By minimizing empirical risk,
MCEFE integrates the encoded complementary information in multiple views to
find the unified data representation and the projection matrices. To enhance
robustness to outliers, the Cauchy estimator is imposed on the reconstruction
error. Furthermore, ensemble manifold regularization is enforced on the
projection matrices to encode the correlations between different views and
avoid overfitting. Experiments are conducted on the new Chinese Academy of
Sciences - Yunnan University - Multimodal Human Action Database (CAS-YNU-MHAD)
to demonstrate the effectiveness and robustness of MCEFE for human action
recognition.
UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking
Authors: Longyin Wen, Dawei Du, Zhaowei Cai, Zhen Lei, Ming-Ching Chang, Honggang Qi, Jongwoo Lim, Ming-Hsuan Yang, Siwei Lyu
Date: 2015-11-13 00:00:00
Tasks: Multi-Object Tracking, Object, object-detection, Object Detection, Object Tracking

In recent years, numerous effective multi-object tracking (MOT) methods are developed because of the wide range of applications. Existing performance evaluations of MOT methods usually separate the object tracking step from the object detection step by using the same fixed object detection results for comparisons. In this work, we perform a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DETection and tRACking (UA-DETRAC) benchmark dataset. The UA-DETRAC benchmark dataset consists of 100 challenging video sequences captured from real-world traffic scenes (over 140,000 frames with rich annotations, including occlusion, weather, vehicle category, truncation, and vehicle bounding boxes) for object detection, object tracking and MOT system. We evaluate complete MOT systems constructed from combinations of state-of-the-art object detection and object tracking methods. Our analysis shows the complex effects of object detection accuracy on MOT system performance. Based on these observations, we propose new evaluation tools and metrics for MOT systems that consider both object detection and object tracking for comprehensive analysis.
Temperature-Based Deep Boltzmann Machines
Authors: Leandro Aparecido Passos Junior, Joao Paulo Papa
Date: 2016-08-27 00:00:00
Tasks: Image Reconstruction, speech-recognition, Speech Recognition, User Identification

Deep learning techniques have been paramount in the last years, mainly due to
their outstanding results in a number of applications, that range from speech
recognition to face-based user identification. Despite other techniques
employed for such purposes, Deep Boltzmann Machines are among the most used
ones, which are composed of layers of Restricted Boltzmann Machines (RBMs)
stacked on top of each other. In this work, we evaluate the concept of
temperature in DBMs, which play a key role in Boltzmann-related distributions,
but it has never been considered in this context up to date. Therefore, the
main contribution of this paper is to take into account this information and to
evaluate its influence in DBMs considering the task of binary image
reconstruction. We expect this work can foster future research considering the
usage of different temperatures during learning in DBMs.
A Probabilistic Optimum-Path Forest Classifier for Binary Classification Problems
Authors: Silas E. N. Fernandes, Danillo R. Pereira, Caio C. O. Ramos, Andre N. Souza, Joao P. Papa
Date: 2016-09-04 00:00:00
Tasks: Binary Classification, Classification, General Classification

Probabilistic-driven classification techniques extend the role of traditional
approaches that output labels (usually integer numbers) only. Such techniques
are more fruitful when dealing with problems where one is not interested in
recognition/identification only, but also into monitoring the behavior of
consumers and/or machines, for instance. Therefore, by means of probability
estimates, one can take decisions to work better in a number of scenarios. In
this paper, we propose a probabilistic-based Optimum Path Forest (OPF)
classifier to handle with binary classification problems, and we show it can be
more accurate than naive OPF in a number of datasets. In addition to being just
more accurate or not, probabilistic OPF turns to be another useful tool to the
scientific community.
Graph-Based Active Learning: A New Look at Expected Error Minimization
Authors: Kwang-Sung Jun, Robert Nowak
Date: 2016-09-03 00:00:00
Tasks: Active Learning

In graph-based active learning, algorithms based on expected error
minimization (EEM) have been popular and yield good empirical performance. The
exact computation of EEM optimally balances exploration and exploitation. In
practice, however, EEM-based algorithms employ various approximations due to
the computational hardness of exact EEM. This can result in a lack of either
exploration or exploitation, which can negatively impact the effectiveness of
active learning. We propose a new algorithm TSA (Two-Step Approximation) that
balances between exploration and exploitation efficiently while enjoying the
same computational complexity as existing approximations. Finally, we
empirically show the value of balancing between exploration and exploitation in
both toy and real-world datasets where our method outperforms several
state-of-the-art methods.
An Online Universal Classifier for Binary, Multi-class and Multi-label Classification
Authors: Meng Joo Er, Rajasekar Venkatesan, Ning Wang
Date: 2016-09-03 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Classification involves the learning of the mapping function that associates
input samples to corresponding target label. There are two major categories of
classification problems: Single-label classification and Multi-label
classification. Traditional binary and multi-class classifications are
sub-categories of single-label classification. Several classifiers are
developed for binary, multi-class and multi-label classification problems, but
there are no classifiers available in the literature capable of performing all
three types of classification. In this paper, a novel online universal
classifier capable of performing all the three types of classification is
proposed. Being a high speed online classifier, the proposed technique can be
applied to streaming data applications. The performance of the developed
classifier is evaluated using datasets from binary, multi-class and multi-label
problems. The results obtained are compared with state-of-the-art techniques
from each of the classification types.
Towards Bayesian Deep Learning: A Framework and Some Existing Methods
Authors: Hao Wang, Dit-yan Yeung
Date: 2016-08-24 00:00:00
Tasks: Deep Learning, Object Recognition, Recommendation Systems, Topic Models

While perception tasks such as visual object recognition and text
understanding play an important role in human intelligence, the subsequent
tasks that involve inference, reasoning and planning require an even higher
level of intelligence. The past few years have seen major advances in many
perception tasks using deep learning models. For higher-level inference,
however, probabilistic graphical models with their Bayesian nature are still
more powerful and flexible. To achieve integrated intelligence that involves
both perception and inference, it is naturally desirable to tightly integrate
deep learning and Bayesian models within a principled probabilistic framework,
which we call Bayesian deep learning. In this unified framework, the perception
of text or images using deep learning can boost the performance of higher-level
inference and in return, the feedback from the inference process is able to
enhance the perception of text or images. This paper proposes a general
framework for Bayesian deep learning and reviews its recent applications on
recommender systems, topic models, and control. In this paper, we also discuss
the relationship and differences between Bayesian deep learning and other
related topics like Bayesian treatment of neural networks.
Stochastic Learning of Multi-Instance Dictionary for Earth Mover's Distance based Histogram Comparison
Authors: Jihong Fan, Ru-Ze Liang
Date: 2016-09-03 00:00:00
Tasks: Dictionary Learning, Image Retrieval, Medical Image Retrieval, Multi-Instance Retrieval, Relation Classification, Retrieval, Stochastic Optimization, Triplet

Dictionary plays an important role in multi-instance data representation. It
maps bags of instances to histograms. Earth mover's distance (EMD) is the most
effective histogram distance metric for the application of multi-instance
retrieval. However, up to now, there is no existing multi-instance dictionary
learning methods designed for EMD based histogram comparison. To fill this gap,
we develop the first EMD-optimal dictionary learning method using stochastic
optimization method. In the stochastic learning framework, we have one triplet
of bags, including one basic bag, one positive bag, and one negative bag. These
bags are mapped to histograms using a multi-instance dictionary. We argue that
the EMD between the basic histogram and the positive histogram should be
smaller than that between the basic histogram and the negative histogram. Base
on this condition, we design a hinge loss. By minimizing this hinge loss and
some regularization terms of the dictionary, we update the dictionary
instances. The experiments over multi-instance retrieval applications shows its
effectiveness when compared to other dictionary learning methods over the
problems of medical image retrieval and natural language relation
classification.
Randomized Prediction Games for Adversarial Machine Learning
Authors: Samuel Rota Bulò, Battista Biggio, Ignazio Pillai, Marcello Pelillo, Fabio Roli
Date: 2016-09-03 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Handwritten Digit Recognition, Malware Detection, Prediction

In spam and malware detection, attackers exploit randomization to obfuscate
malicious data and increase their chances of evading detection at test time;
e.g., malware code is typically obfuscated using random strings or byte
sequences to hide known exploits. Interestingly, randomization has also been
proposed to improve security of learning algorithms against evasion attacks, as
it results in hiding information about the classifier to the attacker. Recent
work has proposed game-theoretical formulations to learn secure classifiers, by
simulating different evasion attacks and modifying the classification function
accordingly. However, both the classification function and the simulated data
manipulations have been modeled in a deterministic manner, without accounting
for any form of randomization. In this work, we overcome this limitation by
proposing a randomized prediction game, namely, a non-cooperative
game-theoretic formulation in which the classifier and the attacker make
randomized strategy selections according to some probability distribution
defined over the respective strategy set. We show that our approach allows one
to improve the trade-off between attack detection and false alarms with respect
to state-of-the-art secure classifiers, even against attacks that are different
from those hypothesized during design, on application examples including
handwritten digit recognition, spam and malware detection.
Lexical-Morphological Modeling for Legal Text Analysis
Authors: Danilo S. Carvalho, Minh-Tien Nguyen, Tran Xuan Chien, Minh Le Nguyen
Date: 2016-09-03 00:00:00
Tasks: Information Retrieval, Language Modeling, Language Modelling, Natural Language Inference, Question Answering, Retrieval

In the context of the Competition on Legal Information Extraction/Entailment
(COLIEE), we propose a method comprising the necessary steps for finding
relevant documents to a legal question and deciding on textual entailment
evidence to provide a correct answer. The proposed method is based on the
combination of several lexical and morphological characteristics, to build a
language model and a set of features for Machine Learning algorithms. We
provide a detailed study on the proposed method performance and failure cases,
indicating that it is competitive with state-of-the-art approaches on Legal
Information Retrieval and Question Answering, while not needing extensive
training data nor depending on expert produced knowledge. The proposed method
achieved significant results in the competition, indicating a substantial level
of adequacy for the tasks addressed.
Online Object Tracking, Learning and Parsing with And-Or Graphs
Authors: Tianfu Wu, Yang Lu, Song-Chun Zhu
Date: 2015-09-27 00:00:00
Proceeding: CVPR 2014 6
Tasks: Object Tracking

This paper presents a method, called AOGTracker, for simultaneously tracking,
learning and parsing (TLP) of unknown objects in video sequences with a
hierarchical and compositional And-Or graph (AOG) representation. %The AOG
captures both structural and appearance variations of a target object in a
principled way. The TLP method is formulated in the Bayesian framework with a
spatial and a temporal dynamic programming (DP) algorithms inferring object
bounding boxes on-the-fly. During online learning, the AOG is discriminatively
learned using latent SVM to account for appearance (e.g., lighting and partial
occlusion) and structural (e.g., different poses and viewpoints) variations of
a tracked object, as well as distractors (e.g., similar objects) in background.
Three key issues in online inference and learning are addressed: (i)
maintaining purity of positive and negative examples collected online, (ii)
controling model complexity in latent structure learning, and (iii) identifying
critical moments to re-learn the structure of AOG based on its intrackability.
The intrackability measures uncertainty of an AOG based on its score maps in a
frame. In experiments, our AOGTracker is tested on two popular tracking
benchmarks with the same parameter setting: the TB-100/50/CVPR2013 benchmarks,
and the VOT benchmarks --- VOT 2013, 2014, 2015 and TIR2015 (thermal imagery
tracking). In the former, our AOGTracker outperforms state-of-the-art tracking
algorithms including two trackers based on deep convolutional network. In the
latter, our AOGTracker outperforms all other trackers in VOT2013 and is
comparable to the state-of-the-art methods in VOT2014, 2015 and TIR2015.
A MIP Backend for the IDP System
Authors: San Pham, Jo Devriendt, Maurice Bruynooghe, Patrick De Causmaecker
Date: 2016-09-02 00:00:00

The IDP knowledge base system currently uses MiniSAT(ID) as its backend
Constraint Programming (CP) solver. A few similar systems have used a Mixed
Integer Programming (MIP) solver as backend. However, so far little is known
about when the MIP solver is preferable. This paper explores this question. It
describes the use of CPLEX as a backend for IDP and reports on experiments
comparing both backends.
A PAC Approach to Application-Specific Algorithm Selection
Authors: Rishi Gupta, Tim Roughgarden
Date: 2015-11-23 00:00:00
Tasks: Learning Theory

The best algorithm for a computational problem generally depends on the
"relevant inputs," a concept that depends on the application domain and often
defies formal articulation. While there is a large literature on empirical
approaches to selecting the best algorithm for a given application domain,
there has been surprisingly little theoretical analysis of the problem.
  This paper adapts concepts from statistical and online learning theory to
reason about application-specific algorithm selection. Our models capture
several state-of-the-art empirical and theoretical approaches to the problem,
ranging from self-improving algorithms to empirical performance models, and our
results identify conditions under which these approaches are guaranteed to
perform well. We present one framework that models algorithm selection as a
statistical learning problem, and our work here shows that dimension notions
from statistical learning theory, historically used to measure the complexity
of classes of binary- and real-valued functions, are relevant in a much broader
algorithmic context. We also study the online version of the algorithm
selection problem, and give possibility and impossibility results for the
existence of no-regret learning algorithms.
Determination of the edge of criticality in echo state networks through Fisher information maximization
Authors: Lorenzo Livi, Filippo Maria Bianchi, Cesare Alippi
Date: 2016-03-11 00:00:00

It is a widely accepted fact that the computational capability of recurrent
neural networks is maximized on the so-called "edge of criticality". Once the
network operates in this configuration, it performs efficiently on a specific
application both in terms of (i) low prediction error and (ii) high short-term
memory capacity. Since the behavior of recurrent networks is strongly
influenced by the particular input signal driving the dynamics, a universal,
application-independent method for determining the edge of criticality is still
missing. In this paper, we aim at addressing this issue by proposing a
theoretically motivated, unsupervised method based on Fisher information for
determining the edge of criticality in recurrent neural networks. It is proven
that Fisher information is maximized for (finite-size) systems operating in
such critical regions. However, Fisher information is notoriously difficult to
compute and either requires the probability density function or the conditional
dependence of the system states with respect to the model parameters. The paper
takes advantage of a recently-developed non-parametric estimator of the Fisher
information matrix and provides a method to determine the critical region of
echo state networks, a particular class of recurrent networks. The considered
control parameters, which indirectly affect the echo state network performance,
are explored to identify those configurations lying on the edge of criticality
and, as such, maximizing Fisher information and computational performance.
Experimental results on benchmarks and real-world data demonstrate the
effectiveness of the proposed method.