pwc-archive/papers-with-abstracts huggingface dataset:
Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters
Authors: Dmitry Kobak, Wieland Brendel, Christos Constantinidis, Claudia E. Feierstein, Adam Kepecs, Zachary F. Mainen, Ranulfo Romo, Xue-Lian Qi, Naoshige Uchida, Christian K. Machens
Date: 2014-10-22 00:00:00
Tasks: Decision Making, Dimensionality Reduction, Diversity

Neurons in higher cortical areas, such as the prefrontal cortex, are known to
be tuned to a variety of sensory and motor variables. The resulting diversity
of neural tuning often obscures the represented information. Here we introduce
a novel dimensionality reduction technique, demixed principal component
analysis (dPCA), which automatically discovers and highlights the essential
features in complex population activities. We reanalyze population data from
the prefrontal areas of rats and monkeys performing a variety of working memory
and decision-making tasks. In each case, dPCA summarizes the relevant features
of the population response in a single figure. The population activity is
decomposed into a few demixed components that capture most of the variance in
the data and that highlight dynamic tuning of the population to various task
parameters, such as stimuli, decisions, rewards, etc. Moreover, dPCA reveals
strong, condition-independent components of the population activity that remain
unnoticed with conventional approaches.
Salient Object Detection: A Discriminative Regional Feature Integration Approach
Authors: Huaizu Jiang, Zejian yuan, Ming-Ming Cheng, Yihong Gong, Nanning Zheng, Jingdong Wang
Date: 2014-10-22 00:00:00
Proceeding: CVPR 2013 6
Tasks: Image Segmentation, Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection, Semantic Segmentation

Salient object detection has been attracting a lot of interest, and recently
various heuristic computational models have been designed. In this paper, we
formulate saliency map computation as a regression problem. Our method, which
is based on multi-level image segmentation, utilizes the supervised learning
approach to map the regional feature vector to a saliency score. Saliency
scores across multiple levels are finally fused to produce the saliency map.
The contributions lie in two-fold. One is that we propose a discriminate
regional feature integration approach for salient object detection. Compared
with existing heuristic models, our proposed method is able to automatically
integrate high-dimensional regional saliency features and choose discriminative
ones. The other is that by investigating standard generic region properties as
well as two widely studied concepts for salient object detection, i.e.,
regional contrast and backgroundness, our approach significantly outperforms
state-of-the-art methods on six benchmark datasets. Meanwhile, we demonstrate
that our method runs as fast as most existing algorithms.
Active Regression by Stratification
Authors: Sivan Sabato, Remi Munos
Date: 2014-10-22 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Active Learning, General Classification, regression

We propose a new active learning algorithm for parametric linear regression
with random design. We provide finite sample convergence guarantees for general
distributions in the misspecified model. This is the first active learner for
this setting that provably can improve over passive learning. Unlike other
learning settings (such as classification), in regression the passive learning
rate of $O(1/\epsilon)$ cannot in general be improved upon. Nonetheless, the
so-called `constant' in the rate of convergence, which is characterized by a
distribution-dependent risk, can be improved in many cases. For a given
distribution, achieving the optimal risk requires prior knowledge of the
distribution. Following the stratification technique advocated in Monte-Carlo
function integration, our active learner approaches the optimal risk using
piecewise constant approximations.
A Spectral Framework for Anomalous Subgraph Detection
Authors: Benjamin A. Miller, Michelle S. Beard, Patrick J. Wolfe, Nadya T. Bliss
Date: 2014-01-29 00:00:00
Tasks: Community Detection

A wide variety of application domains are concerned with data consisting of
entities and their relationships or connections, formally represented as
graphs. Within these diverse application areas, a common problem of interest is
the detection of a subset of entities whose connectivity is anomalous with
respect to the rest of the data. While the detection of such anomalous
subgraphs has received a substantial amount of attention, no
application-agnostic framework exists for analysis of signal detectability in
graph-based data. In this paper, we describe a framework that enables such
analysis using the principal eigenspace of a graph's residuals matrix, commonly
called the modularity matrix in community detection. Leveraging this analytical
tool, we show that the framework has a natural power metric in the spectral
norm of the anomalous subgraph's adjacency matrix (signal power) and of the
background graph's residuals matrix (noise power). We propose several
algorithms based on spectral properties of the residuals matrix, with more
computationally expensive techniques providing greater detection power.
Detection and identification performance are presented for a number of signal
and noise models, including clusters and bipartite foregrounds embedded into
simple random backgrounds as well as graphs with community structure and
realistic degree distributions. The trends observed verify intuition gleaned
from other signal processing areas, such as greater detection power when the
signal is embedded within a less active portion of the background. We
demonstrate the utility of the proposed techniques in detecting small, highly
anomalous subgraphs in real graphs derived from Internet traffic and product
co-purchases.
Vehicle Detection and Tracking Techniques: A Concise Review
Authors: Raad Ahmed Hadi, Ghazali Sulong, Loay Edwar George
Date: 2014-10-22 00:00:00
Tasks: Management, vehicle detection

Vehicle detection and tracking applications play an important role for
civilian and military applications such as in highway traffic surveillance
control, management and urban traffic planning. Vehicle detection process on
road are used for vehicle tracking, counts, average speed of each individual
vehicle, traffic analysis and vehicle categorizing objectives and may be
implemented under different environments changes. In this review, we present a
concise overview of image processing methods and analysis tools which used in
building these previous mentioned applications that involved developing traffic
surveillance systems. More precisely and in contrast with other reviews, we
classified the processing methods under three categories for more clarification
to explain the traffic systems.
A Parallel and Efficient Algorithm for Learning to Match
Authors: Jingbo Shang, Tianqi Chen, Hang Li, Zhengdong Lu, Yong Yu
Date: 2014-10-22 00:00:00
Tasks: Collaborative Filtering, Link Prediction

Many tasks in data mining and related fields can be formalized as matching
between objects in two heterogeneous domains, including collaborative
filtering, link prediction, image tagging, and web search. Machine learning
techniques, referred to as learning-to-match in this paper, have been
successfully applied to the problems. Among them, a class of state-of-the-art
methods, named feature-based matrix factorization, formalize the task as an
extension to matrix factorization by incorporating auxiliary features into the
model. Unfortunately, making those algorithms scale to real world problems is
challenging, and simple parallelization strategies fail due to the complex
cross talking patterns between sub-tasks. In this paper, we tackle this
challenge with a novel parallel and efficient algorithm for feature-based
matrix factorization. Our algorithm, based on coordinate descent, can easily
handle hundreds of millions of instances and features on a single machine. The
key recipe of this algorithm is an iterative relaxation of the objective to
facilitate parallel updates of parameters, with guaranteed convergence on
minimizing the original objective function. Experimental results demonstrate
that the proposed method is effective on a wide range of matching problems,
with efficiency significantly improved upon the baselines while accuracy
retained unchanged.
Mean-Field Networks
Authors: Yujia Li, Richard Zemel
Date: 2014-10-21 00:00:00

The mean field algorithm is a widely used approximate inference algorithm for
graphical models whose exact inference is intractable. In each iteration of
mean field, the approximate marginals for each variable are updated by getting
information from the neighbors. This process can be equivalently converted into
a feedforward network, with each layer representing one iteration of mean field
and with tied weights on all layers. This conversion enables a few natural
extensions, e.g. untying the weights in the network. In this paper, we study
these mean field networks (MFNs), and use them as inference tools as well as
discriminative models. Preliminary experiment results show that MFNs can learn
to do inference very efficiently and perform significantly better than mean
field as discriminative models.
Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation
Authors: Michael Bloodgood, Chris Callison-Burch
Date: 2014-10-21 00:00:00
Tasks: Active Learning, Machine Translation, Translation

We explore how to improve machine translation systems by adding more
translation data in situations where we already have substantial resources. The
main challenge is how to buck the trend of diminishing returns that is commonly
encountered. We present an active learning-style data solicitation algorithm to
meet this challenge. We test it, gathering annotations via Amazon Mechanical
Turk, and find that we get an order of magnitude increase in performance rates
of improvement.
Compositional Structure Learning for Action Understanding
Authors: Ran Xu, Gang Chen, Caiming Xiong, Wei Chen, Jason J. Corso
Date: 2014-10-21 00:00:00
Tasks: Action Detection, Action Understanding, General Classification

The focus of the action understanding literature has predominately been
classification, how- ever, there are many applications demanding richer action
understanding such as mobile robotics and video search, with solutions to
classification, localization and detection. In this paper, we propose a
compositional model that leverages a new mid-level representation called
compositional trajectories and a locally articulated spatiotemporal deformable
parts model (LALSDPM) for fully action understanding. Our methods is
advantageous in capturing the variable structure of dynamic human activity over
a long range. First, the compositional trajectories capture long-ranging,
frequently co-occurring groups of trajectories in space time and represent them
in discriminative hierarchies, where human motion is largely separated from
camera motion; second, LASTDPM learns a structured model with multi-layer
deformable parts to capture multiple levels of articulated motion. We implement
our methods and demonstrate state of the art performance on all three problems:
action detection, localization, and recognition.
Generalized Compression Dictionary Distance as Universal Similarity Measure
Authors: Andrey Bogomolov, Bruno Lepri, Fabio Pianesi
Date: 2014-10-21 00:00:00
Tasks: Clustering, feature selection, General Classification

We present a new similarity measure based on information theoretic measures
which is superior than Normalized Compression Distance for clustering problems
and inherits the useful properties of conditional Kolmogorov complexity. We
show that Normalized Compression Dictionary Size and Normalized Compression
Dictionary Entropy are computationally more efficient, as the need to perform
the compression itself is eliminated. Also they scale linearly with exponential
vector size growth and are content independent. We show that normalized
compression dictionary distance is compressor independent, if limited to
lossless compressors, which gives space for optimizations and implementation
speed improvement for real-time and big data applications. The introduced
measure is applicable for machine learning tasks of parameter-free unsupervised
clustering, supervised learning such as classification and regression, feature
selection, and is applicable for big data problems with order of magnitude
speed increase.
Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
Authors: Andrey Bogomolov, Bruno Lepri, Michela Ferron, Fabio Pianesi, Alex, Pentland
Date: 2014-10-21 00:00:00

Research has proven that stress reduces quality of life and causes many
diseases. For this reason, several researchers devised stress detection systems
based on physiological parameters. However, these systems require that
obtrusive sensors are continuously carried by the user. In our paper, we
propose an alternative approach providing evidence that daily stress can be
reliably recognized based on behavioral metrics, derived from the user's mobile
phone activity and from additional indicators, such as the weather conditions
(data pertaining to transitory properties of the environment) and the
personality traits (data concerning permanent dispositions of individuals). Our
multifactorial statistical model, which is person-independent, obtains the
accuracy score of 72.28% for a 2-class daily stress recognition problem. The
model is efficient to implement for most of multimedia applications due to
highly reduced low-dimensional feature space (32d). Moreover, we identify and
discuss the indicators which have strong predictive power.
Investigation of A Collective Decision Making System of Different Neighbourhood-Size Based on Hyper-Geometric Distribution
Authors: Debdipta Goswami, Heiko Hamann
Date: 2014-10-21 00:00:00
Tasks: Decision Making

The study of collective decision making system has become the central part of
the Swarm- Intelligence Related research in recent years. The most challenging
task of modelling a collec- tive decision making system is to develop the
macroscopic stochastic equation from its microscopic model. In this report we
have investigated the behaviour of a collective decision making system with
specified microscopic rules that resemble the chemical reaction and used
different group size. Then we ventured to derive a generalized analytical model
of a collective-decision system using hyper-geometric distribution.
  Index Terms-swarm; collective decision making; noise; group size;
hyper-geometric distribution
Regularizing Recurrent Networks - On Injected Noise and Norm-based Methods
Authors: Saahil Ognawala, Justin Bayer
Date: 2014-10-21 00:00:00

Advancements in parallel processing have lead to a surge in multilayer
perceptrons' (MLP) applications and deep learning in the past decades.
Recurrent Neural Networks (RNNs) give additional representational power to
feedforward MLPs by providing a way to treat sequential data. However, RNNs are
hard to train using conventional error backpropagation methods because of the
difficulty in relating inputs over many time-steps. Regularization approaches
from MLP sphere, like dropout and noisy weight training, have been
insufficiently applied and tested on simple RNNs. Moreover, solutions have been
proposed to improve convergence in RNNs but not enough to improve the long term
dependency remembering capabilities thereof.
  In this study, we aim to empirically evaluate the remembering and
generalization ability of RNNs on polyphonic musical datasets. The models are
trained with injected noise, random dropout, norm-based regularizers and their
respective performances compared to well-initialized plain RNNs and advanced
regularization methods like fast-dropout. We conclude with evidence that
training with noise does not improve performance as conjectured by a few works
in RNN optimization before ours.
Improvement of PSO algorithm by memory based gradient search - application in inventory management
Authors: Tamás Varga, András Király, János Abonyi
Date: 2014-10-21 00:00:00
Tasks: Management

Advanced inventory management in complex supply chains requires effective and
robust nonlinear optimization due to the stochastic nature of supply and demand
variations. Application of estimated gradients can boost up the convergence of
Particle Swarm Optimization (PSO) algorithm but classical gradient calculation
cannot be applied to stochastic and uncertain systems. In these situations
Monte-Carlo (MC) simulation can be applied to determine the gradient. We
developed a memory based algorithm where instead of generating and evaluating
new simulated samples the stored and shared former function evaluations of the
particles are sampled to estimate the gradients by local weighted least squares
regression. The performance of the resulted regional gradient-based PSO is
verified by several benchmark problems and in a complex application example
where optimal reorder points of a supply chain are determined.
A Robust Ensemble Approach to Learn From Positive and Unlabeled Data Using SVM Base Models
Authors: Marc Claesen, Frank De Smet, Johan A. K. Suykens, Bart De Moor
Date: 2014-02-13 00:00:00

We present a novel approach to learn binary classifiers when only positive
and unlabeled instances are available (PU learning). This problem is routinely
cast as a supervised task with label noise in the negative set. We use an
ensemble of SVM models trained on bootstrap resamples of the training data for
increased robustness against label noise. The approach can be considered in a
bagging framework which provides an intuitive explanation for its mechanics in
a semi-supervised setting. We compared our method to state-of-the-art
approaches in simulations using multiple public benchmark data sets. The
included benchmark comprises three settings with increasing label noise: (i)
fully supervised, (ii) PU learning and (iii) PU learning with false positives.
Our approach shows a marginal improvement over existing methods in the second
setting and a significant improvement in the third.
The Tomaco Hybrid Matching Framework for SAWSDL Semantic Web Services
Authors: Thanos G. Stavropoulos, Stelios Andreadis, Nick Bassiliades, Dimitris Vrakas, Ioannis Vlahavas
Date: 2014-10-21 00:00:00
Tasks: Retrieval, text similarity

This work aims to resolve issues related to Web Service retrieval, also known
as Service Selection, Discovery or essentially Matching, in two directions.
Firstly, a novel matching algorithm for SAWSDL is introduced. The algorithm is
hybrid in nature, combining novel and known concepts, such as a logic-based
strategy and syntactic text-similarity measures on semantic annotations and
textual descriptions. A plugin for the S3 contest environment was developed, in
order to position Tomaco amongst state-of-the-art in an objective, reproducible
manner. Evaluation showed that Tomaco ranks high amongst state of the art,
especially for early recall levels. Secondly, this work introduces the Tomaco
web application, which aims to accelerate the wide-spread adoption of Semantic
Web Service technologies and algorithms while targeting the lack of
user-friendly applications in this field. Tomaco integrates a variety of
configurable matching algorithms proposed in this paper. It, finally, allows
discovery of both existing and user-contributed service collections and
ontologies, serving also as a service registry.
Mobility Enhancement for Elderly
Authors: Ramviyas Parasuraman
Date: 2014-10-21 00:00:00
Tasks: Navigate, speech-recognition, Speech Recognition

Loss of Mobility is a common handicap to senior citizens. It denies them the
ease of movement they would like to have like outdoor visits, movement in
hospitals, social outgoings, but more seriously in the day to day in-house
routine functions necessary for living etc. Trying to overcome this handicap by
means of servant or domestic help and simple wheel chairs is not only costly in
the long run, but forces the senior citizen to be at the mercy of sincerity of
domestic helps and also the consequent loss of dignity. In order to give a
dignified life, the mobility obtained must be at the complete discretion, will
and control of the senior citizen. This can be provided only by a reasonably
sophisticated and versatile wheel chair, giving enhanced ability of vision,
hearing through man-machine interface, and sensor aided navigation and control.
More often than not senior people have poor vision which makes it difficult for
them to maker visual judgement and so calls for the use of Artificial
Intelligence in visual image analysis and guided navigation systems.
  In this project, we deal with two important enhancement features for mobility
enhancement, Audio command and Vision aided obstacle detection and navigation.
We have implemented speech recognition algorithm using template of stored words
for identifying the voice command given by the user. This frees the user of an
agile hand to operate joystick or mouse control. Also, we have developed a new
appearance based obstacle detection system using stereo-vision cameras which
estimates the distance of nearest obstacle to the wheel chair and takes
necessary action. This helps user in making better judgement of route and
navigate obstacles. The main challenge in this project is how to navigate in an
unknown/unfamiliar environment by avoiding obstacles.
On Iterative Hard Thresholding Methods for High-dimensional M-Estimation
Authors: Prateek Jain, Ambuj Tewari, Purushottam Kar
Date: 2014-10-20 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: regression, Vocal Bursts Intensity Prediction

The use of M-estimators in generalized linear regression models in high
dimensional settings requires risk minimization with hard $L_0$ constraints. Of
the known methods, the class of projected gradient descent (also known as
iterative hard thresholding (IHT)) methods is known to offer the fastest and
most scalable solutions. However, the current state-of-the-art is only able to
analyze these methods in extremely restrictive settings which do not hold in
high dimensional statistical models. In this work we bridge this gap by
providing the first analysis for IHT-style methods in the high dimensional
statistical setting. Our bounds are tight and match known minimax lower bounds.
Our results rely on a general analysis framework that enables us to analyze
several popular hard thresholding style algorithms (such as HTP, CoSaMP, SP) in
the high dimensional regression setting. We also extend our analysis to a large
family of "fully corrective methods" that includes two-stage and partial
hard-thresholding algorithms. We show that our results hold for the problem of
sparse regression, as well as low-rank matrix recovery.
Where do goals come from? A Generic Approach to Autonomous Goal-System Development
Authors: Matthias Rolf, Minoru Asada
Date: 2014-10-21 00:00:00
Tasks: Dimensionality Reduction, Reinforcement Learning

Goals express agents' intentions and allow them to organize their behavior
based on low-dimensional abstractions of high-dimensional world states. How can
agents develop such goals autonomously? This paper proposes a detailed
conceptual and computational account to this longstanding problem. We argue to
consider goals as high-level abstractions of lower-level intention mechanisms
such as rewards and values, and point out that goals need to be considered
alongside with a detection of the own actions' effects. We propose Latent Goal
Analysis as a computational learning formulation thereof, and show
constructively that any reward or value function can by explained by goals and
such self-detection as latent mechanisms. We first show that learned goals
provide a highly effective dimensionality reduction in a practical
reinforcement learning problem. Then, we investigate a developmental scenario
in which entirely task-unspecific rewards induced by visual saliency lead to
self and goal representations that constitute goal-directed reaching.