pwc-archive/papers-with-abstracts huggingface dataset:
Learning Temporal Alignment Uncertainty for Efficient Event Detection
Authors: Iman Abbasnejad, Sridha Sridharan, Simon Denman, Clinton Fookes, Simon Lucey
Date: 2015-09-04 00:00:00
Tasks: Event Detection

In this paper we tackle the problem of efficient video event detection. We
argue that linear detection functions should be preferred in this regard due to
their scalability and efficiency during estimation and evaluation. A popular
approach in this regard is to represent a sequence using a bag of words (BOW)
representation due to its: (i) fixed dimensionality irrespective of the
sequence length, and (ii) its ability to compactly model the statistics in the
sequence. A drawback to the BOW representation, however, is the intrinsic
destruction of the temporal ordering information. In this paper we propose a
new representation that leverages the uncertainty in relative temporal
alignments between pairs of sequences while not destroying temporal ordering.
Our representation, like BOW, is of a fixed dimensionality making it easily
integrated with a linear detection function. Extensive experiments on CK+,
6DMG, and UvA-NEMO databases show significant performance improvements across
both isolated and continuous event detection tasks.
l1-norm Penalized Orthogonal Forward Regression
Authors: Xia Hong, Sheng Chen, Yi Guo, Junbin Gao
Date: 2015-09-04 00:00:00
Tasks: regression

A l1-norm penalized orthogonal forward regression (l1-POFR) algorithm is
proposed based on the concept of leaveone- out mean square error (LOOMSE).
Firstly, a new l1-norm penalized cost function is defined in the constructed
orthogonal space, and each orthogonal basis is associated with an individually
tunable regularization parameter. Secondly, due to orthogonal computation, the
LOOMSE can be analytically computed without actually splitting the data set,
and moreover a closed form of the optimal regularization parameter in terms of
minimal LOOMSE is derived. Thirdly, a lower bound for regularization parameters
is proposed, which can be used for robust LOOMSE estimation by adaptively
detecting and removing regressors to an inactive set so that the computational
cost of the algorithm is significantly reduced. Illustrative examples are
included to demonstrate the effectiveness of this new l1-POFR approach.
Linear Global Translation Estimation with Feature Tracks
Authors: Zhaopeng Cui, Nianjuan Jiang, Chengzhou Tang, Ping Tan
Date: 2015-03-06 00:00:00
Tasks: Position, Translation

This paper derives a novel linear position constraint for cameras seeing a
common scene point, which leads to a direct linear method for global camera
translation estimation. Unlike previous solutions, this method deals with
collinear camera motion and weak image association at the same time. The final
linear formulation does not involve the coordinates of scene points, which
makes it efficient even for large scale data. We solve the linear equation
based on $L_1$ norm, which makes our system more robust to outliers in
essential matrices and feature correspondences. We experiment this method on
both sequentially captured images and unordered Internet images. The
experiments demonstrate its strength in robustness, accuracy, and efficiency.
The influence of Chunking on Dependency Crossing and Distance
Authors: Qian Lu, Chunshan Xu, Haitao Liu
Date: 2015-09-03 00:00:00
Tasks: Chunking

This paper hypothesizes that chunking plays important role in reducing
dependency distance and dependency crossings. Computer simulations, when
compared with natural languages,show that chunking reduces mean dependency
distance (MDD) of a linear sequence of nodes (constrained by continuity or
projectivity) to that of natural languages. More interestingly, chunking alone
brings about less dependency crossings as well, though having failed to reduce
them, to such rarity as found in human languages. These results suggest that
chunking may play a vital role in the minimization of dependency distance, and
a somewhat contributing role in the rarity of dependency crossing. In addition,
the results point to a possibility that the rarity of dependency crossings is
not a mere side-effect of minimization of dependency distance, but a linguistic
phenomenon with its own motivations.
Incremental Active Opinion Learning Over a Stream of Opinionated Documents
Authors: Max Zimmermann, Eirini Ntoutsi, Myra Spiliopoulou
Date: 2015-09-03 00:00:00
Tasks: Active Learning

Applications that learn from opinionated documents, like tweets or product
reviews, face two challenges. First, the opinionated documents constitute an
evolving stream, where both the author's attitude and the vocabulary itself may
change. Second, labels of documents are scarce and labels of words are
unreliable, because the sentiment of a word depends on the (unknown) context in
the author's mind. Most of the research on mining over opinionated streams
focuses on the first aspect of the problem, whereas for the second a continuous
supply of labels from the stream is assumed. Such an assumption though is
utopian as the stream is infinite and the labeling cost is prohibitive. To this
end, we investigate the potential of active stream learning algorithms that ask
for labels on demand. Our proposed ACOSTREAM 1 approach works with limited
labels: it uses an initial seed of labeled documents, occasionally requests
additional labels for documents from the human expert and incrementally adapts
to the underlying stream while exploiting the available labeled documents. In
its core, ACOSTREAM consists of a MNB classifier coupled with "sampling"
strategies for requesting class labels for new unlabeled documents. In the
experiments, we evaluate the classifier performance over time by varying: (a)
the class distribution of the opinionated stream, while assuming that the set
of the words in the vocabulary is fixed but their polarities may change with
the class distribution; and (b) the number of unknown words arriving at each
moment, while the class polarity may also change. Our results show that active
learning on a stream of opinionated documents, delivers good performance while
requiring a small selection of labels
Image Classification with Rejection using Contextual Information
Authors: Filipe Condessa, José Bioucas-Dias, Carlos Castro, John Ozolek, Jelena Kovačević
Date: 2015-09-03 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

We introduce a new supervised algorithm for image classification with
rejection using multiscale contextual information. Rejection is desired in
image-classification applications that require a robust classifier but not the
classification of the entire image. The proposed algorithm combines local and
multiscale contextual information with rejection, improving the classification
performance. As a probabilistic model for classification, we adopt a
multinomial logistic regression. The concept of rejection with contextual
information is implemented by modeling the classification problem as an energy
minimization problem over a graph representing local and multiscale
similarities of the image. The rejection is introduced through an energy data
term associated with the classification risk and the contextual information
through an energy smoothness term associated with the local and multiscale
similarities within the image. We illustrate the proposed method on the
classification of images of H&E-stained teratoma tissues.
Probabilistic Neural Network Training for Semi-Supervised Classifiers
Authors: Hamidreza Farhidzadeh
Date: 2015-09-03 00:00:00
Tasks: General Classification

In this paper, we propose another version of help-training approach by
employing a Probabilistic Neural Network (PNN) that improves the performance of
the main discriminative classifier in the semi-supervised strategy. We
introduce the PNN-training algorithm and use it for training the support vector
machine (SVM) with a few numbers of labeled data and a large number of
unlabeled data. We try to find the best labels for unlabeled data and then use
SVM to enhance the classification rate. We test our method on two famous
benchmarks and show the efficiency of our method in comparison with pervious
methods.
Machine Learning Methods to Analyze Arabidopsis Thaliana Plant Root Growth
Authors: Hamidreza Farhidzadeh
Date: 2015-09-03 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

One of the challenging problems in biology is to classify plants based on
their reaction on genetic mutation. Arabidopsis Thaliana is a plant that is so
interesting, because its genetic structure has some similarities with that of
human beings. Biologists classify the type of this plant to mutated and not
mutated (wild) types. Phenotypic analysis of these types is a time-consuming
and costly effort by individuals. In this paper, we propose a modified feature
extraction step by using velocity and acceleration of root growth. In the
second step, for plant classification, we employed different Support Vector
Machine (SVM) kernels and two hybrid systems of neural networks. Gated Negative
Correlation Learning (GNCL) and Mixture of Negatively Correlated Experts (MNCE)
are two ensemble methods based on complementary feature of classical
classifiers; Mixture of Expert (ME) and Negative Correlation Learning (NCL).
The hybrid systems conserve of advantages and decrease the effects of
disadvantages of NCL and ME. Our Experimental shows that MNCE and GNCL improve
the efficiency of classical classifiers, however, some SVM kernels function has
better performance than classifiers based on neural network ensemble method.
Moreover, kernels consume less time to obtain a classification rate.
A Neural Attention Model for Abstractive Sentence Summarization
Authors: Alexander M. Rush, Sumit Chopra, Jason Weston
Date: 2015-09-02 00:00:00
Proceeding: EMNLP 2015 9
Tasks: Extractive Text Summarization, model, Sentence, Sentence Summarization, Text Summarization

Summarization based on text extraction is inherently limited, but
generation-style abstractive methods have proven challenging to build. In this
work, we propose a fully data-driven approach to abstractive sentence
summarization. Our method utilizes a local attention-based model that generates
each word of the summary conditioned on the input sentence. While the model is
structurally simple, it can easily be trained end-to-end and scales to a large
amount of training data. The model shows significant performance gains on the
DUC-2004 shared task compared with several strong baselines.
Toward a generic representation of random variables for machine learning
Authors: Gautier Marti, Philippe Very, Philippe Donnat
Date: 2015-06-02 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Time Series, Time Series Analysis

This paper presents a pre-processing and a distance which improve the
performance of machine learning algorithms working on independent and
identically distributed stochastic processes. We introduce a novel
non-parametric approach to represent random variables which splits apart
dependency and distribution without losing any information. We also propound an
associated metric leveraging this representation and its statistical estimate.
Besides experiments on synthetic datasets, the benefits of our contribution is
illustrated through the example of clustering financial time series, for
instance prices from the credit default swaps market. Results are available on
the website www.datagrapple.com and an IPython Notebook tutorial is available
at www.datagrapple.com/Tech for reproducible research.
Community Detection in Networks with Node Features
Authors: Yuan Zhang, Elizaveta Levina, Ji Zhu
Date: 2015-09-03 00:00:00
Tasks: Community Detection

Many methods have been proposed for community detection in networks, but most
of them do not take into account additional information on the nodes that is
often available in practice. In this paper, we propose a new joint community
detection criterion that uses both the network edge information and the node
features to detect community structures. One advantage our method has over
existing joint detection approaches is the flexibility of learning the impact
of different features which may differ across communities. Another advantage is
the flexibility of choosing the amount of influence the feature information has
on communities. The method is asymptotically consistent under the block model
with additional assumptions on the feature distributions, and performs well on
simulated and real networks.
Semi-described and semi-supervised learning with Gaussian processes
Authors: Andreas Damianou, Neil D. Lawrence
Date: 2015-09-03 00:00:00
Tasks: Gaussian Processes, Missing Values

Propagating input uncertainty through non-linear Gaussian process (GP)
mappings is intractable. This hinders the task of training GPs using uncertain
and partially observed inputs. In this paper we refer to this task as
"semi-described learning". We then introduce a GP framework that solves both,
the semi-described and the semi-supervised learning problems (where missing
values occur in the outputs). Auto-regressive state space simulation is also
recognised as a special case of semi-described learning. To achieve our goal we
develop variational methods for handling semi-described inputs in GPs, and
couple them with algorithms that allow for imputing the missing values while
treating the uncertainty in a principled, Bayesian manner. Extensive
experiments on simulated and real-world data study the problems of iterative
forecasting and regression/classification with missing values. The results
suggest that the principled propagation of uncertainty stemming from our
framework can significantly improve performance in these tasks.
Training of CC4 Neural Network with Spread Unary Coding
Authors: Pushpa Sree Potluri
Date: 2015-09-03 00:00:00
Tasks: Classification, General Classification

This paper adapts the corner classification algorithm (CC4) to train the
neural networks using spread unary inputs. This is an important problem as
spread unary appears to be at the basis of data representation in biological
learning. The modified CC4 algorithm is tested using the pattern classification
experiment and the results are found to be good. Specifically, we show that the
number of misclassified points is not particularly sensitive to the chosen
radius of generalization.
Vision-Based Road Detection using Contextual Blocks
Authors: Caio César Teodoro Mendes, Vincent Frémont, Denis Fernando Wolf
Date: 2015-09-03 00:00:00
Tasks: Autonomous Navigation, Computational Efficiency, feature selection

Road detection is a fundamental task in autonomous navigation systems. In
this paper, we consider the case of monocular road detection, where images are
segmented into road and non-road regions. Our starting point is the well-known
machine learning approach, in which a classifier is trained to distinguish road
and non-road regions based on hand-labeled images. We proceed by introducing
the use of "contextual blocks" as an efficient way of providing contextual
information to the classifier. Overall, the proposed methodology, including its
image feature selection and classifier, was conceived with computational cost
in mind, leaving room for optimized implementations. Regarding experiments, we
perform a sensible evaluation of each phase and feature subset that composes
our system. The results show a great benefit from using contextual blocks and
demonstrate their computational efficiency. Finally, we submit our results to
the KITTI road detection benchmark achieving scores comparable with state of
the art methods.
A Novice Guide towards Human Motion Analysis and Understanding
Authors: Ahmed Nabil Mohamed
Date: 2015-09-03 00:00:00
Tasks: Philosophy

Human motion analysis and understanding has been, and is still, the focus of
attention of many disciplines which is considered an obvious indicator of the
wide and massive importance of the subject. The purpose of this article is to
shed some light on this very important subject, so it can be a good insight for
a novice computer vision researcher in this field by providing him/her with a
wealth of knowledge about the subject covering many directions. There are two
main contributions of this article. The first one investigates various aspects
of some disciplines (e.g., arts, philosophy, psychology, and neuroscience) that
are interested in the subject and review some of their contributions stressing
on those that can be useful for computer vision researchers. Moreover, many
examples are illustrated to indicate the benefits of integrating concepts and
results among different disciplines. The second contribution is concerned with
the subject from the computer vision aspect where we discuss the following
issues. First, we explore many demanding and promising applications to reveal
the wide and massive importance of the field. Second, we list various types of
sensors that may be used for acquiring various data. Third, we review different
taxonomies used for classifying motions. Fourth, we review various processes
involved in motion analysis. Fifth, we exhibit how different surveys are
structured. Sixth, we examine many of the most cited and recent reviews in the
field that have been published during the past two decades to reveal various
approaches used for implementing different stages of the problem and refer to
various algorithms and their suitability for different situations. Moreover, we
provide a long list of public datasets and discuss briefly some examples of
these datasets. Finally, we provide a general discussion of the subject from
the aspect of computer vision.
Training a Restricted Boltzmann Machine for Classification by Labeling Model Samples
Authors: Malte Probst, Franz Rothlauf
Date: 2015-09-03 00:00:00
Tasks: Classification, General Classification

We propose an alternative method for training a classification model. Using
the MNIST set of handwritten digits and Restricted Boltzmann Machines, it is
possible to reach a classification performance competitive to semi-supervised
learning if we first train a model in an unsupervised fashion on unlabeled data
only, and then manually add labels to model samples instead of training data
samples with the help of a GUI. This approach can benefit from the fact that
model samples can be presented to the human labeler in a video-like fashion,
resulting in a higher number of labeled examples. Also, after some initial
training, hard-to-classify examples can be distinguished from easy ones
automatically, saving manual work.
Building a Truly Distributed Constraint Solver with JADE
Authors: Ibrahim Adeyanju
Date: 2015-09-03 00:00:00
Tasks: Scheduling

Real life problems such as scheduling meeting between people at different
locations can be modelled as distributed Constraint Satisfaction Problems
(CSPs). Suitable and satisfactory solutions can then be found using constraint
satisfaction algorithms which can be exhaustive (backtracking) or otherwise
(local search). However, most research in this area tested their algorithms by
simulation on a single PC with a single program entry point. The main
contribution of our work is the design and implementation of a truly
distributed constraint solver based on a local search algorithm using Java
Agent DEvelopment framework (JADE) to enable communication between agents on
different machines. Particularly, we discuss design and implementation issues
related to truly distributed constraint solver which might not be critical when
simulated on a single machine. Evaluation results indicate that our truly
distributed constraint solver works well within the observed limitations when
tested with various distributed CSPs. Our application can also incorporate any
constraint solving algorithm with little modifications.
Extending local features with contextual information in graph kernels
Authors: Nicolò Navarin, Alessandro Sperduti, Riccardo Tesselli
Date: 2015-07-08 00:00:00
Tasks: Graph Classification

Graph kernels are usually defined in terms of simpler kernels over local
substructures of the original graphs. Different kernels consider different
types of substructures. However, in some cases they have similar predictive
performances, probably because the substructures can be interpreted as
approximations of the subgraphs they induce. In this paper, we propose to
associate to each feature a piece of information about the context in which the
feature appears in the graph. A substructure appearing in two different graphs
will match only if it appears with the same context in both graphs. We propose
a kernel based on this idea that considers trees as substructures, and where
the contexts are features too. The kernel is inspired from the framework in
[6], even if it is not part of it. We give an efficient algorithm for computing
the kernel and show promising results on real-world graph classification
datasets.
Generating Weather Forecast Texts with Case Based Reasoning
Authors: Ibrahim Adeyanju
Date: 2015-09-03 00:00:00
Tasks: Retrieval, Text Generation

Several techniques have been used to generate weather forecast texts. In this
paper, case based reasoning (CBR) is proposed for weather forecast text
generation because similar weather conditions occur over time and should have
similar forecast texts. CBR-METEO, a system for generating weather forecast
texts was developed using a generic framework (jCOLIBRI) which provides modules
for the standard components of the CBR architecture. The advantage in a CBR
approach is that systems can be built in minimal time with far less human
effort after initial consultation with experts. The approach depends heavily on
the goodness of the retrieval and revision components of the CBR process. We
evaluated CBRMETEO with NIST, an automated metric which has been shown to
correlate well with human judgements for this domain. The system shows
comparable performance with other NLG systems that perform the same task.
Sampling-based Causal Inference in Cue Combination and its Neural Implementation
Authors: Zhaofei Yu, Feng Chen, Jianwu Dong, Qionghai Dai
Date: 2015-09-03 00:00:00
Tasks: Causal Inference

Causal inference in cue combination is to decide whether the cues have a
single cause or multiple causes. Although the Bayesian causal inference model
explains the problem of causal inference in cue combination successfully, how
causal inference in cue combination could be implemented by neural circuits, is
unclear. The existing method based on calculating log posterior ratio with
variable elimination has the problem of being unrealistic and task-specific. In
this paper, we take advantages of the special structure of the Bayesian causal
inference model and propose a hierarchical inference algorithm based on
importance sampling. A simple neural circuit is designed to implement the
proposed inference algorithm. Theoretical analyses and experimental results
demonstrate that our algorithm converges to the accurate value as the sample
size goes to infinite. Moreover, the neural circuit we design can be easily
generalized to implement inference for other problems, such as the
multi-stimuli cause inference and the same-different judgment.