pwc-archive/papers-with-abstracts huggingface dataset:
Large scale biomedical texts classification: a kNN and an ESA-based approaches
Authors: Khadim Dramé, Fleur Mougin, Gayo Diallo
Date: 2016-06-09 00:00:00
Tasks: Classification, General Classification, Information Retrieval, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Retrieval, text-classification, Text Classification

With the large and increasing volume of textual data, automated methods for
identifying significant topics to classify textual documents have received a
growing interest. While many efforts have been made in this direction, it still
remains a real challenge. Moreover, the issue is even more complex as full
texts are not always freely available. Then, using only partial information to
annotate these documents is promising but remains a very ambitious issue.
MethodsWe propose two classification methods: a k-nearest neighbours
(kNN)-based approach and an explicit semantic analysis (ESA)-based approach.
Although the kNN-based approach is widely used in text classification, it needs
to be improved to perform well in this specific classification problem which
deals with partial information. Compared to existing kNN-based methods, our
method uses classical Machine Learning (ML) algorithms for ranking the labels.
Additional features are also investigated in order to improve the classifiers'
performance. In addition, the combination of several learning algorithms with
various techniques for fixing the number of relevant topics is performed. On
the other hand, ESA seems promising for this classification task as it yielded
interesting results in related issues, such as semantic relatedness computation
between texts and text classification. Unlike existing works, which use ESA for
enriching the bag-of-words approach with additional knowledge-based features,
our ESA-based method builds a standalone classifier. Furthermore, we
investigate if the results of this method could be useful as a complementary
feature of our kNN-based approach.ResultsExperimental evaluations performed on
large standard annotated datasets, provided by the BioASQ organizers, show that
the kNN-based method with the Random Forest learning algorithm achieves good
performances compared with the current state-of-the-art methods, reaching a
competitive f-measure of 0.55% while the ESA-based approach surprisingly
yielded reserved results.ConclusionsWe have proposed simple classification
methods suitable to annotate textual documents using only partial information.
They are therefore adequate for large multi-label classification and
particularly in the biomedical domain. Thus, our work contributes to the
extraction of relevant information from unstructured documents in order to
facilitate their automated processing. Consequently, it could be used for
various purposes, including document indexing, information retrieval, etc.
Con-Patch: When a Patch Meets its Context
Authors: Yaniv Romano, Michael Elad
Date: 2016-03-22 00:00:00
Tasks: Denoising, Image Denoising, Image Super-Resolution, Super-Resolution

Measuring the similarity between patches in images is a fundamental building
block in various tasks. Naturally, the patch-size has a major impact on the
matching quality, and on the consequent application performance. Under the
assumption that our patch database is sufficiently sampled, using large patches
(e.g. 21-by-21) should be preferred over small ones (e.g. 7-by-7). However,
this "dense-sampling" assumption is rarely true; in most cases large patches
cannot find relevant nearby examples. This phenomenon is a consequence of the
curse of dimensionality, stating that the database-size should grow
exponentially with the patch-size to ensure proper matches. This explains the
favored choice of small patch-size in most applications.
  Is there a way to keep the simplicity and work with small patches while
getting some of the benefits that large patches provide? In this work we offer
such an approach. We propose to concatenate the regular content of a
conventional (small) patch with a compact representation of its (large)
surroundings - its context. Therefore, with a minor increase of the dimensions
(e.g. with additional 10 values to the patch representation), we
implicitly/softly describe the information of a large patch. The additional
descriptors are computed based on a self-similarity behavior of the patch
surrounding.
  We show that this approach achieves better matches, compared to the use of
conventional-size patches, without the need to increase the database-size.
Also, the effectiveness of the proposed method is tested on three distinct
problems: (i) External natural image denoising, (ii) Depth image
super-resolution, and (iii) Motion-compensated frame-rate up-conversion.
Apparent Age Estimation Using Ensemble of Deep Learning Models
Authors: Refik Can Malli, Mehmet Aygun, Hazim Kemal Ekenel
Date: 2016-06-09 00:00:00
Tasks: Age Estimation, Deep Learning

In this paper, we address the problem of apparent age estimation. Different
from estimating the real age of individuals, in which each face image has a
single age label, in this problem, face images have multiple age labels,
corresponding to the ages perceived by the annotators, when they look at these
images. This provides an intriguing computer vision problem, since in generic
image or object classification tasks, it is typical to have a single ground
truth label per class. To account for multiple labels per image, instead of
using average age of the annotated face image as the class label, we have
grouped the face images that are within a specified age range. Using these age
groups and their age-shifted groupings, we have trained an ensemble of deep
learning models. Before feeding an input face image to a deep learning model,
five facial landmark points are detected and used for 2-D alignment. We have
employed and fine tuned convolutional neural networks (CNNs) that are based on
VGG-16 [24] architecture and pretrained on the IMDB-WIKI dataset [22]. The
outputs of these deep learning models are then combined to produce the final
estimation. Proposed method achieves 0.3668 error in the final ChaLearn LAP
2016 challenge test set [5].
A Cognitive Architecture for the Implementation of Emotions in Computing Systems
Authors: Jordi Vallverdú, Max Talanov, Salvatore Distefano, Manuel Mazzara, Alexander Tchitchigin, Ildar Nurgaliev
Date: 2016-06-09 00:00:00

In this paper we present a new neurobiologically-inspired affective cognitive
architecture: NEUCOGAR (NEUromodulating COGnitive ARchitecture). The objective
of NEUCOGAR is the identification of a mapping from the influence of serotonin,
dopamine and noradrenaline to the computing processes based on Von Neuman's
architecture, in order to implement affective phenomena which can operate on
the Turing's machine model. As basis of the modeling we use and extend the
L\"ovheim Cube of Emotion with parameters of the Von Neumann architecture.
Validation is conducted via simulation on a computing system of dopamine
neuromodulation and its effects on the Cortex. In the experimental phase of the
project, the increase of computing power and storage redistribution due to
emotion stimulus modulated by the dopamine system, confirmed the soundness of
the model.
A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition
Authors: Mostafa Mehdipour Ghazi, Hazim Kemal Ekenel
Date: 2016-06-09 00:00:00
Tasks: Deep Learning, Face Recognition

Deep learning based approaches have been dominating the face recognition
field due to the significant performance improvement they have provided on the
challenging wild datasets. These approaches have been extensively tested on
such unconstrained datasets, on the Labeled Faces in the Wild and YouTube
Faces, to name a few. However, their capability to handle individual appearance
variations caused by factors such as head pose, illumination, occlusion, and
misalignment has not been thoroughly assessed till now. In this paper, we
present a comprehensive study to evaluate the performance of deep learning
based face representation under several conditions including the varying head
pose angles, upper and lower face occlusion, changing illumination of different
strengths, and misalignment due to erroneous facial feature localization. Two
successful and publicly available deep learning models, namely VGG-Face and
Lightened CNN have been utilized to extract face representations. The obtained
results show that although deep learning provides a powerful representation for
face recognition, it can still benefit from preprocessing, for example, for
pose and illumination normalization in order to achieve better performance
under various conditions. Particularly, if these variations are not included in
the dataset used to train the deep learning model, the role of preprocessing
becomes more crucial. Experimental results also show that deep learning based
representation is robust to misalignment and can tolerate facial feature
localization errors up to 10% of the interocular distance.
Understanding User Instructions by Utilizing Open Knowledge for Service Robots
Authors: Dongcai Lu, Feng Wu, Xiaoping Chen
Date: 2016-06-09 00:00:00

Understanding user instructions in natural language is an active research
topic in AI and robotics. Typically, natural user instructions are high-level
and can be reduced into low-level tasks expressed in common verbs (e.g.,
`take', `get', `put'). For robots understanding such instructions, one of the
key challenges is to process high-level user instructions and achieve the
specified tasks with robots' primitive actions. To address this, we propose
novel algorithms by utilizing semantic roles of common verbs defined in
semantic dictionaries and integrating multiple open knowledge to generate task
plans. Specifically, we present a new method for matching and recovering
semantics of user instructions and a novel task planner that exploits
functional knowledge of robot's action model. To verify and evaluate our
approach, we implemented a prototype system using knowledge from several open
resources. Experiments on our system confirmed the correctness and efficiency
of our algorithms. Notably, our system has been deployed in the KeJia robot,
which participated the annual RoboCup@Home competitions in the past three years
and achieved encouragingly high scores in the benchmark tests.
Simultaneous Inpainting and Denoising by Directional Global Three-part Decomposition: Connecting Variational and Fourier Domain Based Image Processing
Authors: Duy Hoang Thai, Carsten Gottschlich
Date: 2016-06-09 00:00:00
Tasks: Denoising, Image Restoration

We consider the very challenging task of restoring images (i) which have a
large number of missing pixels, (ii) whose existing pixels are corrupted by
noise and (iii) the ideal image to be restored contains both cartoon and
texture elements. The combination of these three properties makes this inverse
problem a very difficult one. The solution proposed in this manuscript is based
on directional global three-part decomposition (DG3PD) [ThaiGottschlich2016]
with directional total variation norm, directional G-norm and
$\ell_\infty$-norm in curvelet domain as key ingredients of the model. Image
decomposition by DG3PD enables a decoupled inpainting and denoising of the
cartoon and texture components. A comparison to existing approaches for
inpainting and denoising shows the advantages of the proposed method. Moreover,
we regard the image restoration problem from the viewpoint of a Bayesian
framework and we discuss the connections between the proposed solution by
function space and related image representation by harmonic analysis and
pyramid decomposition.
e-Commerce product classification: our participation at cDiscount 2015 challenge
Authors: Ioannis Partalas, Georgios Balikas
Date: 2016-06-09 00:00:00
Tasks: General Classification, text-classification, Text Classification

This report describes our participation in the cDiscount 2015 challenge where
the goal was to classify product items in a predefined taxonomy of products.
Our best submission yielded an accuracy score of 64.20\% in the private part of
the leaderboard and we were ranked 10th out of 175 participating teams. We
followed a text classification approach employing mainly linear models. The
final solution was a weighted voting system which combined a variety of trained
models.
Decoy Bandits Dueling on a Poset
Authors: Julien Audiffren, Ralaivola Liva
Date: 2016-02-08 00:00:00

We adress the problem of dueling bandits defined on partially ordered sets,
or posets. In this setting, arms may not be comparable, and there may be
several (incomparable) optimal arms. We propose an algorithm, UnchainedBandits,
that efficiently finds the set of optimal arms of any poset even when pairs of
comparable arms cannot be distinguished from pairs of incomparable arms, with a
set of minimal assumptions. This algorithm relies on the concept of decoys,
which stems from social psychology. For the easier case where the
incomparability information may be accessible, we propose a second algorithm,
SlicingBandits, which takes advantage of this information and achieves a very
significant gain of performance compared to UnchainedBandits. We provide
theoretical guarantees and experimental evaluation for both algorithms.
Detecting "Smart" Spammers On Social Network: A Topic Model Approach
Authors: Linqing Liu, Yao Lu, Ye Luo, Renxian Zhang, Laurent Itti, Jianwei Lu
Date: 2016-04-28 00:00:00
Proceeding: HLT 2016 6
Tasks: General Classification

Spammer detection on social network is a challenging problem. The rigid
anti-spam rules have resulted in emergence of "smart" spammers. They resemble
legitimate users who are difficult to identify. In this paper, we present a
novel spammer classification approach based on Latent Dirichlet
Allocation(LDA), a topic model. Our approach extracts both the local and the
global information of topic distribution patterns, which capture the essence of
spamming. Tested on one benchmark dataset and one self-collected dataset, our
proposed method outperforms other state-of-the-art methods in terms of averaged
F1-score.
Variational Information Maximization for Feature Selection
Authors: Shuyang Gao, Greg Ver Steeg, Aram Galstyan
Date: 2016-06-09 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: feature selection

Feature selection is one of the most fundamental problems in machine
learning. An extensive body of work on information-theoretic feature selection
exists which is based on maximizing mutual information between subsets of
features and class labels. Practical methods are forced to rely on
approximations due to the difficulty of estimating mutual information. We
demonstrate that approximations made by existing methods are based on
unrealistic assumptions. We formulate a more flexible and general class of
assumptions based on variational distributions and use them to tractably
generate lower bounds for mutual information. These bounds define a novel
information-theoretic framework for feature selection, which we prove to be
optimal under tree graphical models with proper choice of variational
distributions. Our experiments demonstrate that the proposed method strongly
outperforms existing information-theoretic feature selection approaches.
Adaptive Normalized Risk-Averting Training For Deep Neural Networks
Authors: Zhiguang Wang, Tim Oates, James Lo
Date: 2015-06-08 00:00:00

This paper proposes a set of new error criteria and learning approaches,
Adaptive Normalized Risk-Averting Training (ANRAT), to attack the non-convex
optimization problem in training deep neural networks (DNNs). Theoretically, we
demonstrate its effectiveness on global and local convexity lower-bounded by
the standard $L_p$-norm error. By analyzing the gradient on the convexity index
$\lambda$, we explain the reason why to learn $\lambda$ adaptively using
gradient descent works. In practice, we show how this method improves training
of deep neural networks to solve visual recognition tasks on the MNIST and
CIFAR-10 datasets. Without using pretraining or other tricks, we obtain results
comparable or superior to those reported in recent literature on the same tasks
using standard ConvNets + MSE/cross entropy. Performance on deep/shallow
multilayer perceptrons and Denoised Auto-encoders is also explored. ANRAT can
be combined with other quasi-Newton training methods, innovative network
variants, regularization techniques and other specific tricks in DNNs. Other
than unsupervised pretraining, it provides a new perspective to address the
non-convex optimization problem in DNNs.
Machine Learning Techniques and Applications For Ground-based Image Analysis
Authors: Soumyabrata Dev, Bihan Wen, Yee Hui Lee, Stefan Winkler
Date: 2016-06-09 00:00:00
Tasks: BIG-bench Machine Learning, Denoising

Ground-based whole sky cameras have opened up new opportunities for
monitoring the earth's atmosphere. These cameras are an important complement to
satellite images by providing geoscientists with cheaper, faster, and more
localized data. The images captured by whole sky imagers can have high spatial
and temporal resolution, which is an important pre-requisite for applications
such as solar energy modeling, cloud attenuation analysis, local weather
prediction, etc.
  Extracting valuable information from the huge amount of image data by
detecting and analyzing the various entities in these images is challenging.
However, powerful machine learning techniques have become available to aid with
the image analysis. This article provides a detailed walk-through of recent
developments in these techniques and their applications in ground-based
imaging. We aim to bridge the gap between computer vision and remote sensing
with the help of illustrative examples. We demonstrate the advantages of using
machine learning techniques in ground-based image analysis via three primary
applications -- segmentation, classification, and denoising.
Face valuing: Training user interfaces with facial expressions and reinforcement learning
Authors: Vivek Veeriah, Patrick M. Pilarski, Richard S. Sutton
Date: 2016-06-09 00:00:00
Tasks: BIG-bench Machine Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

An important application of interactive machine learning is extending or
amplifying the cognitive and physical capabilities of a human. To accomplish
this, machines need to learn about their human users' intentions and adapt to
their preferences. In most current research, a user has conveyed preferences to
a machine using explicit corrective or instructive feedback; explicit feedback
imposes a cognitive load on the user and is expensive in terms of human effort.
The primary objective of the current work is to demonstrate that a learning
agent can reduce the amount of explicit feedback required for adapting to the
user's preferences pertaining to a task by learning to perceive a value of its
behavior from the human user, particularly from the user's facial
expressions---we call this face valuing. We empirically evaluate face valuing
on a grip selection task. Our preliminary results suggest that an agent can
quickly adapt to a user's changing preferences with minimal explicit feedback
by learning a value function that maps facial features extracted from a camera
image to expected future reward. We believe that an agent learning to perceive
a value from the body language of its human user is complementary to existing
interactive machine learning approaches and will help in creating successful
human-machine interactive applications.
Generating Natural Questions About an Image
Authors: Nasrin Mostafazadeh, Ishan Misra, Jacob Devlin, Margaret Mitchell, Xiaodong He, Lucy Vanderwende
Date: 2016-03-19 00:00:00
Proceeding: ACL 2016 8
Tasks: Image Captioning, Natural Questions, Question Generation, Question-Generation, Retrieval

There has been an explosion of work in the vision & language community during
the past few years from image captioning to video transcription, and answering
questions about images. These tasks have focused on literal descriptions of the
image. To move beyond the literal, we choose to explore how questions about an
image are often directed at commonsense inference and the abstract events
evoked by objects in the image. In this paper, we introduce the novel task of
Visual Question Generation (VQG), where the system is tasked with asking a
natural and engaging question when shown an image. We provide three datasets
which cover a variety of images from object-centric to event-centric, with
considerably more abstract training data than provided to state-of-the-art
captioning systems thus far. We train and test several generative and retrieval
models to tackle the task of VQG. Evaluation results show that while such
models ask reasonable questions for a variety of images, there is still a wide
gap with human performance which motivates further work on connecting images
with commonsense knowledge and pragmatics. Our proposed task offers a new
challenge to the community which we hope furthers interest in exploring deeper
connections between vision & language.
Spontaneous Subtle Expression Detection and Recognition based on Facial Strain
Authors: Sze-Teng Liong, John See, Raphael Chung-Wei Phan, Yee-Hui Oh, Anh Cat Le Ngo, KokSheik Wong, Su-Wei Tan
Date: 2016-06-09 00:00:00
Tasks: Optical Flow Estimation

Optical strain is an extension of optical flow that is capable of quantifying
subtle changes on faces and representing the minute facial motion intensities
at the pixel level. This is computationally essential for the relatively new
field of spontaneous micro-expression, where subtle expressions can be
technically challenging to pinpoint. In this paper, we present a novel method
for detecting and recognizing micro-expressions by utilizing facial optical
strain magnitudes to construct optical strain features and optical strain
weighted features. The two sets of features are then concatenated to form the
resultant feature histogram. Experiments were performed on the CASME II and
SMIC databases. We demonstrate on both databases, the usefulness of optical
strain information and more importantly, that our best approaches are able to
outperform the original baseline results for both detection and recognition
tasks. A comparison of the proposed method with other existing spatio-temporal
feature extraction approaches is also presented.
Neural Network-Based Abstract Generation for Opinions and Arguments
Authors: Lu Wang, Wang Ling
Date: 2016-06-09 00:00:00
Proceeding: NAACL 2016 6
Tasks: Abstract generation, Extractive Summarization

We study the problem of generating abstractive summaries for opinionated
text. We propose an attention-based neural network model that is able to absorb
information from multiple text units to construct informative, concise, and
fluent summaries. An importance-based sampling method is designed to allow the
encoder to integrate information from an important subset of input. Automatic
evaluation indicates that our system outperforms state-of-the-art abstractive
and extractive summarization systems on two newly collected datasets of movie
reviews and arguments. Our system summaries are also rated as more informative
and grammatical in human evaluation.
The Information Sieve
Authors: Greg Ver Steeg, Aram Galstyan
Date: 2015-07-08 00:00:00
Tasks: Missing Values

We introduce a new framework for unsupervised learning of representations
based on a novel hierarchical decomposition of information. Intuitively, data
is passed through a series of progressively fine-grained sieves. Each layer of
the sieve recovers a single latent factor that is maximally informative about
multivariate dependence in the data. The data is transformed after each pass so
that the remaining unexplained information trickles down to the next layer.
Ultimately, we are left with a set of latent factors explaining all the
dependence in the original data and remainder information consisting of
independent noise. We present a practical implementation of this framework for
discrete variables and apply it to a variety of fundamental tasks in
unsupervised learning including independent component analysis, lossy and
lossless compression, and predicting missing values in data.