pwc-archive/papers-with-abstracts huggingface dataset:
A General Family of Trimmed Estimators for Robust High-dimensional Data Analysis
Authors: Eunho Yang, Aurelie Lozano, Aleksandr Aravkin
Date: 2016-05-26 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We consider the problem of robustifying high-dimensional structured
estimation. Robust techniques are key in real-world applications which often
involve outliers and data corruption. We focus on trimmed versions of
structurally regularized M-estimators in the high-dimensional setting,
including the popular Least Trimmed Squares estimator, as well as analogous
estimators for generalized linear models and graphical models, using possibly
non-convex loss functions. We present a general analysis of their statistical
convergence rates and consistency, and then take a closer look at the trimmed
versions of the Lasso and Graphical Lasso estimators as special cases. On the
optimization side, we show how to extend algorithms for M-estimators to fit
trimmed variants and provide guarantees on their numerical convergence. The
generality and competitive performance of high-dimensional trimmed estimators
are illustrated numerically on both simulated and real-world genomics data.
Position-based Content Attention for Time Series Forecasting with Sequence-to-sequence RNNs
Authors: Yagmur G. Cinar, Hamid Mirisaee, Parantapa Goswami, Eric Gaussier, Ali Ait-Bachir, Vadim Strijov
Date: 2017-03-29 00:00:00
Tasks: Position, Time Series, Time Series Analysis, Time Series Forecasting

We propose here an extended attention model for sequence-to-sequence
recurrent neural networks (RNNs) designed to capture (pseudo-)periods in time
series. This extended attention model can be deployed on top of any RNN and is
shown to yield state-of-the-art performance for time series forecasting on
several univariate and multivariate time series.
Seernet at EmoInt-2017: Tweet Emotion Intensity Estimator
Authors: Venkatesh Duppada, Sushant Hiray
Date: 2017-08-21 00:00:00
Proceeding: WS 2017 9

The paper describes experiments on estimating emotion intensity in tweets
using a generalized regressor system. The system combines lexical, syntactic
and pre-trained word embedding features, trains them on general regressors and
finally combines the best performing models to create an ensemble. The proposed
system stood 3rd out of 22 systems in the leaderboard of WASSA-2017 Shared Task
on Emotion Intensity.
Evasion Attacks against Machine Learning at Test Time
Authors: Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Srndic, Pavel Laskov, Giorgio Giacinto, Fabio Roli
Date: 2017-08-21 00:00:00
Tasks: BIG-bench Machine Learning, Malware Detection, Model Selection

In security-sensitive applications, the success of machine learning depends
on a thorough vetting of their resistance to adversarial data. In one
pertinent, well-motivated attack scenario, an adversary may attempt to evade a
deployed system at test time by carefully manipulating attack samples. In this
work, we present a simple but effective gradient-based approach that can be
exploited to systematically assess the security of several, widely-used
classification algorithms against evasion attacks. Following a recently
proposed framework for security evaluation, we simulate attack scenarios that
exhibit different risk levels for the classifier by increasing the attacker's
knowledge of the system and her ability to manipulate attack samples. This
gives the classifier designer a better picture of the classifier performance
under evasion attacks, and allows him to perform a more informed model
selection (or parameter setting). We evaluate our approach on the relevant
security task of malware detection in PDF files, and show that such systems can
be easily evaded. We also sketch some countermeasures suggested by our
analysis.
Channel Pruning for Accelerating Very Deep Neural Networks
Authors: Yihui He, Xiangyu Zhang, Jian Sun
Date: 2017-07-19 00:00:00
Proceeding: ICCV 2017 10
Tasks: channel selection, regression

In this paper, we introduce a new channel pruning method to accelerate very
deep convolutional neural networks.Given a trained CNN model, we propose an
iterative two-step algorithm to effectively prune each layer, by a LASSO
regression based channel selection and least square reconstruction. We further
generalize this algorithm to multi-layer and multi-branch cases. Our method
reduces the accumulated error and enhance the compatibility with various
architectures. Our pruned VGG-16 achieves the state-of-the-art results by 5x
speed-up along with only 0.3% increase of error. More importantly, our method
is able to accelerate modern networks like ResNet, Xception and suffers only
1.4%, 1.0% accuracy loss under 2x speed-up respectively, which is significant.
Code has been made publicly available.
e-Counterfeit: a mobile-server platform for document counterfeit detection
Authors: Albert Berenguel, Oriol Ramos Terrades, Josep Lladós, Cristina Cañero
Date: 2017-08-21 00:00:00
Tasks: Texture Classification

This paper presents a novel application to detect counterfeit identity
documents forged by a scan-printing operation. Texture analysis approaches are
proposed to extract validation features from security background that is
usually printed in documents as IDs or banknotes. The main contribution of this
work is the end-to-end mobile-server architecture, which provides a service for
non-expert users and therefore can be used in several scenarios. The system
also provides a crowdsourcing mode so labeled images can be gathered,
generating databases for incremental training of the algorithms.
Distantly Supervised Road Segmentation
Authors: Satoshi Tsutsui, Tommi Kerola, Shunta Saito
Date: 2017-08-21 00:00:00
Tasks: Road Segmentation, Segmentation

We present an approach for road segmentation that only requires image-level
annotations at training time. We leverage distant supervision, which allows us
to train our model using images that are different from the target domain.
Using large publicly available image databases as distant supervisors, we
develop a simple method to automatically generate weak pixel-wise road masks.
These are used to iteratively train a fully convolutional neural network, which
produces our final segmentation model. We evaluate our method on the Cityscapes
dataset, where we compare it with a fully supervised approach. Further, we
discuss the trade-off between annotation cost and performance. Overall, our
distantly supervised approach achieves 93.8% of the performance of the fully
supervised approach, while using orders of magnitude less annotation work.
Supervised Infinite Feature Selection
Authors: Sadegh Eskandari, Emre Akbas
Date: 2017-04-09 00:00:00
Tasks: feature selection, General Classification, image-classification, Image Classification

In this paper, we present a new feature selection method that is suitable for
both unsupervised and supervised problems. We build upon the recently proposed
Infinite Feature Selection (IFS) method where feature subsets of all sizes
(including infinity) are considered. We extend IFS in two ways. First, we
propose a supervised version of it. Second, we propose new ways of forming the
feature adjacency matrix that perform better for unsupervised problems. We
extensively evaluate our methods on many benchmark datasets, including large
image-classification datasets (PASCAL VOC), and show that our methods
outperform both the IFS and the widely used "minimum-redundancy
maximum-relevancy (mRMR)" feature selection algorithm.
What does a convolutional neural network recognize in the moon?
Authors: Daigo Shoji
Date: 2017-08-18 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Many people see a human face or animals in the pattern of the maria on the
moon. Although the pattern corresponds to the actual variation in composition
of the lunar surface, the culture and environment of each society influence the
recognition of these objects (i.e., symbols) as specific entities. In contrast,
a convolutional neural network (CNN) recognizes objects from characteristic
shapes in a training data set. Using CNN, this study evaluates the
probabilities of the pattern of lunar maria categorized into the shape of a
crab, a lion and a hare. If Mare Frigoris (a dark band on the moon) is included
in the lunar image, the lion is recognized. However, in an image without Mare
Frigoris, the hare has the highest probability of recognition. Thus, the
recognition of objects similar to the lunar pattern depends on which part of
the lunar maria is taken into account. In human recognition, before we find
similarities between the lunar maria and objects such as animals, we may be
persuaded in advance to see a particular image from our culture and environment
and then adjust the lunar pattern to the shape of the imagined object.
ExSIS: Extended Sure Independence Screening for Ultrahigh-dimensional Linear Models
Authors: Talal Ahmed, Waheed U. Bajwa
Date: 2017-08-21 00:00:00

Statistical inference can be computationally prohibitive in ultrahigh-dimensional linear models. Correlation-based variable screening, in which one leverages marginal correlations for removal of irrelevant variables from the model prior to statistical inference, can be used to overcome this challenge. Prior works on correlation-based variable screening either impose statistical priors on the linear model or assume specific post-screening inference methods. This paper first extends the analysis of correlation-based variable screening to arbitrary linear models and post-screening inference techniques. In particular, (i) it shows that a condition---termed the screening condition---is sufficient for successful correlation-based screening of linear models, and (ii) it provides insights into the dependence of marginal correlation-based screening on different problem parameters. Numerical experiments confirm that these insights are not mere artifacts of analysis; rather, they are reflective of the challenges associated with marginal correlation-based variable screening. Second, the paper explicitly derives the screening condition for arbitrary (random or deterministic) linear models and, in the process, it establishes that---under appropriate conditions---it is possible to reduce the dimension of an ultrahigh-dimensional, arbitrary linear model to almost the sample size even when the number of active variables scales almost linearly with the sample size. Third, it specializes the screening condition to sub-Gaussian linear models and contrasts the final results to those existing in the literature. This specialization formally validates the claim that the main result of this paper generalizes existing ones on correlation-based screening.
Scientific Information Extraction with Semi-supervised Neural Tagging
Authors: Yi Luan, Mari Ostendorf, Hannaneh Hajishirzi
Date: 2017-08-21 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Articles, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This paper addresses the problem of extracting keyphrases from scientific
articles and categorizing them as corresponding to a task, process, or
material. We cast the problem as sequence tagging and introduce semi-supervised
methods to a neural tagging model, which builds on recent advances in named
entity recognition. Since annotated training data is scarce in this domain, we
introduce a graph-based semi-supervised algorithm together with a data
selection scheme to leverage unannotated articles. Both inductive and
transductive semi-supervised learning strategies outperform state-of-the-art
information extraction performance on the 2017 SemEval Task 10 ScienceIE task.
Vector Space Model as Cognitive Space for Text Classification
Authors: Barathi Ganesh HB, Anand Kumar M, Soman Kp
Date: 2017-08-21 00:00:00
Tasks: Author Profiling, Classification, Gender Prediction, General Classification, Language Identification, Native Language Identification, Recommendation Systems, text-classification, Text Classification

In this era of digitization, knowing the user's sociolect aspects have become
essential features to build the user specific recommendation systems. These
sociolect aspects could be found by mining the user's language sharing in the
form of text in social media and reviews. This paper describes about the
experiment that was performed in PAN Author Profiling 2017 shared task. The
objective of the task is to find the sociolect aspects of the users from their
tweets. The sociolect aspects considered in this experiment are user's gender
and native language information. Here user's tweets written in a different
language from their native language are represented as Document - Term Matrix
with document frequency as the constraint. Further classification is done using
the Support Vector Machine by taking gender and native language as target
classes. This experiment attains the average accuracy of 73.42% in gender
prediction and 76.26% in the native language identification task.
Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs
Authors: Sudip Mittal, Anupam Joshi, Tim Finin
Date: 2017-08-10 00:00:00
Tasks: Knowledge Graphs

Knowledge graphs and vector space models are robust knowledge representation
techniques with individual strengths and weaknesses. Vector space models excel
at determining similarity between concepts, but are severely constrained when
evaluating complex dependency relations and other logic-based operations that
are a strength of knowledge graphs. We describe the VKG structure that helps
unify knowledge graphs and vector representation of entities, and enables
powerful inference methods and search capabilities that combine their
complementary strengths. We analogize this to thinking `fast' in vector space
along with thinking 'slow' and `deeply' by reasoning over the knowledge graph.
We have created a query processing engine that takes complex queries and
decomposes them into subqueries optimized to run on the respective knowledge
graph or vector view of a VKG. We show that the VKG structure can process
specific queries that are not efficiently handled by vector spaces or knowledge
graphs alone. We also demonstrate and evaluate the VKG structure and the query
processing engine by developing a system called Cyber-All-Intel for knowledge
extraction, representation and querying in an end-to-end pipeline grounded in
the cybersecurity informatics domain.
Stochastic Gradient Descent for Relational Logistic Regression via Partial Network Crawls
Authors: Jiasen Yang, Bruno Ribeiro, Jennifer Neville
Date: 2017-07-24 00:00:00
Tasks: regression, Relational Reasoning

Research in statistical relational learning has produced a number of methods
for learning relational models from large-scale network data. While these
methods have been successfully applied in various domains, they have been
developed under the unrealistic assumption of full data access. In practice,
however, the data are often collected by crawling the network, due to
proprietary access, limited resources, and privacy concerns. Recently, we
showed that the parameter estimates for relational Bayes classifiers computed
from network samples collected by existing network crawlers can be quite
inaccurate, and developed a crawl-aware estimation method for such models
(Yang, Ribeiro, and Neville, 2017). In this work, we extend the methodology to
learning relational logistic regression models via stochastic gradient descent
from partial network crawls, and show that the proposed method yields accurate
parameter estimates and confidence intervals.
More cat than cute? Interpretable Prediction of Adjective-Noun Pairs
Authors: Delia Fernandez, Alejandro Woodward, Victor Campos, Xavier Giro-i-Nieto, Brendan Jou, Shih-Fu Chang
Date: 2017-08-21 00:00:00
Tasks: Prediction

The increasing availability of affect-rich multimedia resources has bolstered
interest in understanding sentiment and emotions in and from visual content.
Adjective-noun pairs (ANP) are a popular mid-level semantic construct for
capturing affect via visually detectable concepts such as "cute dog" or
"beautiful landscape". Current state-of-the-art methods approach ANP prediction
by considering each of these compound concepts as individual tokens, ignoring
the underlying relationships in ANPs. This work aims at disentangling the
contributions of the `adjectives' and `nouns' in the visual prediction of ANPs.
Two specialised classifiers, one trained for detecting adjectives and another
for nouns, are fused to predict 553 different ANPs. The resulting ANP
prediction model is more interpretable as it allows us to study contributions
of the adjective and noun components. Source code and models are available at
https://imatge-upc.github.io/affective-2017-musa2/ .
Outcome-Oriented Predictive Process Monitoring: Review and Benchmark
Authors: Irene Teinemaa, Marlon Dumas, Marcello La Rosa, Fabrizio Maria Maggi
Date: 2017-07-21 00:00:00
Tasks: Predictive Process Monitoring

Predictive business process monitoring refers to the act of making
predictions about the future state of ongoing cases of a business process,
based on their incomplete execution traces and logs of historical (completed)
traces. Motivated by the increasingly pervasive availability of fine-grained
event data about business process executions, the problem of predictive process
monitoring has received substantial attention in the past years. In particular,
a considerable number of methods have been put forward to address the problem
of outcome-oriented predictive process monitoring, which refers to classifying
each ongoing case of a process according to a given set of possible categorical
outcomes - e.g., Will the customer complain or not? Will an order be delivered,
canceled or withdrawn? Unfortunately, different authors have used different
datasets, experimental settings, evaluation measures and baselines to assess
their proposals, resulting in poor comparability and an unclear picture of the
relative merits and applicability of different methods. To address this gap,
this article presents a systematic review and taxonomy of outcome-oriented
predictive process monitoring methods, and a comparative experimental
evaluation of eleven representative methods using a benchmark covering 24
predictive process monitoring tasks based on nine real-life event logs.
Image-based localization using LSTMs for structured feature correlation
Authors: Florian Walch, Caner Hazirbas, Laura Leal-Taixé, Torsten Sattler, Sebastian Hilsenbeck, Daniel Cremers
Date: 2016-11-23 00:00:00
Proceeding: ICCV 2017 10
Tasks: Dimensionality Reduction, Feature Correlation, Image-Based Localization

In this work we propose a new CNN+LSTM architecture for camera pose
regression for indoor and outdoor scenes. CNNs allow us to learn suitable
feature representations for localization that are robust against motion blur
and illumination changes. We make use of LSTM units on the CNN output, which
play the role of a structured dimensionality reduction on the feature vector,
leading to drastic improvements in localization performance. We provide
extensive quantitative comparison of CNN-based and SIFT-based localization
methods, showing the weaknesses and strengths of each. Furthermore, we present
a new large-scale indoor dataset with accurate ground truth from a laser
scanner. Experimental results on both indoor and outdoor public datasets show
our method outperforms existing deep architectures, and can localize images in
hard conditions, e.g., in the presence of mostly textureless surfaces, where
classic SIFT-based methods fail.
DeepBreath: Deep Learning of Breathing Patterns for Automatic Stress Recognition using Low-Cost Thermal Imaging in Unconstrained Settings
Authors: Youngjun Cho, Nadia Bianchi-Berthouze, Simon J. Julier
Date: 2017-08-20 00:00:00
Tasks: Data Augmentation, Mental Stress Detection, Physiological Computing

We propose DeepBreath, a deep learning model which automatically recognises
people's psychological stress level (mental overload) from their breathing
patterns. Using a low cost thermal camera, we track a person's breathing
patterns as temperature changes around his/her nostril. The paper's technical
contribution is threefold. First of all, instead of creating hand-crafted
features to capture aspects of the breathing patterns, we transform the
uni-dimensional breathing signals into two dimensional respiration variability
spectrogram (RVS) sequences. The spectrograms easily capture the complexity of
the breathing dynamics. Second, a spatial pattern analysis based on a deep
Convolutional Neural Network (CNN) is directly applied to the spectrogram
sequences without the need of hand-crafting features. Finally, a data
augmentation technique, inspired from solutions for over-fitting problems in
deep learning, is applied to allow the CNN to learn with a small-scale dataset
from short-term measurements (e.g., up to a few hours). The model is trained
and tested with data collected from people exposed to two types of cognitive
tasks (Stroop Colour Word Test, Mental Computation test) with sessions of
different difficulty levels. Using normalised self-report as ground truth, the
CNN reaches 84.59% accuracy in discriminating between two levels of stress and
56.52% in discriminating between three levels. In addition, the CNN
outperformed powerful shallow learning methods based on a single layer neural
network. Finally, the dataset of labelled thermal images will be open to the
community.
Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks
Authors: Nathan Hartmann, Erick Fonseca, Christopher Shulby, Marcos Treviso, Jessica Rodrigues, Sandra Aluisio
Date: 2017-08-20 00:00:00
Proceeding: WS 2017 10
Tasks: POS, POS Tagging, Semantic Similarity, Semantic Textual Similarity, Sentence, Word Embeddings

Word embeddings have been found to provide meaningful representations for
words in an efficient way; therefore, they have become common in Natural
Language Processing sys- tems. In this paper, we evaluated different word
embedding models trained on a large Portuguese corpus, including both Brazilian
and European variants. We trained 31 word embedding models using FastText,
GloVe, Wang2Vec and Word2Vec. We evaluated them intrinsically on syntactic and
semantic analogies and extrinsically on POS tagging and sentence semantic
similarity tasks. The obtained results suggest that word analogies are not
appropriate for word embedding evaluation; task-specific evaluations appear to
be a better option.
Joint Multi-view Face Alignment in the Wild
Authors: Jiankang Deng, George Trigeorgis, Yuxiang Zhou, Stefanos Zafeiriou
Date: 2017-08-20 00:00:00
Tasks: Face Alignment, Face Detection

The de facto algorithm for facial landmark estimation involves running a face
detector with a subsequent deformable model fitting on the bounding box. This
encompasses two basic problems: i) the detection and deformable fitting steps
are performed independently, while the detector might not provide best-suited
initialisation for the fitting step, ii) the face appearance varies hugely
across different poses, which makes the deformable face fitting very
challenging and thus distinct models have to be used (\eg, one for profile and
one for frontal faces). In this work, we propose the first, to the best of our
knowledge, joint multi-view convolutional network to handle large pose
variations across faces in-the-wild, and elegantly bridge face detection and
facial landmark localisation tasks. Existing joint face detection and landmark
localisation methods focus only on a very small set of landmarks. By contrast,
our method can detect and align a large number of landmarks for semi-frontal
(68 landmarks) and profile (39 landmarks) faces. We evaluate our model on a
plethora of datasets including standard static image datasets such as IBUG,
300W, COFW, and the latest Menpo Benchmark for both semi-frontal and profile
faces. Significant improvement over state-of-the-art methods on deformable face
tracking is witnessed on 300VW benchmark. We also demonstrate state-of-the-art
results for face detection on FDDB and MALF datasets.