pwc-archive/papers-with-abstracts huggingface dataset:
SEE: Towards Semi-Supervised End-to-End Scene Text Recognition
Authors: Christian Bartz, Haojin Yang, Christoph Meinel
Date: 2017-12-14 00:00:00
Tasks: Scene Text Detection, Scene Text Recognition, Text Detection

Detecting and recognizing text in natural scene images is a challenging, yet
not completely solved task. In recent years several new systems that try to
solve at least one of the two sub-tasks (text detection and text recognition)
have been proposed. In this paper we present SEE, a step towards
semi-supervised neural networks for scene text detection and recognition, that
can be optimized end-to-end. Most existing works consist of multiple deep
neural networks and several pre-processing steps. In contrast to this, we
propose to use a single deep neural network, that learns to detect and
recognize text from natural images, in a semi-supervised way. SEE is a network
that integrates and jointly learns a spatial transformer network, which can
learn to detect text regions in an image, and a text recognition network that
takes the identified text regions and recognizes their textual content. We
introduce the idea behind our novel approach and show its feasibility, by
performing a range of experiments on standard benchmark datasets, where we
achieve competitive results.
Learning to Attend via Word-Aspect Associative Fusion for Aspect-based Sentiment Analysis
Authors: Yi Tay, Anh Tuan Luu, Siu Cheung Hui
Date: 2017-12-14 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentence

Aspect-based sentiment analysis (ABSA) tries to predict the polarity of a
given document with respect to a given aspect entity. While neural network
architectures have been successful in predicting the overall polarity of
sentences, aspect-specific sentiment analysis still remains as an open problem.
In this paper, we propose a novel method for integrating aspect information
into the neural model. More specifically, we incorporate aspect information
into the neural model by modeling word-aspect relationships. Our novel model,
\textit{Aspect Fusion LSTM} (AF-LSTM) learns to attend based on associative
relationships between sentence words and aspect which allows our model to
adaptively focus on the correct words given an aspect term. This ameliorates
the flaws of other state-of-the-art models that utilize naive concatenations to
model word-aspect similarity. Instead, our model adopts circular convolution
and circular correlation to model the similarity between aspect and words and
elegantly incorporates this within a differentiable neural attention framework.
Finally, our model is end-to-end differentiable and highly related to
convolution-correlation (holographic like) memories. Our proposed neural model
achieves state-of-the-art performance on benchmark datasets, outperforming
ATAE-LSTM by $4\%-5\%$ on average across multiple datasets.
Semi-Automatic Algorithm for Breast MRI Lesion Segmentation Using Marker-Controlled Watershed Transformation
Authors: Sulaiman Vesal, Andres Diaz-Pinto, Nishant Ravikumar, Stephan Ellmann, Amirabbas Davari, Andreas Maier
Date: 2017-12-14 00:00:00
Tasks: Lesion Segmentation, Segmentation

Magnetic resonance imaging (MRI) is an effective imaging modality for
identifying and localizing breast lesions in women. Accurate and precise lesion
segmentation using a computer-aided-diagnosis (CAD) system, is a crucial step
in evaluating tumor volume and in the quantification of tumor characteristics.
However, this is a challenging task, since breast lesions have sophisticated
shape, topological structure, and high variance in their intensity distribution
across patients. In this paper, we propose a novel marker-controlled watershed
transformation-based approach, which uses the brightest pixels in a region of
interest (determined by experts) as markers to overcome this challenge, and
accurately segment lesions in breast MRI. The proposed approach was evaluated
on 106 lesions, which includes 64 malignant and 42 benign cases. Segmentation
results were quantified by comparison with ground truth labels, using the Dice
similarity coefficient (DSC) and Jaccard index (JI) metrics. The proposed
method achieved an average Dice coefficient of 0.7808$\pm$0.1729 and Jaccard
index of 0.6704$\pm$0.2167. These results illustrate that the proposed method
shows promise for future work related to the segmentation and classification of
benign and malignant breast lesions.
Rate of Change Analysis for Interestingness Measures
Authors: Nandan Sudarsanam, Nishanth Kumar, Abhishek Sharma, Balaraman Ravindran
Date: 2017-12-14 00:00:00
Tasks: General Classification

The use of Association Rule Mining techniques in diverse contexts and domains
has resulted in the creation of numerous interestingness measures. This, in
turn, has motivated researchers to come up with various classification schemes
for these measures. One popular approach to classify the objective measures is
to assess the set of mathematical properties they satisfy in order to help
practitioners select the right measure for a given problem. In this research,
we discuss the insufficiency of the existing properties in literature to
capture certain behaviors of interestingness measures. This motivates us to
present a novel approach to analyze and classify measures. We refer to this as
a rate of change analysis (RCA). In this analysis a measure is described by how
it varies if there is a unit change in the frequency count
$(f_{11},f_{10},f_{01},f_{00})$, for different pre-existing states of the
frequency counts. More formally, we look at the first partial derivative of the
measure with respect to the various frequency count variables. We then use this
analysis to define two new properties, Unit-Null Asymptotic Invariance (UNAI)
and Unit-Null Zero Rate (UNZR). UNAI looks at the asymptotic effect of adding
frequency patterns, while UNZR looks at the initial effect of adding frequency
patterns when they do not pre-exist in the dataset. We present a comprehensive
analysis of 50 interestingness measures and classify them in accordance with
the two properties. We also present empirical studies, involving both synthetic
and real-world datasets, which are used to cluster various measures according
to the rule ranking patterns of the measures. The study concludes with the
observation that classification of measures using the empirical clusters share
significant similarities to the classification of measures done through the
properties presented in this research.
Relation Extraction : A Survey
Authors: Sachin Pawar, Girish K. Palshikar, Pushpak Bhattacharyya
Date: 2017-12-14 00:00:00
Tasks: Articles, Information Retrieval, Management, Open Information Extraction, Question Answering, Relation, Relation Extraction, Retrieval, Sentence, Survey

With the advent of the Internet, large amount of digital text is generated
everyday in the form of news articles, research publications, blogs, question
answering forums and social media. It is important to develop techniques for
extracting information automatically from these documents, as lot of important
information is hidden within them. This extracted information can be used to
improve access and management of knowledge hidden in large text corpora.
Several applications such as Question Answering, Information Retrieval would
benefit from this information. Entities like persons and organizations, form
the most basic unit of the information. Occurrences of entities in a sentence
are often linked through well-defined relations; e.g., occurrences of person
and organization in a sentence may be linked through relations such as employed
at. The task of Relation Extraction (RE) is to identify such relations
automatically. In this paper, we survey several important supervised,
semi-supervised and unsupervised RE techniques. We also cover the paradigms of
Open Information Extraction (OIE) and Distant Supervision. Finally, we describe
some of the recent trends in the RE techniques and possible future research
directions. This survey would be useful for three kinds of readers - i)
Newcomers in the field who want to quickly learn about RE; ii) Researchers who
want to know how the various RE techniques evolved over time and what are
possible future research directions and iii) Practitioners who just need to
know which RE technique works best in various settings.
Differentiable lower bound for expected BLEU score
Authors: Vlad Zhukov, Eugene Golikov, Maksim Kretov
Date: 2017-12-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In natural language processing tasks performance of the models is often
measured with some non-differentiable metric, such as BLEU score. To use
efficient gradient-based methods for optimization, it is a common workaround to
optimize some surrogate loss function. This approach is effective if
optimization of such loss also results in improving target metric. The
corresponding problem is referred to as loss-evaluation mismatch. In the
present work we propose a method for calculation of differentiable lower bound
of expected BLEU score that does not involve computationally expensive sampling
procedure such as the one required when using REINFORCE rule from reinforcement
learning (RL) framework.
Basic Filters for Convolutional Neural Networks Applied to Music: Training or Design?
Authors: Monika Doerfler, Thomas Grill, Roswitha Bammer, Arthur Flexer
Date: 2017-09-07 00:00:00
Tasks: Time Series, Time Series Analysis

When convolutional neural networks are used to tackle learning problems based
on music or, more generally, time series data, raw one-dimensional data are
commonly pre-processed to obtain spectrogram or mel-spectrogram coefficients,
which are then used as input to the actual neural network. In this
contribution, we investigate, both theoretically and experimentally, the
influence of this pre-processing step on the network's performance and pose the
question, whether replacing it by applying adaptive or learned filters directly
to the raw data, can improve learning success. The theoretical results show
that approximately reproducing mel-spectrogram coefficients by applying
adaptive filters and subsequent time-averaging is in principle possible. We
also conducted extensive experimental work on the task of singing voice
detection in music. The results of these experiments show that for
classification based on Convolutional Neural Networks the features obtained
from adaptive filter banks followed by time-averaging perform better than the
canonical Fourier-transform-based mel-spectrogram coefficients. Alternative
adaptive approaches with center frequencies or time-averaging lengths learned
from training data perform equally well.
Adaptive regularization for Lasso models in the context of non-stationary data streams
Authors: Ricardo Pio Monti, Christoforos Anagnostopoulos, Giovanni Montana
Date: 2016-10-28 00:00:00

Large scale, streaming datasets are ubiquitous in modern machine learning.
Streaming algorithms must be scalable, amenable to incremental training and
robust to the presence of non-stationarity. In this work consider the problem
of learning $\ell_1$ regularized linear models in the context of streaming
data. In particular, the focus of this work revolves around how to select the
regularization parameter when data arrives sequentially and the underlying
distribution is non-stationary (implying the choice of optimal regularization
parameter is itself time-varying). We propose a framework through which to
infer an adaptive regularization parameter. Our approach employs an $\ell_1$
penalty constraint where the corresponding sparsity parameter is iteratively
updated via stochastic gradient descent. This serves to reformulate the choice
of regularization parameter in a principled framework for online learning. The
proposed method is derived for linear regression and subsequently extended to
generalized linear models. We validate our approach using simulated and real
datasets and present an application to a neuroimaging dataset.
A review of Gaussian Markov models for conditional independence
Authors: Irene Córdoba, Concha Bielza, Pedro Larrañaga
Date: 2016-06-23 00:00:00
Tasks: Model Selection, Two-sample testing

Markov models lie at the interface between statistical independence in a probability distribution and graph separation properties. We review model selection and estimation in directed and undirected Markov models with Gaussian parametrization, emphasizing the main similarities and differences. These two model classes are similar but not equivalent, although they share a common intersection. We present the existing results from a historical perspective, taking into account the amount of literature existing from both the artificial intelligence and statistics research communities, where these models were originated. We cover classical topics such as maximum likelihood estimation and model selection via hypothesis testing, but also more modern approaches like regularization and Bayesian methods. We also discuss how the Markov models reviewed fit in the rich hierarchy of other, higher level Markov model classes. Finally, we close the paper overviewing relaxations of the Gaussian assumption and pointing out the main areas of application where these Markov models are nowadays used.
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution
Authors: Cong Ma, Kaizheng Wang, Yuejie Chi, Yuxin Chen
Date: 2017-11-28 00:00:00
Proceeding: ICML 2018
Tasks: Low-Rank Matrix Completion, Matrix Completion, Retrieval

Recent years have seen a flurry of activities in designing provably efficient nonconvex procedures for solving statistical estimation problems. Due to the highly nonconvex nature of the empirical loss, state-of-the-art procedures often require proper regularization (e.g. trimming, regularized cost, projection) in order to guarantee fast convergence. For vanilla procedures such as gradient descent, however, prior theory either recommends highly conservative learning rates to avoid overshooting, or completely lacks performance guarantees. This paper uncovers a striking phenomenon in nonconvex optimization: even in the absence of explicit regularization, gradient descent enforces proper regularization implicitly under various statistical models. In fact, gradient descent follows a trajectory staying within a basin that enjoys nice geometry, consisting of points incoherent with the sampling mechanism. This "implicit regularization" feature allows gradient descent to proceed in a far more aggressive fashion without overshooting, which in turn results in substantial computational savings. Focusing on three fundamental statistical estimation problems, i.e. phase retrieval, low-rank matrix completion, and blind deconvolution, we establish that gradient descent achieves near-optimal statistical and computational guarantees without explicit regularization. In particular, by marrying statistical modeling with generic optimization theory, we develop a general recipe for analyzing the trajectories of iterative algorithms via a leave-one-out perturbation argument. As a byproduct, for noisy matrix completion, we demonstrate that gradient descent achieves near-optimal error control --- measured entrywise and by the spectral norm --- which might be of independent interest.
Control Variates for Stochastic Gradient MCMC
Authors: Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth
Date: 2017-06-16 00:00:00

It is well known that Markov chain Monte Carlo (MCMC) methods scale poorly
with dataset size. A popular class of methods for solving this issue is
stochastic gradient MCMC. These methods use a noisy estimate of the gradient of
the log posterior, which reduces the per iteration computational cost of the
algorithm. Despite this, there are a number of results suggesting that
stochastic gradient Langevin dynamics (SGLD), probably the most popular of
these methods, still has computational cost proportional to the dataset size.
We suggest an alternative log posterior gradient estimate for stochastic
gradient MCMC, which uses control variates to reduce the variance. We analyse
SGLD using this gradient estimate, and show that, under log-concavity
assumptions on the target distribution, the computational cost required for a
given level of accuracy is independent of the dataset size. Next we show that a
different control variate technique, known as zero variance control variates
can be applied to SGMCMC algorithms for free. This post-processing step
improves the inference of the algorithm by reducing the variance of the MCMC
output. Zero variance control variates rely on the gradient of the log
posterior; we explore how the variance reduction is affected by replacing this
with the noisy gradient estimate calculated by SGMCMC.
Passing the Brazilian OAB Exam: data preparation and some experiments
Authors: Pedro Delfino, Bruno Cuconato, Edward Hermann Haeusler, Alexandre Rademaker
Date: 2017-12-14 00:00:00
Tasks: Legal Reasoning

In Brazil, all legal professionals must demonstrate their knowledge of the
law and its application by passing the OAB exams, the national bar exams. The
OAB exams therefore provide an excellent benchmark for the performance of legal
information systems since passing the exam would arguably signal that the
system has acquired capacity of legal reasoning comparable to that of a human
lawyer. This article describes the construction of a new data set and some
preliminary experiments on it, treating the problem of finding the
justification for the answers to questions. The results provide a baseline
performance measure against which to evaluate future improvements. We discuss
the reasons to the poor performance and propose next steps.
DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction Prediction
Authors: Sunyoung Kwon, Sungroh Yoon
Date: 2017-04-27 00:00:00
Tasks: Deep Learning, Feature Engineering

Chemical-chemical interaction (CCI) plays a key role in predicting candidate
drugs, toxicity, therapeutic effects, and biological functions. In various
types of chemical analyses, computational approaches are often required due to
the amount of data that needs to be handled. The recent remarkable growth and
outstanding performance of deep learning have attracted considerable research
attention. However,even in state-of-the-art drug analysis methods, deep
learning continues to be used only as a classifier, although deep learning is
capable of not only simple classification but also automated feature
extraction. In this paper, we propose the first end-to-end learning method for
CCI, named DeepCCI. Hidden features are derived from a simplified molecular
input line entry system (SMILES), which is a string notation representing the
chemical structure, instead of learning from crafted features. To discover
hidden representations for the SMILES strings, we use convolutional neural
networks (CNNs). To guarantee the commutative property for homogeneous
interaction, we apply model sharing and hidden representation merging
techniques. The performance of DeepCCI was compared with a plain deep
classifier and conventional machine learning methods. The proposed DeepCCI
showed the best performance in all seven evaluation metrics used. In addition,
the commutative property was experimentally validated. The automatically
extracted features through end-to-end SMILES learning alleviates the
significant efforts required for manual feature engineering. It is expected to
improve prediction performance, in drug analyses.
Multi-appearance Segmentation and Extended 0-1 Program for Dense Small Object Tracking
Authors: Longtao Chen, Jing Lou, Wei Zhu, Qingyuan Xia, Mingwu Ren
Date: 2017-12-14 00:00:00
Tasks: Management, Multi-Object Tracking, Object, Object Tracking

Aiming to address the fast multi-object tracking for dense small object in
the cluster background, we review track orientated multi-hypothesis
tracking(TOMHT) with consideration of batch optimization. Employing
autocorrelation based motion score test and staged hypotheses merging approach,
we build our homologous hypothesis generation and management method. A new
one-to-many constraint is proposed and applied to tackle the track exclusions
during complex occlusions. Besides, to achieve better results, we develop a
multi-appearance segmentation for detection, which exploits tree-like
topological information and realizes one threshold for one object. Experimental
results verify the strength of our methods, indicating speed and performance
advantages of our tracker.
Detection and Attention: Diagnosing Pulmonary Lung Cancer from CT by Imitating Physicians
Authors: Ning Li, Haopeng Liu, Bin Qiu, Wei Guo, Shijun Zhao, Kungang Li, Jie He
Date: 2017-12-14 00:00:00
Tasks: Computed Tomography (CT), Lung Nodule Detection, object-detection, Object Detection

This paper proposes a novel and efficient method to build a Computer-Aided
Diagnoses (CAD) system for lung nodule detection based on Computed Tomography
(CT). This task was treated as an Object Detection on Video (VID) problem by
imitating how a radiologist reads CT scans. A lung nodule detector was trained
to automatically learn nodule features from still images to detect lung nodule
candidates with both high recall and accuracy. Unlike previous work which used
3-dimensional information around the nodule to reduce false positives, we
propose two simple but efficient methods, Multi-slice propagation (MSP) and
Motionless-guide suppression (MLGS), which analyze sequence information of CT
scans to reduce false negatives and suppress false positives. We evaluated our
method in open-source LUNA16 dataset which contains 888 CT scans, and obtained
state-of-the-art result (Free-Response Receiver Operating Characteristic score
of 0.892) with detection speed (end to end within 20 seconds per patient on a
single NVidia GTX 1080) much higher than existing methods.
Data Augmentation in Emotion Classification Using Generative Adversarial Networks
Authors: Xinyue Zhu, Yifan Liu, Zengchang Qin, Jiahong Li
Date: 2017-11-02 00:00:00
Tasks: Classification, Data Augmentation, Emotion Classification, General Classification

It is a difficult task to classify images with multiple class labels using
only a small number of labeled examples, especially when the label (class)
distribution is imbalanced. Emotion classification is such an example of
imbalanced label distribution, because some classes of emotions like
\emph{disgusted} are relatively rare comparing to other labels like {\it happy
or sad}. In this paper, we propose a data augmentation method using generative
adversarial networks (GAN). It can complement and complete the data manifold
and find better margins between neighboring classes. Specifically, we design a
framework with a CNN model as the classifier and a cycle-consistent adversarial
networks (CycleGAN) as the generator. In order to avoid gradient vanishing
problem, we employ the least-squared loss as adversarial loss. We also propose
several evaluation methods on three benchmark datasets to validate GAN's
performance. Empirical results show that we can obtain 5%~10% increase in the
classification accuracy after employing the GAN-based data augmentation
techniques.