pwc-archive/papers-with-abstracts huggingface dataset:
Detecting Noteheads in Handwritten Scores with ConvNets and Bounding Box Regression
Authors: Jan Hajič jr., Pavel Pecina
Date: 2017-08-05 00:00:00
Tasks: General Classification, regression

Noteheads are the interface between the written score and music. Each
notehead on the page signifies one note to be played, and detecting noteheads
is thus an unavoidable step for Optical Music Recognition. Noteheads are
clearly distinct objects, however, the variety of music notation handwriting
makes noteheads harder to identify, and while handwritten music notation symbol
{\em classification} is a well-studied task, symbol {\em detection} has usually
been limited to heuristics and rule-based systems instead of machine learning
methods better suited to deal with the uncertainties in handwriting. We present
ongoing work on a simple notehead detector using convolutional neural networks
for pixel classification and bounding box regression that achieves a detection
f-score of 0.97 on binary score images in the MUSCIMA++ dataset, does not
require staff removal, and is applicable to a variety of handwriting styles and
levels of musical complexity.
Thompson Sampling Guided Stochastic Searching on the Line for Deceptive Environments with Applications to Root-Finding Problems
Authors: Sondre Glimsdal, Ole-Christoffer Granmo
Date: 2017-08-05 00:00:00
Tasks: Stochastic Optimization, Thompson Sampling

The multi-armed bandit problem forms the foundation for solving a wide range
of on-line stochastic optimization problems through a simple, yet effective
mechanism. One simply casts the problem as a gambler that repeatedly pulls one
out of N slot machine arms, eliciting random rewards. Learning of reward
probabilities is then combined with reward maximization, by carefully balancing
reward exploration against reward exploitation. In this paper, we address a
particularly intriguing variant of the multi-armed bandit problem, referred to
as the {\it Stochastic Point Location (SPL) Problem}. The gambler is here only
told whether the optimal arm (point) lies to the "left" or to the "right" of
the arm pulled, with the feedback being erroneous with probability $1-\pi$.
This formulation thus captures optimization in continuous action spaces with
both {\it informative} and {\it deceptive} feedback. To tackle this class of
problems, we formulate a compact and scalable Bayesian representation of the
solution space that simultaneously captures both the location of the optimal
arm as well as the probability of receiving correct feedback. We further
introduce the accompanying Thompson Sampling guided Stochastic Point Location
(TS-SPL) scheme for balancing exploration against exploitation. By learning
$\pi$, TS-SPL also supports {\it deceptive} environments that are lying about
the direction of the optimal arm. This, in turn, allows us to solve the
fundamental Stochastic Root Finding (SRF) Problem. Empirical results
demonstrate that our scheme deals with both deceptive and informative
environments, significantly outperforming competing algorithms both for SRF and
SPL.
e-QRAQ: A Multi-turn Reasoning Dataset and Simulator with Explanations
Authors: Clemens Rosenbaum, Tian Gao, Tim Klinger
Date: 2017-08-05 00:00:00

In this paper we present a new dataset and user simulator e-QRAQ (explainable
Query, Reason, and Answer Question) which tests an Agent's ability to read an
ambiguous text; ask questions until it can answer a challenge question; and
explain the reasoning behind its questions and answer. The User simulator
provides the Agent with a short, ambiguous story and a challenge question about
the story. The story is ambiguous because some of the entities have been
replaced by variables. At each turn the Agent may ask for the value of a
variable or try to answer the challenge question. In response the User
simulator provides a natural language explanation of why the Agent's query or
answer was useful in narrowing down the set of possible answers, or not. To
demonstrate one potential application of the e-QRAQ dataset, we train a new
neural architecture based on End-to-End Memory Networks to successfully
generate both predictions and partial explanations of its current understanding
of the problem. We observe a strong correlation between the quality of the
prediction and explanation.
Quantifying homologous proteins and proteoforms
Authors: Dmitry Malioutov, Tianchi Chen, Jacob Jaffe, Edoardo Airoldi, Steven Carr, Bogdan Budnik, Nikolai Slavov
Date: 2017-08-05 00:00:00

Many proteoforms - arising from alternative splicing, post-translational
modifications (PTMs), or paralogous genes - have distinct biological functions,
such as histone PTM proteoforms. However, their quantification by existing
bottom-up mass-spectrometry (MS) methods is undermined by peptide-specific
biases. To avoid these biases, we developed and implemented a first-principles
model (HIquant) for quantifying proteoform stoichiometries. We characterized
when MS data allow inferring proteoform stoichiometries by HIquant, derived an
algorithm for optimal inference, and demonstrated experimentally high accuracy
in quantifying fractional PTM occupancy without using external standards, even
in the challenging case of the histone modification code.
  HIquant server is implemented at:
https://web.northeastern.edu/slavov/2014_HIquant/
Fast Modeling Methods for Complex System with Separable Features
Authors: Chen Chen, Changtong Luo, Zonglin Jiang
Date: 2017-08-05 00:00:00
Tasks: global-optimization

Data-driven modeling plays an increasingly important role in different areas
of engineering. For most of existing methods, such as genetic programming (GP),
the convergence speed might be too slow for large scale problems with a large
number of variables. Fortunately, in many applications, the target models are
separable in some sense. In this paper, we analyze different types of
separability of some real-world engineering equations and establish a
mathematical model of generalized separable system (GS system). In order to get
the structure of the GS system, two concepts, namely block and factor are
introduced, and a special method, block and factor detection is also proposed,
in which the target model is decomposed into a number of blocks, further into
minimal blocks and factors. Compare to the conventional GP, the new method can
make large reductions to the search space. The minimal blocks and factors are
optimized and assembled with a global optimization search engine, low
dimensional simplex evolution (LDSE). An extensive study between the proposed
method and a state-of-the-art data-driven fitting tool, Eureqa, has been
presented with several man-made problems. Test results indicate that the
proposed method is more effective and efficient under all the investigated
cases.
Neural Machine Translation with Word Predictions
Authors: Rongxiang Weng, Shu-Jian Huang, Zaixiang Zheng, Xin-yu Dai, Jia-Jun Chen
Date: 2017-08-05 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Decoder, Machine Translation, NMT, Sentence, Translation

In the encoder-decoder architecture for neural machine translation (NMT), the
hidden states of the recurrent structures in the encoder and decoder carry the
crucial information about the sentence.These vectors are generated by
parameters which are updated by back-propagation of translation errors through
time. We argue that propagating errors through the end-to-end recurrent
structures are not a direct way of control the hidden vectors. In this paper,
we propose to use word predictions as a mechanism for direct supervision. More
specifically, we require these vectors to be able to predict the vocabulary in
target sentence. Our simple mechanism ensures better representations in the
encoder and decoder without using any extra data or annotation. It is also
helpful in reducing the target side vocabulary and improving the decoding
efficiency. Experiments on Chinese-English and German-English machine
translation tasks show BLEU improvements by 4.53 and 1.3, respectively
Extractive Multi Document Summarization using Dynamical Measurements of Complex Networks
Authors: Jorge V. Tohalino, Diego R. Amancio
Date: 2017-08-05 00:00:00
Tasks: Document Summarization, Extractive Document Summarization, Extractive Text Summarization, Multi-Document Summarization

Due to the large amount of textual information available on Internet, it is
of paramount relevance to use techniques that find relevant and concise
content. A typical task devoted to the identification of informative sentences
in documents is the so called extractive document summarization task. In this
paper, we use complex network concepts to devise an extractive Multi Document
Summarization (MDS) method, which extracts the most central sentences from
several textual sources. In the proposed model, texts are represented as
networks, where nodes represent sentences and the edges are established based
on the number of shared words. Differently from previous works, the
identification of relevant terms is guided by the characterization of nodes via
dynamical measurements of complex networks, including symmetry, accessibility
and absorption time. The evaluation of the proposed system revealed that
excellent results were obtained with particular dynamical measurements,
including those based on the exploration of networks via random walks.
A Syllable-based Technique for Word Embeddings of Korean Words
Authors: Sanghyuk Choi, Taeuk Kim, Jinseok Seol, Sang-goo Lee
Date: 2017-08-05 00:00:00
Proceeding: WS 2017 9
Tasks: Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Translation, Word Embeddings

Word embedding has become a fundamental component to many NLP tasks such as
named entity recognition and machine translation. However, popular models that
learn such embeddings are unaware of the morphology of words, so it is not
directly applicable to highly agglutinative languages such as Korean. We
propose a syllable-based learning model for Korean using a convolutional neural
network, in which word representation is composed of trained syllable vectors.
Our model successfully produces morphologically meaningful representation of
Korean words compared to the original Skip-gram embeddings. The results also
show that it is quite robust to the Out-of-Vocabulary problem.
A Novel data Pre-processing method for multi-dimensional and non-uniform data
Authors: Farhana Javed Zareen, Suraiya Jabin
Date: 2017-08-05 00:00:00

We are in the era of data analytics and data science which is on full bloom.
There is abundance of all kinds of data for example biometrics based data,
satellite images data, chip-seq data, social network data, sensor based data
etc. from a variety of sources. This data abundance is the result of the fact
that storage cost is getting cheaper day by day, so people as well as almost
all business or scientific organizations are storing more and more data. Most
of the real data is multi-dimensional, non-uniform, and big in size, such that
it requires a unique pre-processing before analyzing it. In order to make data
useful for any kind of analysis, pre-processing is a very important step. This
paper presents a unique and novel pre-processing method for multi-dimensional
and non-uniform data with the aim of making it uniform and reduced in size
without losing much of its value. We have chosen biometric signature data to
demonstrate the proposed method as it qualifies for the attributes of being
multi-dimensional, non-uniform and big in size. Biometric signature data does
not only captures the structural characteristics of a signature but also its
behavioral characteristics that are captured using a dynamic signature capture
device. These features like pen pressure, pen tilt angle, time taken to sign a
document when collected in real-time turn out to be of varying dimensions. This
feature data set along with the structural data needs to be pre-processed in
order to use it to train a machine learning based model for signature
verification purposes. We demonstrate the success of the proposed method over
other methods using experimental results for biometric signature data but the
same can be implemented for any other data with similar properties from a
different domain.
The Landscape of Deep Learning Algorithms
Authors: Pan Zhou, Jiashi Feng
Date: 2017-05-19 00:00:00
Tasks: Deep Learning, Generalization Bounds

This paper studies the landscape of empirical risk of deep neural networks by
theoretically analyzing its convergence behavior to the population risk as well
as its stationary points and properties. For an $l$-layer linear neural
network, we prove its empirical risk uniformly converges to its population risk
at the rate of $\mathcal{O}(r^{2l}\sqrt{d\log(l)}/\sqrt{n})$ with training
sample size of $n$, the total weight dimension of $d$ and the magnitude bound
$r$ of weight of each layer. We then derive the stability and generalization
bounds for the empirical risk based on this result. Besides, we establish the
uniform convergence of gradient of the empirical risk to its population
counterpart. We prove the one-to-one correspondence of the non-degenerate
stationary points between the empirical and population risks with convergence
guarantees, which describes the landscape of deep neural networks. In addition,
we analyze these properties for deep nonlinear neural networks with sigmoid
activation functions. We prove similar results for convergence behavior of
their empirical risks as well as the gradients and analyze properties of their
non-degenerate stationary points.
  To our best knowledge, this work is the first one theoretically
characterizing landscapes of deep learning algorithms. Besides, our results
provide the sample complexity of training a good deep neural network. We also
provide theoretical understanding on how the neural network depth $l$, the
layer width, the network size $d$ and parameter magnitude determine the neural
network landscapes.
Referenceless Quality Estimation for Natural Language Generation
Authors: Ondřej Dušek, Jekaterina Novikova, Verena Rieser
Date: 2017-08-05 00:00:00
Tasks: Text Generation

Traditional automatic evaluation measures for natural language generation
(NLG) use costly human-authored references to estimate the quality of a system
output. In this paper, we propose a referenceless quality estimation (QE)
approach based on recurrent neural networks, which predicts a quality score for
a NLG system output by comparing it to the source meaning representation only.
Our method outperforms traditional metrics and a constant baseline in most
respects; we also show that synthetic data helps to increase correlation
results by 21% compared to the base system. Our results are comparable to
results obtained in similar QE tasks despite the more challenging setting.
Jointly Extracting Relations with Class Ties via Effective Deep Ranking
Authors: Hai Ye, WenHan Chao, Zhunchen Luo, Zhoujun Li
Date: 2016-12-22 00:00:00
Proceeding: ACL 2017 7
Tasks: Relation, Relation Extraction

Connections between relations in relation extraction, which we call class
ties, are common. In distantly supervised scenario, one entity tuple may have
multiple relation facts. Exploiting class ties between relations of one entity
tuple will be promising for distantly supervised relation extraction. However,
previous models are not effective or ignore to model this property. In this
work, to effectively leverage class ties, we propose to make joint relation
extraction with a unified model that integrates convolutional neural network
(CNN) with a general pairwise ranking framework, in which three novel ranking
loss functions are introduced. Additionally, an effective method is presented
to relieve the severe class imbalance problem from NR (not relation) for model
training. Experiments on a widely used dataset show that leveraging class ties
will enhance extraction and demonstrate the effectiveness of our model to learn
class ties. Our model outperforms the baselines significantly, achieving
state-of-the-art performance.
Recurrent Human Pose Estimation
Authors: Vasileios Belagiannis, Andrew Zisserman
Date: 2016-05-10 00:00:00
Tasks: Pose Estimation

We propose a novel ConvNet model for predicting 2D human body poses in an
image. The model regresses a heatmap representation for each body keypoint, and
is able to learn and represent both the part appearances and the context of the
part configuration. We make the following three contributions: (i) an
architecture combining a feed forward module with a recurrent module, where the
recurrent module can be run iteratively to improve the performance, (ii) the
model can be trained end-to-end and from scratch, with auxiliary losses
incorporated to improve performance, (iii) we investigate whether keypoint
visibility can also be predicted. The model is evaluated on two benchmark
datasets. The result is a simple architecture that achieves performance on par
with the state of the art, but without the complexity of a graphical model
stage (or layers).
SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis
Authors: Mengqi Ji, Juergen Gall, Haitian Zheng, Yebin Liu, Lu Fang
Date: 2017-08-05 00:00:00
Proceeding: ICCV 2017 10

This paper proposes an end-to-end learning framework for multiview
stereopsis. We term the network SurfaceNet. It takes a set of images and their
corresponding camera parameters as input and directly infers the 3D model. The
key advantage of the framework is that both photo-consistency as well geometric
relations of the surface structure can be directly learned for the purpose of
multiview stereopsis in an end-to-end fashion. SurfaceNet is a fully 3D
convolutional network which is achieved by encoding the camera parameters
together with the images in a 3D voxel representation. We evaluate SurfaceNet
on the large-scale DTU benchmark.
A simple genome-wide association study algorithm
Authors: Lev V. Utkin, Irina L. Utkina
Date: 2017-08-05 00:00:00

A computationally simple genome-wide association study (GWAS) algorithm for
estimating the main and epistatic effects of markers or single nucleotide
polymorphisms (SNPs) is proposed. It is based on the intuitive assumption that
changes of alleles corresponding to important SNPs in a pair of individuals
lead to large difference of phenotype values of these individuals. The
algorithm is based on considering pairs of individuals instead of SNPs or pairs
of SNPs. The main advantage of the algorithm is that it weakly depends on the
number of SNPs in a genotype matrix. It mainly depends on the number of
individuals, which is typically very small in comparison with the number of
SNPs. Numerical experiments with real data sets illustrate the proposed
algorithm.
An aggregating strategy for shifting experts in discrete sequence prediction
Authors: Vishnu Raj, Sheetal Kalyani
Date: 2017-08-05 00:00:00

We study how we can adapt a predictor to a non-stationary environment with
advises from multiple experts. We study the problem under complete feedback
when the best expert changes over time from a decision theoretic point of view.
Proposed algorithm is based on popular exponential weighing method with
exponential discounting. We provide theoretical results bounding regret under
the exponential discounting setting. Upper bound on regret is derived for
finite time horizon problem. Numerical verification of different real life
datasets are provided to show the utility of proposed algorithm.
Learning Discriminative Alpha-Beta-divergence for Positive Definite Matrices (Extended Version)
Authors: Anoop Cherian, Panagiotis Stanitsas, Mehrtash Harandi, Vassilios Morellas, Nikolaos Papanikolopoulos
Date: 2017-08-05 00:00:00
Tasks: Dictionary Learning, Metric Learning, Riemannian optimization

Symmetric positive definite (SPD) matrices are useful for capturing
second-order statistics of visual data. To compare two SPD matrices, several
measures are available, such as the affine-invariant Riemannian metric,
Jeffreys divergence, Jensen-Bregman logdet divergence, etc.; however, their
behaviors may be application dependent, raising the need of manual selection to
achieve the best possible performance. Further and as a result of their
overwhelming complexity for large-scale problems, computing pairwise
similarities by clever embedding of SPD matrices is often preferred to direct
use of the aforementioned measures. In this paper, we propose a discriminative
metric learning framework, Information Divergence and Dictionary Learning
(IDDL), that not only learns application specific measures on SPD matrices
automatically, but also embeds them as vectors using a learned dictionary. To
learn the similarity measures (which could potentially be distinct for every
dictionary atom), we use the recently introduced alpha-beta-logdet divergence,
which is known to unify the measures listed above. We propose a novel IDDL
objective, that learns the parameters of the divergence and the dictionary
atoms jointly in a discriminative setup and is solved efficiently using
Riemannian optimization. We showcase extensive experiments on eight computer
vision datasets, demonstrating state-of-the-art performances.
Semi-Automatic Terminology Ontology Learning Based on Topic Modeling
Authors: Monika Rani, Amit Kumar Dhar, O. P. Vyas
Date: 2017-08-05 00:00:00

Ontologies provide features like a common vocabulary, reusability,
machine-readable content, and also allows for semantic search, facilitate agent
interaction and ordering & structuring of knowledge for the Semantic Web (Web
3.0) application. However, the challenge in ontology engineering is automatic
learning, i.e., the there is still a lack of fully automatic approach from a
text corpus or dataset of various topics to form ontology using machine
learning techniques. In this paper, two topic modeling algorithms are explored,
namely LSI & SVD and Mr.LDA for learning topic ontology. The objective is to
determine the statistical relationship between document and terms to build a
topic ontology and ontology graph with minimum human intervention. Experimental
analysis on building a topic ontology and semantic retrieving corresponding
topic ontology for the user's query demonstrating the effectiveness of the
proposed approach.
Sublabel-Accurate Discretization of Nonconvex Free-Discontinuity Problems
Authors: Thomas Möllenhoff, Daniel Cremers
Date: 2016-11-21 00:00:00
Proceeding: ICCV 2017 10

In this work we show how sublabel-accurate multilabeling approaches can be
derived by approximating a classical label-continuous convex relaxation of
nonconvex free-discontinuity problems. This insight allows to extend these
sublabel-accurate approaches from total variation to general convex and
nonconvex regularizations. Furthermore, it leads to a systematic approach to
the discretization of continuous convex relaxations. We study the relationship
to existing discretizations and to discrete-continuous MRFs. Finally, we apply
the proposed approach to obtain a sublabel-accurate and convex solution to the
vectorial Mumford-Shah functional and show in several experiments that it leads
to more precise solutions using fewer labels.