pwc-archive/papers-with-abstracts huggingface dataset:
A General Framework for the Recognition of Online Handwritten Graphics
Authors: Frank Julca-Aguilar, Harold Mouchère, Christian Viard-Gaudin, Nina S. T. Hirata
Date: 2017-09-19 00:00:00

We propose a new framework for the recognition of online handwritten
graphics. Three main features of the framework are its ability to treat symbol
and structural level information in an integrated way, its flexibility with
respect to different families of graphics, and means to control the tradeoff
between recognition effectiveness and computational cost. We model a graphic as
a labeled graph generated from a graph grammar. Non-terminal vertices represent
subcomponents, terminal vertices represent symbols, and edges represent
relations between subcomponents or symbols. We then model the recognition
problem as a graph parsing problem: given an input stroke set, we search for a
parse tree that represents the best interpretation of the input. Our graph
parsing algorithm generates multiple interpretations (consistent with the
grammar) and then we extract an optimal interpretation according to a cost
function that takes into consideration the likelihood scores of symbols and
structures. The parsing algorithm consists in recursively partitioning the
stroke set according to structures defined in the grammar and it does not
impose constraints present in some previous works (e.g. stroke ordering). By
avoiding such constraints and thanks to the powerful representativeness of
graphs, our approach can be adapted to the recognition of different graphic
notations. We show applications to the recognition of mathematical expressions
and flowcharts. Experimentation shows that our method obtains state-of-the-art
accuracy in both applications.
Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization
Authors: Tomoya Murata, Taiji Suzuki
Date: 2017-03-01 00:00:00
Proceeding: NeurIPS 2017 12

In this paper, we develop a new accelerated stochastic gradient method for
efficiently solving the convex regularized empirical risk minimization problem
in mini-batch settings. The use of mini-batches is becoming a golden standard
in the machine learning community, because mini-batch settings stabilize the
gradient estimate and can easily make good use of parallel computing. The core
of our proposed method is the incorporation of our new "double acceleration"
technique and variance reduction technique. We theoretically analyze our
proposed method and show that our method much improves the mini-batch
efficiencies of previous accelerated stochastic methods, and essentially only
needs size $\sqrt{n}$ mini-batches for achieving the optimal iteration
complexities for both non-strongly and strongly convex objectives, where $n$ is
the training set size. Further, we show that even in non-mini-batch settings,
our method achieves the best known convergence rate for both non-strongly and
strongly convex objectives.
APPD: Adaptive and Precise Pupil Boundary Detection using Entropy of Contour Gradients
Authors: Cihan Topal, Halil Ibrahim Cakir, Cuneyt Akinlar
Date: 2017-09-19 00:00:00
Tasks: Boundary Detection

Eye tracking spreads through a vast area of applications from ophthalmology,
assistive technologies to gaming and virtual reality. Precisely detecting the
pupil's contour and center is the very first step in many of these tasks, hence
needs to be performed accurately. Although detection of pupil is a simple
problem when it is entirely visible; occlusions and oblique view angles
complicate the solution. In this study, we propose APPD, an adaptive and
precise pupil boundary detection method that is able to infer whether entire
pupil is in clearly visible by a heuristic that estimates the shape of a
contour in a computationally efficient way. Thus, a faster detection is
performed with the assumption of no occlusions. If the heuristic fails, a more
comprehensive search among extracted image features is executed to maintain
accuracy. Furthermore, the algorithm can find out if there is no pupil as an
helpful information for many applications. We provide a dataset containing 3904
high resolution eye images collected from 12 subjects and perform an extensive
set of experiments to obtain quantitative results in terms of accuracy,
localization and timing. The proposed method outperforms three other state of
the art algorithms and has an average execution time $\sim$5 ms in
single-thread on a standard laptop computer for 720p images.
MetaLDA: a Topic Model that Efficiently Incorporates Meta information
Authors: He Zhao, Lan Du, Wray Buntine, Gang Liu
Date: 2017-09-19 00:00:00
Tasks: Topic Models, Word Embeddings

Besides the text content, documents and their associated words usually come
with rich sets of meta informa- tion, such as categories of documents and
semantic/syntactic features of words, like those encoded in word embeddings.
Incorporating such meta information directly into the generative process of
topic models can improve modelling accuracy and topic quality, especially in
the case where the word-occurrence information in the training data is
insufficient. In this paper, we present a topic model, called MetaLDA, which is
able to leverage either document or word meta information, or both of them
jointly. With two data argumentation techniques, we can derive an efficient
Gibbs sampling algorithm, which benefits from the fully local conjugacy of the
model. Moreover, the algorithm is favoured by the sparsity of the meta
information. Extensive experiments on several real world datasets demonstrate
that our model achieves comparable or improved performance in terms of both
perplexity and topic quality, particularly in handling sparse texts. In
addition, compared with other models using meta information, our model runs
significantly faster.
pix2code: Generating Code from a Graphical User Interface Screenshot
Authors: Tony Beltramelli
Date: 2017-05-22 00:00:00
Tasks: Class-level Code Generation, Code Generation

Transforming a graphical user interface screenshot created by a designer into
computer code is a typical task conducted by a developer in order to build
customized software, websites, and mobile applications. In this paper, we show
that deep learning methods can be leveraged to train a model end-to-end to
automatically generate code from a single input image with over 77% of accuracy
for three different platforms (i.e. iOS, Android and web-based technologies).
Learning Convolutional Networks for Content-weighted Image Compression
Authors: Mu Li, WangMeng Zuo, Shuhang Gu, Debin Zhao, David Zhang
Date: 2017-03-30 00:00:00
Proceeding: CVPR 2018 6
Tasks: Binarization, Decoder, Image Compression, SSIM

Lossy image compression is generally formulated as a joint rate-distortion
optimization to learn encoder, quantizer, and decoder. However, the quantizer
is non-differentiable, and discrete entropy estimation usually is required for
rate control. These make it very challenging to develop a convolutional network
(CNN)-based image compression system. In this paper, motivated by that the
local information content is spatially variant in an image, we suggest that the
bit rate of the different parts of the image should be adapted to local
content. And the content aware bit rate is allocated under the guidance of a
content-weighted importance map. Thus, the sum of the importance map can serve
as a continuous alternative of discrete entropy estimation to control
compression rate. And binarizer is adopted to quantize the output of encoder
due to the binarization scheme is also directly defined by the importance map.
Furthermore, a proxy function is introduced for binary operation in backward
propagation to make it differentiable. Therefore, the encoder, decoder,
binarizer and importance map can be jointly optimized in an end-to-end manner
by using a subset of the ImageNet database. In low bit rate image compression,
experiments show that our system significantly outperforms JPEG and JPEG 2000
by structural similarity (SSIM) index, and can produce the much better visual
result with sharp edges, rich textures, and fewer artifacts.
On the Generalized Essential Matrix Correction: An efficient solution to the problem and its applications
Authors: Pedro Miraldo, Joao R. Cardoso
Date: 2017-09-19 00:00:00

This paper addresses the problem of finding the closest generalized essential matrix from a given $6\times 6$ matrix, with respect to the Frobenius norm. To the best of our knowledge, this nonlinear constrained optimization problem has not been addressed in the literature yet. Although it can be solved directly, it involves a large number of constraints, and any optimization method to solve it would require much computational effort. We start by deriving a couple of unconstrained formulations of the problem. After that, we convert the original problem into a new one, involving only orthogonal constraints, and propose an efficient algorithm of steepest descent-type to find its solution. To test the algorithms, we evaluate the methods with synthetic data and conclude that the proposed steepest descent-type approach is much faster than the direct application of general optimization techniques to the original formulation with 33 constraints and to the unconstrained ones. To further motivate the relevance of our method, we apply it in two pose problems (relative and absolute) using synthetic and real data.
Nonnegative matrix factorization with side information for time series recovery and prediction
Authors: Jiali Mei, Yohann de Castro, Yannig Goude, Jean-Marc Azaïs, Georges Hébrail
Date: 2017-09-19 00:00:00
Tasks: Time Series, Time Series Analysis

Motivated by the reconstruction and the prediction of electricity
consumption, we extend Nonnegative Matrix Factorization~(NMF) to take into
account side information (column or row features). We consider general linear
measurement settings, and propose a framework which models non-linear
relationships between features and the response variables. We extend previous
theoretical results to obtain a sufficient condition on the identifiability of
the NMF in this setting. Based the classical Hierarchical Alternating Least
Squares~(HALS) algorithm, we propose a new algorithm (HALSX, or Hierarchical
Alternating Least Squares with eXogeneous variables) which estimates the
factorization model. The algorithm is validated on both simulated and real
electricity consumption datasets as well as a recommendation dataset, to show
its performance in matrix recovery and prediction for new rows and columns.
Learning Deep Architectures for Interaction Prediction in Structure-based Virtual Screening
Authors: Adam Gonczarek, Jakub M. Tomczak, Szymon Zaręba, Joanna Kaczmar, Piotr Dąbrowski, Michał J. Walczak
Date: 2016-10-23 00:00:00
Tasks: BIG-bench Machine Learning

We introduce a deep learning architecture for structure-based virtual
screening that generates fixed-sized fingerprints of proteins and small
molecules by applying learnable atom convolution and softmax operations to each
compound separately. These fingerprints are further transformed non-linearly,
their inner-product is calculated and used to predict the binding potential.
Moreover, we show that widely used benchmark datasets may be insufficient for
testing structure-based virtual screening methods that utilize machine
learning. Therefore, we introduce a new benchmark dataset, which we constructed
based on DUD-E and PDBBind databases.
Improving Opinion-Target Extraction with Character-Level Word Embeddings
Authors: Soufian Jebbara, Philipp Cimiano
Date: 2017-09-19 00:00:00
Proceeding: WS 2017 9
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Word Embeddings

Fine-grained sentiment analysis is receiving increasing attention in recent
years. Extracting opinion target expressions (OTE) in reviews is often an
important step in fine-grained, aspect-based sentiment analysis. Retrieving
this information from user-generated text, however, can be difficult. Customer
reviews, for instance, are prone to contain misspelled words and are difficult
to process due to their domain-specific language. In this work, we investigate
whether character-level models can improve the performance for the
identification of opinion target expressions. We integrate information about
the character structure of a word into a sequence labeling system using
character-level word embeddings and show their positive impact on the system's
performance. Specifically, we obtain an increase by 3.3 points F1-score with
respect to our baseline model. In further experiments, we reveal encoded
character patterns of the learned embeddings and give a nuanced view of the
performance differences of both models.
A Real-Time Deep Learning Pedestrian Detector for Robot Navigation
Authors: David Ribeiro, Andre Mateus, Pedro Miraldo, Jacinto C. Nascimento
Date: 2016-07-15 00:00:00
Tasks: Deep Learning, Pedestrian Detection, Robot Navigation

A real-time Deep Learning based method for Pedestrian Detection (PD) is
applied to the Human-Aware robot navigation problem. The pedestrian detector
combines the Aggregate Channel Features (ACF) detector with a deep
Convolutional Neural Network (CNN) in order to obtain fast and accurate
performance. Our solution is firstly evaluated using a set of real images taken
from onboard and offboard cameras and, then, it is validated in a typical robot
navigation environment with pedestrians (two distinct experiments are
conducted). The results on both tests show that our pedestrian detector is
robust and fast enough to be used on robot navigation applications.
Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture
Authors: Soufian Jebbara, Philipp Cimiano
Date: 2017-09-19 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Task 2, Term Extraction, Vocal Bursts Valence Prediction

The World Wide Web holds a wealth of information in the form of unstructured
texts such as customer reviews for products, events and more. By extracting and
analyzing the expressed opinions in customer reviews in a fine-grained way,
valuable opportunities and insights for customers and businesses can be gained.
We propose a neural network based system to address the task of Aspect-Based
Sentiment Analysis to compete in Task 2 of the ESWC-2016 Challenge on Semantic
Sentiment Analysis. Our proposed architecture divides the task in two subtasks:
aspect term extraction and aspect-specific sentiment extraction. This approach
is flexible in that it allows to address each subtask independently. As a first
step, a recurrent neural network is used to extract aspects from a text by
framing the problem as a sequence labeling task. In a second step, a recurrent
network processes each extracted aspect with respect to its context and
predicts a sentiment label. The system uses pretrained semantic word embedding
features which we experimentally enhance with semantic knowledge extracted from
WordNet. Further features extracted from SenticNet prove to be beneficial for
the extraction of sentiment labels. As the best performing system in its
category, our proposed system proves to be an effective approach for the
Aspect-Based Sentiment Analysis.
Aspect-Based Relational Sentiment Analysis Using a Stacked Neural Network Architecture
Authors: Soufian Jebbara, Philipp Cimiano
Date: 2017-09-19 00:00:00
Tasks: Relation, Relation Extraction, Sentiment Analysis, Term Extraction

Sentiment analysis can be regarded as a relation extraction problem in which
the sentiment of some opinion holder towards a certain aspect of a product,
theme or event needs to be extracted. We present a novel neural architecture
for sentiment analysis as a relation extraction problem that addresses this
problem by dividing it into three subtasks: i) identification of aspect and
opinion terms, ii) labeling of opinion terms with a sentiment, and iii)
extraction of relations between opinion terms and aspect terms. For each
subtask, we propose a neural network based component and combine all of them
into a complete system for relational sentiment analysis. The component for
aspect and opinion term extraction is a hybrid architecture consisting of a
recurrent neural network stacked on top of a convolutional neural network. This
approach outperforms a standard convolutional deep neural architecture as well
as a recurrent network architecture and performs competitively compared to
other methods on two datasets of annotated customer reviews. To extract
sentiments for individual opinion terms, we propose a recurrent architecture in
combination with word distance features and achieve promising results,
outperforming a majority baseline by 18% accuracy and providing the first
results for the USAGE dataset. Our relation extraction component outperforms
the current state-of-the-art in aspect-opinion relation extraction by 15%
F-Measure.
Exploring Human-like Attention Supervision in Visual Question Answering
Authors: Tingting Qiao, Jianfeng Dong, Duanqing Xu
Date: 2017-09-19 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Attention mechanisms have been widely applied in the Visual Question
Answering (VQA) task, as they help to focus on the area-of-interest of both
visual and textual information. To answer the questions correctly, the model
needs to selectively target different areas of an image, which suggests that an
attention-based model may benefit from an explicit attention supervision. In
this work, we aim to address the problem of adding attention supervision to VQA
models. Since there is a lack of human attention data, we first propose a Human
Attention Network (HAN) to generate human-like attention maps, training on a
recently released dataset called Human ATtention Dataset (VQA-HAT). Then, we
apply the pre-trained HAN on the VQA v2.0 dataset to automatically produce the
human-like attention maps for all image-question pairs. The generated
human-like attention map dataset for the VQA v2.0 dataset is named as
Human-Like ATtention (HLAT) dataset. Finally, we apply human-like attention
supervision to an attention-based VQA model. The experiments show that adding
human-like supervision yields a more accurate attention together with a better
performance, showing a promising future for human-like attention supervision in
VQA.
Scalable Estimation of Dirichlet Process Mixture Models on Distributed Data
Authors: Ruohui Wang, Dahua Lin
Date: 2017-09-19 00:00:00

We consider the estimation of Dirichlet Process Mixture Models (DPMMs) in
distributed environments, where data are distributed across multiple computing
nodes. A key advantage of Bayesian nonparametric models such as DPMMs is that
they allow new components to be introduced on the fly as needed. This, however,
posts an important challenge to distributed estimation -- how to handle new
components efficiently and consistently. To tackle this problem, we propose a
new estimation method, which allows new components to be created locally in
individual computing nodes. Components corresponding to the same cluster will
be identified and merged via a probabilistic consolidation scheme. In this way,
we can maintain the consistency of estimation with very low communication cost.
Experiments on large real-world data sets show that the proposed method can
achieve high scalability in distributed and asynchronous environments without
compromising the mixing performance.
Colour Terms: a Categorisation Model Inspired by Visual Cortex Neurons
Authors: Arash Akbarinia, C. Alejandro Parraga
Date: 2017-09-19 00:00:00

Although it seems counter-intuitive, categorical colours do not exist as
external physical entities but are very much the product of our brains. Our
cortical machinery segments the world and associate objects to specific colour
terms, which is not only convenient for communication but also increases the
efficiency of visual processing by reducing the dimensionality of input scenes.
Although the neural substrate for this phenomenon is unknown, a recent study of
cortical colour processing has discovered a set of neurons that are
isoresponsive to stimuli in the shape of 3D-ellipsoidal surfaces in
colour-opponent space. We hypothesise that these neurons might help explain the
underlying mechanisms of colour naming in the visual cortex.
  Following this, we propose a biologically-inspired colour naming model where
each colour term - e.g. red, green, blue, yellow, etc. - is represented through
an ellipsoid in 3D colour-opponent space. This paradigm is also supported by
previous psychophysical colour categorisation experiments whose results
resemble such shapes. "Belongingness" of each pixel to different colour
categories is computed by a non-linear sigmoidal logistic function. The final
colour term for a given pixel is calculated by a maximum pooling mechanism. The
simplicity of our model allows its parameters to be learnt from a handful of
segmented images. It also offers a straightforward extension to include further
colour terms. Additionally, ellipsoids of proposed model can adapt to image
contents offering a dynamical solution in order to address phenomenon of colour
constancy. Our results on the Munsell chart and two datasets of real-world
images show an overall improvement comparing to state-of-the-art algorithms.
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Authors: Guotai Wang, Maria A. Zuluaga, Wenqi Li, Rosalind Pratt, Premal A. Patel, Michael Aertsen, Tom Doel, Anna L. David, Jan Deprest, Sebastien Ourselin, Tom Vercauteren
Date: 2017-07-03 00:00:00
Tasks: Brain Tumor Segmentation, Image Segmentation, Interactive Segmentation, Medical Image Segmentation, Placenta Segmentation, Segmentation, Semantic Segmentation, Tumor Segmentation

Accurate medical image segmentation is essential for diagnosis, surgical
planning and many other applications. Convolutional Neural Networks (CNNs) have
become the state-of-the-art automatic segmentation methods. However, fully
automatic results may still need to be refined to become accurate and robust
enough for clinical use. We propose a deep learning-based interactive
segmentation method to improve the results obtained by an automatic CNN and to
reduce user interactions during refinement for higher accuracy. We use one CNN
to obtain an initial automatic segmentation, on which user interactions are
added to indicate mis-segmentations. Another CNN takes as input the user
interactions with the initial segmentation and gives a refined result. We
propose to combine user interactions with CNNs through geodesic distance
transforms, and propose a resolution-preserving network that gives a better
dense prediction. In addition, we integrate user interactions as hard
constraints into a back-propagatable Conditional Random Field. We validated the
proposed framework in the context of 2D placenta segmentation from fetal MRI
and 3D brain tumor segmentation from FLAIR images. Experimental results show
our method achieves a large improvement from automatic CNNs, and obtains
comparable and even higher accuracy with fewer user interventions and less time
compared with traditional interactive methods.
Linearly constrained Gaussian processes
Authors: Carl Jidling, Niklas Wahlström, Adrian Wills, Thomas B. Schön
Date: 2017-03-02 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Gaussian Processes

We consider a modification of the covariance function in Gaussian processes
to correctly account for known linear constraints. By modelling the target
function as a transformation of an underlying function, the constraints are
explicitly incorporated in the model such that they are guaranteed to be
fulfilled by any sample drawn or prediction made. We also propose a
constructive procedure for designing the transformation operator and illustrate
the result on both simulated and real-data examples.