pwc-archive/papers-with-abstracts huggingface dataset:
Comparison of ontology alignment systems across single matching task via the McNemar's test
Authors: Majid Mohammadi, Amir Ahooye Atashin, Wout Hofman, Yao-Hua Tan
Date: 2017-03-29 00:00:00
Tasks: Anatomy

Ontology alignment is widely-used to find the correspondences between
different ontologies in diverse fields.After discovering the alignments,several
performance scores are available to evaluate them.The scores typically require
the identified alignment and a reference containing the underlying actual
correspondences of the given ontologies.The current trend in the alignment
evaluation is to put forward a new score(e.g., precision, weighted precision,
etc.)and to compare various alignments by juxtaposing the obtained scores.
However,it is substantially provocative to select one measure among others for
comparison.On top of that, claiming if one system has a better performance than
one another cannot be substantiated solely by comparing two scalars.In this
paper,we propose the statistical procedures which enable us to theoretically
favor one system over one another.The McNemar's test is the statistical means
by which the comparison of two ontology alignment systems over one matching
task is drawn.The test applies to a 2x2 contingency table which can be
constructed in two different ways based on the alignments,each of which has
their own merits/pitfalls.The ways of the contingency table construction and
various apposite statistics from the McNemar's test are elaborated in minute
detail.In the case of having more than two alignment systems for comparison,
the family-wise error rate is expected to happen. Thus, the ways of preventing
such an error are also discussed.A directed graph visualizes the outcome of the
McNemar's test in the presence of multiple alignment systems.From this graph,
it is readily understood if one system is better than one another or if their
differences are imperceptible.The proposed statistical methodologies are
applied to the systems participated in the OAEI 2016 anatomy track, and also
compares several well-known similarity metrics for the same matching problem.
Automatic Stance Detection Using End-to-End Memory Networks
Authors: Mitra Mohtarami, Ramy Baly, James Glass, Preslav Nakov, Lluis Marquez, Alessandro Moschitti
Date: 2018-04-20 00:00:00
Proceeding: NAACL 2018 6
Tasks: Stance Detection

We present a novel end-to-end memory network for stance detection, which
jointly (i) predicts whether a document agrees, disagrees, discusses or is
unrelated with respect to a given target claim, and also (ii) extracts snippets
of evidence for that prediction. The network operates at the paragraph level
and integrates convolutional and recurrent neural networks, as well as a
similarity matrix as part of the overall architecture. The experimental
evaluation on the Fake News Challenge dataset shows state-of-the-art
performance.
Robust And Scalable Learning Of Complex Dataset Topologies Via Elpigraph
Authors: Luca Albergante, Evgeny M. Mirkes, Huidong Chen, Alexis Martin, Louis Faure, Emmanuel Barillot, Luca Pinello, Alexander N. Gorban, Andrei Zinovyev
Date: 2018-04-20 00:00:00
Tasks: Astronomy

Large datasets represented by multidimensional data point clouds often
possess non-trivial distributions with branching trajectories and excluded
regions, with the recent single-cell transcriptomic studies of developing
embryo being notable examples. Reducing the complexity and producing compact
and interpretable representations of such data remains a challenging task. Most
of the existing computational methods are based on exploring the local data
point neighbourhood relations, a step that can perform poorly in the case of
multidimensional and noisy data. Here we present ElPiGraph, a scalable and
robust method for approximation of datasets with complex structures which does
not require computing the complete data distance matrix or the data point
neighbourhood graph. This method is able to withstand high levels of noise and
is capable of approximating complex topologies via principal graph ensembles
that can be combined into a consensus principal graph. ElPiGraph deals
efficiently with large and complex datasets in various fields from biology,
where it can be used to infer gene dynamics from single-cell RNA-Seq, to
astronomy, where it can be used to explore complex structures in the
distribution of galaxies.
Skin Tone Emoji and Sentiment on Twitter
Authors: Steven Coats
Date: 2018-04-20 00:00:00
Tasks: Attribute

In 2015, the Unicode Consortium introduced five skin tone emoji that can be
used in combination with emoji representing human figures and body parts. In
this study, use of the skin tone emoji is analyzed geographically in a large
sample of data from Twitter. It can be shown that values for the skin tone
emoji by country correspond approximately to the skin tone of the resident
populations, and that a negative correlation exists between tweet sentiment and
darker skin tone at the global level. In an era of large-scale migrations and
continued sensitivity to questions of skin color and race, understanding how
new language elements such as skin tone emoji are used can help frame our
understanding of how people represent themselves and others in terms of a
salient personal appearance attribute.
Evaluating Discourse Phenomena in Neural Machine Translation
Authors: Rachel Bawden, Rico Sennrich, Alexandra Birch, Barry Haddow
Date: 2017-11-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation, NMT, Sentence, Translation

For machine translation to tackle discourse phenomena, models must have
access to extra-sentential linguistic context. There has been recent interest
in modelling context in neural machine translation (NMT), but models have been
principally evaluated with standard automatic metrics, poorly adapted to
evaluating discourse phenomena. In this article, we present hand-crafted,
discourse test sets, designed to test the models' ability to exploit previous
source and target sentences. We investigate the performance of recently
proposed multi-encoder NMT models trained on subtitles for English to French.
We also explore a novel way of exploiting context from the previous sentence.
Despite gains using BLEU, multi-encoder models give limited improvement in the
handling of discourse phenomena: 50% accuracy on our coreference test set and
53.5% for coherence/cohesion (compared to a non-contextual baseline of 50%). A
simple strategy of decoding the concatenation of the previous and current
sentence leads to good performance, and our novel strategy of multi-encoding
and decoding of two sentences leads to the best performance (72.5% for
coreference and 57% for coherence/cohesion), highlighting the importance of
target-side context.
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
Authors: Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry
Date: 2018-02-15 00:00:00
Tasks: 3D Surface Generation, Point Cloud Completion, Super-Resolution

We introduce a method for learning to generate the surface of 3D shapes. Our
approach represents a 3D shape as a collection of parametric surface elements
and, in contrast to methods generating voxel grids or point clouds, naturally
infers a surface representation of the shape. Beyond its novelty, our new shape
generation framework, AtlasNet, comes with significant advantages, such as
improved precision and generalization capabilities, and the possibility to
generate a shape of arbitrary resolution without memory issues. We demonstrate
these benefits and compare to strong baselines on the ShapeNet benchmark for
two applications: (i) auto-encoding shapes, and (ii) single-view reconstruction
from a still image. We also provide results showing its potential for other
applications, such as morphing, parametrization, super-resolution, matching,
and co-segmentation.
Lower Bounds on the Bayes Risk of the Bayesian BTL Model with Applications to Comparison Graphs
Authors: Mine Alsan, Ranjitha Prasad, Vincent Y. F. Tan
Date: 2017-09-27 00:00:00

We consider the problem of aggregating pairwise comparisons to obtain a
consensus ranking order over a collection of objects. We use the popular
Bradley-Terry-Luce (BTL) model which allows us to probabilistically describe
pairwise comparisons between objects. In particular, we employ the Bayesian BTL
model which allows for meaningful prior assumptions and to cope with situations
where the number of objects is large and the number of comparisons between some
objects is small or even zero. For the conventional Bayesian BTL model, we
derive information-theoretic lower bounds on the Bayes risk of estimators for
norm-based distortion functions. We compare the information-theoretic lower
bound with the Bayesian Cram\'{e}r-Rao lower bound we derive for the case when
the Bayes risk is the mean squared error. We illustrate the utility of the
bounds through simulations by comparing them with the error performance of an
expectation-maximization based inference algorithm proposed for the Bayesian
BTL model. We draw parallels between pairwise comparisons in the BTL model and
inter-player games represented as edges in a comparison graph and analyze the
effect of various graph structures on the lower bounds. We also extend the
information-theoretic and Bayesian Cram\'{e}r-Rao lower bounds to the more
general Bayesian BTL model which takes into account home-field advantage.
Energy-Efficient CMOS Memristive Synapses for Mixed-Signal Neuromorphic System-on-a-Chip
Authors: Vishal Saxena, Xinyu Wu, Kehan Zhu
Date: 2018-02-07 00:00:00

Emerging non-volatile memory (NVM), or memristive, devices promise
energy-efficient realization of deep learning, when efficiently integrated with
mixed-signal integrated circuits on a CMOS substrate. Even though several
algorithmic challenges need to be addressed to turn the vision of memristive
Neuromorphic Systems-on-a-Chip (NeuSoCs) into reality, issues at the device and
circuit interface need immediate attention from the community. In this work, we
perform energy-estimation of a NeuSoC system and predict the desirable circuit
and device parameters for energy-efficiency optimization. Also, CMOS synapse
circuits based on the concept of CMOS memristor emulator are presented as a
system prototyping methodology, while practical memristor devices are being
developed and integrated with general-purpose CMOS. The proposed mixed-signal
memristive synapse can be designed and fabricated using standard CMOS
technologies and open doors to interesting applications in cognitive computing
circuits.
An Approximate Shading Model with Detail Decomposition for Object Relighting
Authors: Zicheng Liao, Kevin Karsch, Hongyi Zhang, David Forsyth
Date: 2018-04-20 00:00:00
Tasks: Object

We present an object relighting system that allows an artist to select an
object from an image and insert it into a target scene. Through simple
interactions, the system can adjust illumination on the inserted object so that
it appears naturally in the scene. To support image-based relighting, we build
object model from the image, and propose a \emph{perceptually-inspired}
approximate shading model for the relighting. It decomposes the shading field
into (a) a rough shape term that can be reshaded, (b) a parametric shading
detail that encodes missing features from the first term, and (c) a geometric
detail term that captures fine-scale material properties. With this
decomposition, the shading model combines 3D rendering and image-based
composition and allows more flexible compositing than image-based methods.
Quantitative evaluation and a set of user studies suggest our method is a
promising alternative to existing methods of object insertion.
PAC-Bayesian Margin Bounds for Convolutional Neural Networks
Authors: Konstantinos Pitas, Mike Davies, Pierre Vandergheynst
Date: 2017-12-30 00:00:00

Recently the generalization error of deep neural networks has been analyzed
through the PAC-Bayesian framework, for the case of fully connected layers. We
adapt this approach to the convolutional setting.
Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound
Authors: Markus A. Degel, Nassir Navab, Shadi Albarqouni
Date: 2018-04-20 00:00:00
Tasks: Domain Adaptation, Left Atrium Segmentation, Medical Image Analysis, Segmentation

Segmentation of the left atrium and deriving its size can help to predict and
detect various cardiovascular conditions. Automation of this process in 3D
Ultrasound image data is desirable, since manual delineations are
time-consuming, challenging and observer-dependent. Convolutional neural
networks have made improvements in computer vision and in medical image
analysis. They have successfully been applied to segmentation tasks and were
extended to work on volumetric data. In this paper we introduce a combined
deep-learning based approach on volumetric segmentation in Ultrasound
acquisitions with incorporation of prior knowledge about left atrial shape and
imaging device. The results show, that including a shape prior helps the domain
adaptation and the accuracy of segmentation is further increased with
adversarial learning.
Visualisation and 'diagnostic classifiers' reveal how recurrent and recursive neural networks process hierarchical structure
Authors: Dieuwke Hupkes, Sara Veldhoen, Willem Zuidema
Date: 2017-11-28 00:00:00
Tasks: Diagnostic

We investigate how neural networks can learn and process languages with
hierarchical, compositional semantics. To this end, we define the artificial
task of processing nested arithmetic expressions, and study whether different
types of neural networks can learn to compute their meaning. We find that
recursive neural networks can find a generalising solution to this problem, and
we visualise this solution by breaking it up in three steps: project, sum and
squash. As a next step, we investigate recurrent neural networks, and show that
a gated recurrent unit, that processes its input incrementally, also performs
very well on this task. To develop an understanding of what the recurrent
network encodes, visualisation techniques alone do not suffice. Therefore, we
develop an approach where we formulate and test multiple hypotheses on the
information encoded and processed by the network. For each hypothesis, we
derive predictions about features of the hidden state representations at each
time step, and train 'diagnostic classifiers' to test those predictions. Our
results indicate that the networks follow a strategy similar to our
hypothesised 'cumulative strategy', which explains the high accuracy of the
network on novel expressions, the generalisation to longer expressions than
seen in training, and the mild deterioration with increasing length. This is
turn shows that diagnostic classifiers can be a useful technique for opening up
the black box of neural networks. We argue that diagnostic classification,
unlike most visualisation techniques, does scale up from small networks in a
toy domain, to larger and deeper recurrent networks dealing with real-life
data, and may therefore contribute to a better understanding of the internal
dynamics of current state-of-the-art models in natural language processing.
Residual-Guide Feature Fusion Network for Single Image Deraining
Authors: Zhiwen Fan, Huafeng Wu, Xueyang Fu, Yue Hunag, Xinghao Ding
Date: 2018-04-20 00:00:00
Tasks: Rain Removal, Single Image Deraining

Single image rain streaks removal is extremely important since rainy images
adversely affect many computer vision systems. Deep learning based methods have
found great success in image deraining tasks. In this paper, we propose a novel
residual-guide feature fusion network, called ResGuideNet, for single image
deraining that progressively predicts highquality reconstruction. Specifically,
we propose a cascaded network and adopt residuals generated from shallower
blocks to guide deeper blocks. By using this strategy, we can obtain a coarse
to fine estimation of negative residual as the blocks go deeper. The outputs of
different blocks are merged into the final reconstruction. We adopt recursive
convolution to build each block and apply supervision to all intermediate
results, which enable our model to achieve promising performance on synthetic
and real-world data while using fewer parameters than previous required.
ResGuideNet is detachable to meet different rainy conditions. For images with
light rain streaks and limited computational resource at test time, we can
obtain a decent performance even with several building blocks. Experiments
validate that ResGuideNet can benefit other low- and high-level vision tasks.
Graph-based Hypothesis Generation for Parallax-tolerant Image Stitching
Authors: Jing Chen, Nan Li, Tianli Liao
Date: 2018-04-20 00:00:00
Tasks: Image Stitching

The seam-driven approach has been proven fairly effective for
parallax-tolerant image stitching, whose strategy is to search for an invisible
seam from finite representative hypotheses of local alignment. In this paper,
we propose a graph-based hypothesis generation and a seam-guided local
alignment for improving the effectiveness and the efficiency of the seam-driven
approach. The experiment demonstrates the significant reduction of number of
hypotheses and the improved quality of naturalness of final stitching results,
comparing to the state-of-the-art method SEAGULL.
Understanding Regularization to Visualize Convolutional Neural Networks
Authors: Maximilian Baust, Florian Ludwig, Christian Rupprecht, Matthias Kohl, Stefan Braunewell
Date: 2018-04-20 00:00:00

Variational methods for revealing visual concepts learned by convolutional
neural networks have gained significant attention during the last years. Being
based on noisy gradients obtained via back-propagation such methods require the
application of regularization strategies. We present a mathematical framework
unifying previously employed regularization methods. Within this framework, we
propose a novel technique based on Sobolev gradients which can be implemented
via convolutions and does not require specialized numerical treatment, such as
total variation regularization. The experiments performed on feature inversion
and activation maximization demonstrate the benefit of a unified approach to
regularization, such as sharper reconstructions via the proposed Sobolev
filters and a better control over reconstructed scales.
Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization
Authors: Fabirzio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Gianluca Bontempi
Date: 2018-04-20 00:00:00
Tasks: Active Learning, Fraud Detection

Credit card fraud detection is a very challenging problem because of the
specific nature of transaction data and the labeling process. The transaction
data is peculiar because they are obtained in a streaming fashion, they are
strongly imbalanced and prone to non-stationarity. The labeling is the outcome
of an active learning process, as every day human investigators contact only a
small number of cardholders (associated to the riskiest transactions) and
obtain the class (fraud or genuine) of the related transactions. An adequate
selection of the set of cardholders is therefore crucial for an efficient fraud
detection process. In this paper, we present a number of active learning
strategies and we investigate their fraud detection accuracies. We compare
different criteria (supervised, semi-supervised and unsupervised) to query
unlabeled transactions. Finally, we highlight the existence of an
exploitation/exploration trade-off for active learning in the context of fraud
detection, which has so far been overlooked in the literature.
Neon2: Finding Local Minima via First-Order Oracles
Authors: Zeyuan Allen-Zhu, Yuanzhi Li
Date: 2017-11-17 00:00:00
Proceeding: NeurIPS 2018 12

We propose a reduction for non-convex optimization that can (1) turn an
stationary-point finding algorithm into an local-minimum finding one, and (2)
replace the Hessian-vector product computations with only gradient
computations. It works both in the stochastic and the deterministic settings,
without hurting the algorithm's performance.
  As applications, our reduction turns Natasha2 into a first-order method
without hurting its performance. It also converts SGD, GD, SCSG, and SVRG into
algorithms finding approximate local minima, outperforming some best known
results.
Outline Objects using Deep Reinforcement Learning
Authors: Zhenxin Wang, Sayan Sarcar, Jingxin Liu, Yilin Zheng, Xiangshi Ren
Date: 2018-04-10 00:00:00
Tasks: Deep Reinforcement Learning, Image Segmentation, Object, Position, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Segmentation, Semantic Segmentation

Image segmentation needs both local boundary position information and global
object context information. The performance of the recent state-of-the-art
method, fully convolutional networks, reaches a bottleneck due to the neural
network limit after balancing between the two types of information
simultaneously in an end-to-end training style. To overcome this problem, we
divide the semantic image segmentation into temporal subtasks. First, we find a
possible pixel position of some object boundary; then trace the boundary at
steps within a limited length until the whole object is outlined. We present
the first deep reinforcement learning approach to semantic image segmentation,
called DeepOutline, which outperforms other algorithms in Coco detection
leaderboard in the middle and large size person category in Coco val2017
dataset. Meanwhile, it provides an insight into a divide and conquer way by
reinforcement learning on computer vision problems.
Accurate Deep Direct Geo-Localization from Ground Imagery and Phone-Grade GPS
Authors: Shaohui Sun, Ramesh Sarukkai, Jack Kwok, Vinay Shet
Date: 2018-04-20 00:00:00
Tasks: 3D Reconstruction, Autonomous Driving, geo-localization, Visual Localization

One of the most critical topics in autonomous driving or ride-sharing
technology is to accurately localize vehicles in the world frame. In addition
to common multi-view camera systems, it usually also relies on industrial grade
sensors, such as LiDAR, differential GPS, high precision IMU, and etc. In this
paper, we develop an approach to provide an effective solution to this problem.
We propose a method to train a geo-spatial deep neural network (CNN+LSTM) to
predict accurate geo-locations (latitude and longitude) using only ordinary
ground imagery and low accuracy phone-grade GPS. We evaluate our approach on
the open dataset released during ACM Multimedia 2017 Grand Challenge. Having
ground truth locations for training, we are able to reach nearly lane-level
accuracy. We also evaluate the proposed method on our own collected images in
San Francisco downtown area often described as "downtown canyon" where consumer
GPS signals are extremely inaccurate. The results show the model can predict
quality locations that suffice in real business applications, such as
ride-sharing, only using phone-grade GPS. Unlike classic visual localization or
recent PoseNet-like methods that may work well in indoor environments or
small-scale outdoor environments, we avoid using a map or an SFM
(structure-from-motion) model at all. More importantly, the proposed method can
be scaled up without concerns over the potential failure of 3D reconstruction.
Delegating via Quitting Games
Authors: Juan Afanador, Nir Oren, Murilo S. Baptista
Date: 2018-04-20 00:00:00
Tasks: Multi-Armed Bandits

Delegation allows an agent to request that another agent completes a task. In
many situations the task may be delegated onwards, and this process can repeat
until it is eventually, successfully or unsuccessfully, performed. We consider
policies to guide an agent in choosing who to delegate to when such recursive
interactions are possible. These policies, based on quitting games and
multi-armed bandits, were empirically tested for effectiveness. Our results
indicate that the quitting game based policies outperform those which do not
explicitly account for the recursive nature of delegation.