pwc-archive/papers-with-abstracts huggingface dataset:
A Deep Generative Model of Vowel Formant Typology
Authors: Ryan Cotterell, Jason Eisner
Date: 2018-07-08 00:00:00
Proceeding: NAACL 2018 6

What makes some types of languages more probable than others? For instance,
we know that almost all spoken languages contain the vowel phoneme /i/; why
should that be? The field of linguistic typology seeks to answer these
questions and, thereby, divine the mechanisms that underlie human language. In
our work, we tackle the problem of vowel system typology, i.e., we propose a
generative probability model of which vowels a language contains. In contrast
to previous work, we work directly with the acoustic information -- the first
two formant values -- rather than modeling discrete sets of phonemic symbols
(IPA). We develop a novel generative probability model and report results based
on a corpus of 233 languages.
Data-driven Upsampling of Point Clouds
Authors: Wentai Zhang, Haoliang Jiang, Zhangsihao Yang, Soji Yamakawa, Kenji Shimada, Levent Burak Kara
Date: 2018-07-08 00:00:00

High quality upsampling of sparse 3D point clouds is critically useful for a
wide range of geometric operations such as reconstruction, rendering, meshing,
and analysis. In this paper, we propose a data-driven algorithm that enables an
upsampling of 3D point clouds without the need for hard-coded rules. Our
approach uses a deep network with Chamfer distance as the loss function,
capable of learning the latent features in point clouds belonging to different
object categories. We evaluate our algorithm across different amplification
factors, with upsampling learned and performed on objects belonging to the same
category as well as different categories. We also explore the desirable
characteristics of input point clouds as a function of the distribution of the
point samples. Finally, we demonstrate the performance of our algorithm in
single-category training versus multi-category training scenarios. The final
proposed model is compared against a baseline, optimization-based upsampling
method. Results indicate that our algorithm is capable of generating more
uniform and accurate upsamplings.
Detecting Synapse Location and Connectivity by Signed Proximity Estimation and Pruning with Deep Nets
Authors: Toufiq Parag, Daniel Berger, Lee Kamentsky, Benedikt Staffler, Donglai Wei, Moritz Helmstaedter, Jeff W. Lichtman, Hanspeter Pfister
Date: 2018-07-08 00:00:00

Synaptic connectivity detection is a critical task for neural reconstruction
from Electron Microscopy (EM) data. Most of the existing algorithms for synapse
detection do not identify the cleft location and direction of connectivity
simultaneously. The few methods that computes direction along with contact
location have only been demonstrated to work on either dyadic (most common in
vertebrate brain) or polyadic (found in fruit fly brain) synapses, but not on
both types. In this paper, we present an algorithm to automatically predict the
location as well as the direction of both dyadic and polyadic synapses. The
proposed algorithm first generates candidate synaptic connections from
voxelwise predictions of signed proximity generated by a 3D U-net. A second 3D
CNN then prunes the set of candidates to produce the final detection of cleft
and connectivity orientation. Experimental results demonstrate that the
proposed method outperforms the existing methods for determining synapses in
both rodent and fruit fly brain.
A Deep-Learning-Based Geological Parameterization for History Matching Complex Models
Authors: Yimin Liu, Wenyue Sun, Louis J. Durlofsky
Date: 2018-07-07 00:00:00

A new low-dimensional parameterization based on principal component analysis
(PCA) and convolutional neural networks (CNN) is developed to represent complex
geological models. The CNN-PCA method is inspired by recent developments in
computer vision using deep learning. CNN-PCA can be viewed as a generalization
of an existing optimization-based PCA (O-PCA) method. Both CNN-PCA and O-PCA
entail post-processing a PCA model to better honor complex geological features.
In CNN-PCA, rather than use a histogram-based regularization as in O-PCA, a new
regularization involving a set of metrics for multipoint statistics is
introduced. The metrics are based on summary statistics of the nonlinear filter
responses of geological models to a pre-trained deep CNN. In addition, in the
CNN-PCA formulation presented here, a convolutional neural network is trained
as an explicit transform function that can post-process PCA models quickly.
CNN-PCA is shown to provide both unconditional and conditional realizations
that honor the geological features present in reference SGeMS geostatistical
realizations for a binary channelized system. Flow statistics obtained through
simulation of random CNN-PCA models closely match results for random SGeMS
models for a demanding case in which O-PCA models lead to significant
discrepancies. Results for history matching are also presented. In this
assessment CNN-PCA is applied with derivative-free optimization, and a subspace
randomized maximum likelihood method is used to provide multiple posterior
models. Data assimilation and significant uncertainty reduction are achieved
for existing wells, and physically reasonable predictions are also obtained for
new wells. Finally, the CNN-PCA method is extended to a more complex
non-stationary bimodal deltaic fan system, and is shown to provide high-quality
realizations for this challenging example.
DeepSource: Point Source Detection using Deep Learning
Authors: A. Vafaei Sadr, Etienne. E. Vos, Bruce A. Bassett, Zafiirah Hosenie, N. Oozeer, Michelle Lochner
Date: 2018-07-07 00:00:00
Tasks: Deep Learning, Radio Interferometry

Point source detection at low signal-to-noise is challenging for astronomical
surveys, particularly in radio interferometry images where the noise is
correlated. Machine learning is a promising solution, allowing the development
of algorithms tailored to specific telescope arrays and science cases. We
present DeepSource - a deep learning solution - that uses convolutional neural
networks to achieve these goals. DeepSource enhances the Signal-to-Noise Ratio
(SNR) of the original map and then uses dynamic blob detection to detect
sources. Trained and tested on two sets of 500 simulated 1 deg x 1 deg MeerKAT
images with a total of 300,000 sources, DeepSource is essentially perfect in
both purity and completeness down to SNR = 4 and outperforms PyBDSF in all
metrics. For uniformly-weighted images it achieves a Purity x Completeness (PC)
score at SNR = 3 of 0.73, compared to 0.31 for the best PyBDSF model. For
natural-weighting we find a smaller improvement of ~40% in the PC score at SNR
= 3. If instead we ask where either of the purity or completeness first drop to
90%, we find that DeepSource reaches this value at SNR = 3.6 compared to the
4.3 of PyBDSF (natural-weighting). A key advantage of DeepSource is that it can
learn to optimally trade off purity and completeness for any science case under
consideration. Our results show that deep learning is a promising approach to
point source detection in astronomical images.
Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery
Authors: Seyed Majid Azimi, Eleonora Vig, Reza Bahmanyar, Marco Körner, Peter Reinartz
Date: 2018-07-07 00:00:00
Tasks: Management, Object, object-detection, Object Detection, Object Detection In Aerial Images, Region Proposal

Automatic multi-class object detection in remote sensing images in
unconstrained scenarios is of high interest for several applications including
traffic monitoring and disaster management. The huge variation in object scale,
orientation, category, and complex backgrounds, as well as the different camera
sensors pose great challenges for current algorithms. In this work, we propose
a new method consisting of a novel joint image cascade and feature pyramid
network with multi-size convolution kernels to extract multi-scale strong and
weak semantic features. These features are fed into rotation-based region
proposal and region of interest networks to produce object detections. Finally,
rotational non-maximum suppression is applied to remove redundant detections.
During training, we minimize joint horizontal and oriented bounding box loss
functions, as well as a novel loss that enforces oriented boxes to be
rectangular. Our method achieves 68.16% mAP on horizontal and 72.45% mAP on
oriented bounding box detection tasks on the challenging DOTA dataset,
outperforming all published methods by a large margin (+6% and +12% absolute
improvement, respectively). Furthermore, it generalizes to two other datasets,
NWPU VHR-10 and UCAS-AOD, and achieves competitive results with the baselines
even when trained on DOTA. Our method can be deployed in multi-class object
detection applications, regardless of the image and object scales and
orientations, making it a great choice for unconstrained aerial and satellite
imagery.
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions
Authors: Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab Mirrokni
Date: 2018-07-07 00:00:00
Proceeding: ICML 2018 7
Tasks: Vocal Bursts Intensity Prediction

Consider the following class of learning schemes: $$\hat{\boldsymbol{\beta}}
:= \arg\min_{\boldsymbol{\beta}}\;\sum_{j=1}^n
\ell(\boldsymbol{x}_j^\top\boldsymbol{\beta}; y_j) + \lambda
R(\boldsymbol{\beta}),\qquad\qquad (1) $$ where $\boldsymbol{x}_i \in
\mathbb{R}^p$ and $y_i \in \mathbb{R}$ denote the $i^{\text{th}}$ feature and
response variable respectively. Let $\ell$ and $R$ be the loss function and
regularizer, $\boldsymbol{\beta}$ denote the unknown weights, and $\lambda$ be
a regularization parameter. Finding the optimal choice of $\lambda$ is a
challenging problem in high-dimensional regimes where both $n$ and $p$ are
large. We propose two frameworks to obtain a computationally efficient
approximation ALO of the leave-one-out cross validation (LOOCV) risk for
nonsmooth losses and regularizers. Our two frameworks are based on the primal
and dual formulations of (1). We prove the equivalence of the two approaches
under smoothness conditions. This equivalence enables us to justify the
accuracy of both methods under such conditions. We use our approaches to obtain
a risk estimate for several standard problems, including generalized LASSO,
nuclear norm regularization, and support vector machines. We empirically
demonstrate the effectiveness of our results for non-differentiable cases.
VFPred: A Fusion of Signal Processing and Machine Learning techniques in Detecting Ventricular Fibrillation from ECG Signals
Authors: Nabil Ibtehaz, M. Saifur Rahman, M. Sohel Rahman
Date: 2018-07-07 00:00:00
Tasks: Arrhythmia Detection, Binary Classification, Electrocardiography (ECG), General Classification, Specificity, Ventricular fibrillation detection

Ventricular Fibrillation (VF), one of the most dangerous arrhythmias, is
responsible for sudden cardiac arrests. Thus, various algorithms have been
developed to predict VF from Electrocardiogram (ECG), which is a binary
classification problem. In the literature, we find a number of algorithms based
on signal processing, where, after some robust mathematical operations the
decision is given based on a predefined threshold over a single value. On the
other hand, some machine learning based algorithms are also reported in the
literature; however, these algorithms merely combine some parameters and make a
prediction using those as features. Both the approaches have their perks and
pitfalls; thus our motivation was to coalesce them to get the best out of the
both worlds. Hence we have developed, VFPred that, in addition to employing a
signal processing pipeline, namely, Empirical Mode Decomposition and Discrete
Time Fourier Transform for useful feature extraction, uses a Support Vector
Machine for efficient classification. VFPred turns out to be a robust algorithm
as it is able to successfully segregate the two classes with equal confidence
(Sensitivity = 99.99%, Specificity = 98.40%) even from a short signal of 5
seconds long, whereas existing works though requires longer signals, flourishes
in one but fails in the other.
A Supervised Geometry-Aware Mapping Approach for Classification of Hyperspectral Images
Authors: Ramanarayan Mohanty, S. L. Happy, Aurobinda Routray
Date: 2018-07-07 00:00:00
Tasks: Classification, Classification Of Hyperspectral Images, Dimensionality Reduction, General Classification

The lack of proper class discrimination among the Hyperspectral (HS) data
points poses a potential challenge in HS classification. To address this issue,
this paper proposes an optimal geometry-aware transformation for enhancing the
classification accuracy. The underlying idea of this method is to obtain a
linear projection matrix by solving a nonlinear objective function based on the
intrinsic geometrical structure of the data. The objective function is
constructed to quantify the discrimination between the points from dissimilar
classes on the projected data space. Then the obtained projection matrix is
used to linearly map the data to more discriminative space. The effectiveness
of the proposed transformation is illustrated with three benchmark real-world
HS data sets. The experiments reveal that the classification and dimensionality
reduction methods on the projected discriminative space outperform their
counterpart in the original space.
Robust and Scalable Differentiable Neural Computer for Question Answering
Authors: Jörg Franke, Jan Niehues, Alex Waibel
Date: 2018-07-07 00:00:00
Proceeding: WS 2018 7
Tasks: Question Answering

Deep learning models are often not easily adaptable to new tasks and require
task-specific adjustments. The differentiable neural computer (DNC), a
memory-augmented neural network, is designed as a general problem solver which
can be used in a wide range of tasks. But in reality, it is hard to apply this
model to new tasks. We analyze the DNC and identify possible improvements
within the application of question answering. This motivates a more robust and
scalable DNC (rsDNC). The objective precondition is to keep the general
character of this model intact while making its application more reliable and
speeding up its required training time. The rsDNC is distinguished by a more
robust training, a slim memory unit and a bidirectional architecture. We not
only achieve new state-of-the-art performance on the bAbI task, but also
minimize the performance variance between different initializations.
Furthermore, we demonstrate the simplified applicability of the rsDNC to new
tasks with passable results on the CNN RC task without adaptions.
Tournament Based Ranking CNN for the Cataract grading
Authors: Dohyeun Kim, Tae Joon Jun, Daeyoung Kim, Youngsub Eom
Date: 2018-07-07 00:00:00

Solving the classification problem, unbalanced number of dataset among the
classes often causes performance degradation. Especially when some classes
dominate the other classes with its large number of datasets, trained model
shows low performance in identifying the dominated classes. This is common case
when it comes to medical dataset. Because the case with a serious degree is not
quite usual, there are imbalance in number of dataset between severe case and
normal cases of diseases. Also, there is difficulty in precisely identifying
grade of medical data because of vagueness between them. To solve these
problems, we propose new architecture of convolutional neural network named
Tournament based Ranking CNN which shows remarkable performance gain in
identifying dominated classes while trading off very small accuracy loss in
dominating classes. Our Approach complemented problems that occur when method
of Ranking CNN that aggregates outputs of multiple binary neural network models
is applied to medical data. By having tournament structure in aggregating
method and using very deep pretrained binary models, our proposed model
recorded 68.36% of exact match accuracy, while Ranking CNN recorded 53.40%,
pretrained Resnet recorded 56.12% and CNN with linear regression recorded
57.48%. As a result, our proposed method is applied efficiently to cataract
grading which have ordinal labels with imbalanced number of data among classes,
also can be applied further to medical problems which have similar features to
cataract and similar dataset configuration.
One-shot Texture Segmentation
Authors: Ivan Ustyuzhaninov, Claudio Michaelis, Wieland Brendel, Matthias Bethge
Date: 2018-07-07 00:00:00
Tasks: Segmentation

We introduce one-shot texture segmentation: the task of segmenting an input
image containing multiple textures given a patch of a reference texture. This
task is designed to turn the problem of texture-based perceptual grouping into
an objective benchmark. We show that it is straight-forward to generate large
synthetic data sets for this task from a relatively small number of natural
textures. In particular, this task can be cast as a self-supervised problem
thereby alleviating the need for massive amounts of manually annotated data
necessary for traditional segmentation tasks. In this paper we introduce and
study two concrete data sets: a dense collage of textures (CollTex) and a
cluttered texturized Omniglot data set. We show that a baseline model trained
on these synthesized data is able to generalize to natural images and videos
without further fine-tuning, suggesting that the learned image representations
are useful for higher-level vision tasks.
When Work Matters: Transforming Classical Network Structures to Graph CNN
Authors: Wenting Zhao, Chunyan Xu, Zhen Cui, Tong Zhang, Jiatao Jiang, Zhen-Yu Zhang, Jian Yang
Date: 2018-07-07 00:00:00
Tasks: Graph Classification, Video Understanding

Numerous pattern recognition applications can be formed as learning from
graph-structured data, including social network, protein-interaction network,
the world wide web data, knowledge graph, etc. While convolutional neural
network (CNN) facilitates great advances in gridded image/video understanding
tasks, very limited attention has been devoted to transform these successful
network structures (including Inception net, Residual net, Dense net, etc.) to
establish convolutional networks on graph, due to its irregularity and
complexity geometric topologies (unordered vertices, unfixed number of adjacent
edges/vertices). In this paper, we aim to give a comprehensive analysis of when
work matters by transforming different classical network structures to graph
CNN, particularly in the basic graph recognition problem. Specifically, we
firstly review the general graph CNN methods, especially in its spectral
filtering operation on the irregular graph data. We then introduce the basic
structures of ResNet, Inception and DenseNet into graph CNN and construct these
network structures on graph, named as G_ResNet, G_Inception, G_DenseNet. In
particular, it seeks to help graph CNNs by shedding light on how these
classical network structures work and providing guidelines for choosing
appropriate graph network frameworks. Finally, we comprehensively evaluate the
performance of these different network structures on several public graph
datasets (including social networks and bioinformatic datasets), and
demonstrate how different network structures work on graph CNN in the graph
recognition task.
How game complexity affects the playing behavior of synthetic agents
Authors: Chairi Kiourt, Dimitris Kalles, Panagiotis Kanellopoulos
Date: 2018-07-07 00:00:00
Tasks: Navigate

Agent based simulation of social organizations, via the investigation of
agents' training and learning tactics and strategies, has been inspired by the
ability of humans to learn from social environments which are rich in agents,
interactions and partial or hidden information. Such richness is a source of
complexity that an effective learner has to be able to navigate. This paper
focuses on the investigation of the impact of the environmental complexity on
the game playing-and-learning behavior of synthetic agents. We demonstrate our
approach using two independent turn-based zero-sum games as the basis of
forming social events which are characterized both by competition and
cooperation. The paper's key highlight is that as the complexity of a social
environment changes, an effective player has to adapt its learning and playing
profile to maintain a given performance profile
Recommender system for learning SQL using hints
Authors: Dejan Lavbič, Tadej Matek, Aljaž Zrnec
Date: 2018-07-07 00:00:00
Tasks: Recommendation Systems

Today's software industry requires individuals who are proficient in as many
programming languages as possible. Structured query language (SQL), as an
adopted standard, is no exception, as it is the most widely used query language
to retrieve and manipulate data. However, the process of learning SQL turns out
to be challenging. The need for a computer-aided solution to help users learn
SQL and improve their proficiency is vital. In this study, we present a new
approach to help users conceptualize basic building blocks of the language
faster and more efficiently. The adaptive design of the proposed approach aids
users in learning SQL by supporting their own path to the solution and
employing successful previous attempts, while not enforcing the ideal solution
provided by the instructor. Furthermore, we perform an empirical evaluation
with 93 participants and demonstrate that the employment of hints is
successful, being especially beneficial for users with lower prior knowledge.
Video Prediction with Appearance and Motion Conditions
Authors: Yunseok Jang, Gunhee Kim, Yale Song
Date: 2018-07-07 00:00:00
Proceeding: ICML 2018 7
Tasks: Prediction, Video Prediction

Video prediction aims to generate realistic future frames by learning dynamic
visual patterns. One fundamental challenge is to deal with future uncertainty:
How should a model behave when there are multiple correct, equally probable
future? We propose an Appearance-Motion Conditional GAN to address this
challenge. We provide appearance and motion information as conditions that
specify how the future may look like, reducing the level of uncertainty. Our
model consists of a generator, two discriminators taking charge of appearance
and motion pathways, and a perceptual ranking module that encourages videos of
similar conditions to look similar. To train our model, we develop a novel
conditioning scheme that consists of different combinations of appearance and
motion conditions. We evaluate our model using facial expression and human
action datasets and report favorable results compared to existing methods.
Representing a Partially Observed Non-Rigid 3D Human Using Eigen-Texture and Eigen-Deformation
Authors: Ryosuke Kimura, Akihiko Sayo, Fabian Lorenzo Dayrit, Yuta Nakashima, Hiroshi Kawasaki, Ambrosio Blanco, Katsushi Ikeuchi
Date: 2018-07-07 00:00:00

Reconstruction of the shape and motion of humans from RGB-D is a challenging
problem, receiving much attention in recent years. Recent approaches for
full-body reconstruction use a statistic shape model, which is built upon
accurate full-body scans of people in skin-tight clothes, to complete invisible
parts due to occlusion. Such a statistic model may still be fit to an RGB-D
measurement with loose clothes but cannot describe its deformations, such as
clothing wrinkles. Observed surfaces may be reconstructed precisely from actual
measurements, while we have no cues for unobserved surfaces. For full-body
reconstruction with loose clothes, we propose to use lower dimensional
embeddings of texture and deformation referred to as eigen-texturing and
eigen-deformation, to reproduce views of even unobserved surfaces. Provided a
full-body reconstruction from a sequence of partial measurements as 3D meshes,
the texture and deformation of each triangle are then embedded using
eigen-decomposition. Combined with neural-network-based coefficient regression,
our method synthesizes the texture and deformation from arbitrary viewpoints.
We evaluate our method using simulated data and visually demonstrate how our
method works on real data.