pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Neural Word Segmentation for Chinese via Segmental Language Modeling
Authors: Zhiqing Sun, Zhi-Hong Deng
Date: 2018-10-07 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Chinese Word Segmentation, Decoder, Language Modeling, Language Modelling, Segmentation

Previous traditional approaches to unsupervised Chinese word segmentation
(CWS) can be roughly classified into discriminative and generative models. The
former uses the carefully designed goodness measures for candidate
segmentation, while the latter focuses on finding the optimal segmentation of
the highest generative probability. However, while there exists a trivial way
to extend the discriminative models into neural version by using neural
language models, those of generative ones are non-trivial. In this paper, we
propose the segmental language models (SLMs) for CWS. Our approach explicitly
focuses on the segmental nature of Chinese, as well as preserves several
properties of language models. In SLMs, a context encoder encodes the previous
context and a segment decoder generates each segment incrementally. As far as
we know, we are the first to propose a neural model for unsupervised CWS and
achieve competitive performance to the state-of-the-art statistical models on
four different datasets from SIGHAN 2005 bakeoff.
Finding Correspondences for Optical Flow and Disparity Estimations using a Sub-pixel Convolution-based Encoder-Decoder Network
Authors: Juan Luis Gonzalez, Muhammad Sarmad, Hyunjoo J. Lee, Munchurl Kim
Date: 2018-10-07 00:00:00
Tasks: Decoder, Disparity Estimation, Optical Flow Estimation

Deep convolutional neural networks (DCNN) have recently shown promising
results in low-level computer vision problems such as optical flow and
disparity estimation, but still, have much room to further improve their
performance. In this paper, we propose a novel sub-pixel convolution-based
encoder-decoder network for optical flow and disparity estimations, which can
extend FlowNetS and DispNet by replacing the deconvolution layers with
sup-pixel convolution blocks. By using sub-pixel refinement and estimation on
the decoder stages instead of deconvolution, we can significantly improve the
estimation accuracy for optical flow and disparity, even with reduced numbers
of parameters. We show a supervised end-to-end training of our proposed
networks for optical flow and disparity estimations, and an unsupervised
end-to-end training for monocular depth and pose estimations. In order to
verify the effectiveness of our proposed networks, we perform intensive
experiments for (i) optical flow and disparity estimations, and (ii) monocular
depth and pose estimations. Throughout the extensive experiments, our proposed
networks outperform the baselines such as FlowNetS and DispNet in terms of
estimation accuracy and training times.
A Minesweeper Solver Using Logic Inference, CSP and Sampling
Authors: Yimin Tang, Tian Jiang, Yanpeng Hu
Date: 2018-10-07 00:00:00

Minesweeper as a puzzle video game and is proved that it is an NPC problem.
We use CSP, Logic Inference and Sampling to make a minesweeper solver and we
limit us each select in 5 seconds.
Assessing Crosslingual Discourse Relations in Machine Translation
Authors: Karin Sim Smith, Lucia Specia
Date: 2018-10-07 00:00:00
Tasks: Machine Translation, Translation

In an attempt to improve overall translation quality, there has been an
increasing focus on integrating more linguistic elements into Machine
Translation (MT). While significant progress has been achieved, especially
recently with neural models, automatically evaluating the output of such
systems is still an open problem. Current practice in MT evaluation relies on a
single reference translation, even though there are many ways of translating a
particular text, and it tends to disregard higher level information such as
discourse. We propose a novel approach that assesses the translated output
based on the source text rather than the reference translation, and measures
the extent to which the semantics of the discourse elements (discourse
relations, in particular) in the source text are preserved in the MT output.
The challenge is to detect the discourse relations in the source text and
determine whether these relations are correctly transferred crosslingually to
the target language -- without a reference translation. This methodology could
be used independently for discourse-level evaluation, or as a component in
other metrics, at a time where substantial amounts of MT are online and would
benefit from evaluation where the source text serves as a benchmark.
Real-Time Workload Classification during Driving using HyperNetworks
Authors: Ruohan Wang, Pierluigi V. Amadori, Yiannis Demiris
Date: 2018-10-07 00:00:00
Tasks: Classification, General Classification

Classifying human cognitive states from behavioral and physiological signals
is a challenging problem with important applications in robotics. The problem
is challenging due to the data variability among individual users, and sensor
artefacts. In this work, we propose an end-to-end framework for real-time
cognitive workload classification with mixture Hyper Long Short Term Memory
Networks, a novel variant of HyperNetworks. Evaluating the proposed approach on
an eye-gaze pattern dataset collected from simulated driving scenarios of
different cognitive demands, we show that the proposed framework outperforms
previous baseline methods and achieves 83.9\% precision and 87.8\% recall
during test. We also demonstrate the merit of our proposed architecture by
showing improved performance over other LSTM-based methods.
Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifier
Authors: Jelmer M. Wolterink, Robbert W. van Hamersvelt, Max A. Viergever, Tim Leiner, Ivana Išgum
Date: 2018-10-07 00:00:00

Coronary artery centerline extraction in cardiac CT angiography (CCTA) images
is a prerequisite for evaluation of stenoses and atherosclerotic plaque. We
propose an algorithm that extracts coronary artery centerlines in CCTA using a
convolutional neural network (CNN).
  A 3D dilated CNN is trained to predict the most likely direction and radius
of an artery at any given point in a CCTA image based on a local image patch.
Starting from a single seed point placed manually or automatically anywhere in
a coronary artery, a tracker follows the vessel centerline in two directions
using the predictions of the CNN. Tracking is terminated when no direction can
be identified with high certainty.
  The CNN was trained using 32 manually annotated centerlines in a training set
consisting of 8 CCTA images provided in the MICCAI 2008 Coronary Artery
Tracking Challenge (CAT08). Evaluation using 24 test images of the CAT08
challenge showed that extracted centerlines had an average overlap of 93.7%
with 96 manually annotated reference centerlines. Extracted centerline points
were highly accurate, with an average distance of 0.21 mm to reference
centerline points. In a second test set consisting of 50 CCTA scans, 5,448
markers in the coronary arteries were used as seed points to extract single
centerlines. This showed strong correspondence between extracted centerlines
and manually placed markers. In a third test set containing 36 CCTA scans,
fully automatic seeding and centerline extraction led to extraction of on
average 92% of clinically relevant coronary artery segments.
  The proposed method is able to accurately and efficiently determine the
direction and radius of coronary arteries. The method can be trained with
limited training data, and once trained allows fast automatic or interactive
extraction of coronary artery trees from CCTA images.
Accelerating Stochastic Gradient Descent Using Antithetic Sampling
Authors: Jingchang Liu, Linli Xu
Date: 2018-10-07 00:00:00
Tasks: Binary Classification, General Classification

(Mini-batch) Stochastic Gradient Descent is a popular optimization method
which has been applied to many machine learning applications. But a rather high
variance introduced by the stochastic gradient in each step may slow down the
convergence. In this paper, we propose the antithetic sampling strategy to
reduce the variance by taking advantage of the internal structure in dataset.
Under this new strategy, stochastic gradients in a mini-batch are no longer
independent but negatively correlated as much as possible, while the mini-batch
stochastic gradient is still an unbiased estimator of full gradient. For the
binary classification problems, we just need to calculate the antithetic
samples in advance, and reuse the result in each iteration, which is practical.
Experiments are provided to confirm the effectiveness of the proposed method.
European Court of Human Right Open Data project
Authors: Alexandre Quemy
Date: 2018-10-07 00:00:00

This paper presents thirteen datasets for binary, multiclass and multilabel
classification based on the European Court of Human Rights judgments since its
creation. The interest of such datasets is explained through the prism of the
researcher, the data scientist, the citizen and the legal practitioner.
Contrarily to many datasets, the creation process, from the collection of raw
data to the feature transformation, is provided under the form of a collection
of fully automated and open-source scripts. It ensures reproducibility and a
high level of confidence in the processed data, which is some of the most
important issues in data governance nowadays. A first experimental campaign is
performed to study some predictability properties and to establish baseline
results on popular machine learning algorithms. The results are consistently
good across the binary datasets with an accuracy comprised between 75.86% and
98.32% for an average accuracy of 96.45%.
ASVRG: Accelerated Proximal SVRG
Authors: Fanhua Shang, Licheng Jiao, Kaiwen Zhou, James Cheng, Yan Ren, Yufei Jin
Date: 2018-10-07 00:00:00

This paper proposes an accelerated proximal stochastic variance reduced
gradient (ASVRG) method, in which we design a simple and effective momentum
acceleration trick. Unlike most existing accelerated stochastic variance
reduction methods such as Katyusha, ASVRG has only one additional variable and
one momentum parameter. Thus, ASVRG is much simpler than those methods, and has
much lower per-iteration complexity. We prove that ASVRG achieves the best
known oracle complexities for both strongly convex and non-strongly convex
objectives. In addition, we extend ASVRG to mini-batch and non-smooth settings.
We also empirically verify our theoretical results and show that the
performance of ASVRG is comparable with, and sometimes even better than that of
the state-of-the-art stochastic methods.
Graphlet Count Estimation via Convolutional Neural Networks
Authors: Xutong Liu, Yu-Zhen Janice Chen, John C. S. Lui, Konstantin Avrachenkov
Date: 2018-10-07 00:00:00

Graphlets are defined as k-node connected induced subgraph patterns. For an
undirected graph, 3-node graphlets include close triangle and open triangle.
When k = 4, there are six types of graphlets, e.g., tailed-triangle and clique
are two possible 4-node graphlets. The number of each graphlet, called graphlet
count, is a signature which characterizes the local network structure of a
given graph. Graphlet count plays a prominent role in network analysis of many
fields, most notably bioinformatics and social science.
  However, computing exact graphlet count is inherently difficult and
computational expensive because the number of graphlets grows exponentially
large as the graph size and/or graphlet size k grow. To deal with this
difficulty, many sampling methods were proposed to estimate graphlet count with
bounded error. Nevertheless, these methods require large number of samples to
be statistically reliable, which is still computationally demanding. Moreover,
they have to repeat laborious counting procedure even if a new graph is similar
or exactly the same as previous studied graphs.
  Intuitively, learning from historic graphs can make estimation more accurate
and avoid many repetitive counting to reduce computational cost. Based on this
idea, we propose a convolutional neural network (CNN) framework and two
preprocessing techniques to estimate graphlet count. Extensive experiments on
two types of random graphs and real world biochemistry graphs show that our
framework can offer substantial speedup on estimating graphlet count of new
graphs with high accuracy.
DeepGeo: Photo Localization with Deep Neural Network
Authors: Sudharshan Suresh, Nathaniel Chodosh, Montiel Abello
Date: 2018-10-07 00:00:00

In this paper we address the task of determining the geographical location of
an image, a pertinent problem in learning and computer vision. This research
was inspired from playing GeoGuessr, a game that tests a humans' ability to
localize themselves using just images of their surroundings. In particular, we
wish to investigate how geographical, ecological and man-made features
generalize for random location prediction. This is framed as a classification
problem: given images sampled from the USA, the most-probable state among 50 is
predicted. Previous work uses models extensively trained on large, unfiltered
online datasets that are primed towards specific locations. To this end, we
create (and open-source) the 50States10K dataset - with 0.5 million Google
Street View images of the country. A deep neural network based on the ResNet
architecture is trained, and four different strategies of incorporating
low-level cardinality information are presented. This model achieves an
accuracy 20 times better than chance on a test dataset, which rises to 71.87%
when taking the best of top-5 guesses. The network also beats human subjects in
4 out of 5 rounds of GeoGuessr.
Online Center of Mass Estimation for a Humanoid Wheeled Inverted Pendulum Robot
Authors: Munzir Zafar, Akash Patel, Bogdan Vlahov, Nathaniel Glaser, Sergio Aguillera, Seth Hutchinson
Date: 2018-10-07 00:00:00

We present a novel application of robust control and online learning for the balancing of a n Degree of Freedom (DoF), Wheeled Inverted Pendulum (WIP) humanoid robot. Our technique condenses the inaccuracies of a mass model into a Center of Mass (CoM) error, balances despite this error, and uses online learning to update the mass model for a better CoM estimate. Using a simulated model of our robot, we meta-learn a set of excitory joint poses that makes our gradient descent algorithm quickly converge to an accurate (CoM) estimate. This simulated pipeline executes in a fully online fashion, using active disturbance rejection to address the mass errors that result from a steadily evolving mass model. Experiments were performed on a 19 DoF WIP, in which we manually acquired the data for the learned set of poses and show that the mass model produced by a gradient descent produces a CoM estimate that improves overall control and efficiency. This work contributes to a greater corpus of whole body control on the Golem Krang humanoid robot.
Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detection
Authors: Yao Xue, Gilbert Bigras, Judith Hugh, Nilanjan Ray
Date: 2018-10-07 00:00:00
Tasks: Cell Detection, compressed sensing, object-detection, Object Detection

Automated cell detection and localization from microscopy images are
significant tasks in biomedical research and clinical practice. In this paper,
we design a new cell detection and localization algorithm that combines deep
convolutional neural network (CNN) and compressed sensing (CS) or sparse coding
(SC) for end-to-end training. We also derive, for the first time, a
backpropagation rule, which is applicable to train any algorithm that
implements a sparse code recovery layer. The key observation behind our
algorithm is that cell detection task is a point object detection task in
computer vision, where the cell centers (i.e., point objects) occupy only a
tiny fraction of the total number of pixels in an image. Thus, we can apply
compressed sensing (or, equivalently sparse coding) to compactly represent a
variable number of cells in a projected space. Then, CNN regresses this
compressed vector from the input microscopy image. Thanks to the SC/CS recovery
algorithm (L1 optimization) that can recover sparse cell locations from the
output of CNN. We train this entire processing pipeline end-to-end and
demonstrate that end-to-end training provides accuracy improvements over a
training paradigm that treats CNN and CS-recovery layers separately. Our
algorithm design also takes into account a form of ensemble average of trained
models naturally to further boost accuracy of cell detection. We have validated
our algorithm on benchmark datasets and achieved excellent performances.
Spatio-temporal Edge Service Placement: A Bandit Learning Approach
Authors: Lixing Chen, Jie Xu, Shaolei Ren, Pan Zhou
Date: 2018-10-07 00:00:00
Tasks: Decision Making, Edge-computing

Shared edge computing platforms deployed at the radio access network are
expected to significantly improve quality of service delivered by Application
Service Providers (ASPs) in a flexible and economic way. However, placing edge
service in every possible edge site by an ASP is practically infeasible due to
the ASP's prohibitive budget requirement. In this paper, we investigate the
edge service placement problem of an ASP under a limited budget, where the ASP
dynamically rents computing/storage resources in edge sites to host its
applications in close proximity to end users. Since the benefit of placing edge
service in a specific site is usually unknown to the ASP a priori, optimal
placement decisions must be made while learning this benefit. We pose this
problem as a novel combinatorial contextual bandit learning problem. It is
"combinatorial" because only a limited number of edge sites can be rented to
provide the edge service given the ASP's budget. It is "contextual" because we
utilize user context information to enable finer-grained learning and decision
making. To solve this problem and optimize the edge computing performance, we
propose SEEN, a Spatial-temporal Edge sErvice placemeNt algorithm. Furthermore,
SEEN is extended to scenarios with overlapping service coverage by
incorporating a disjunctively constrained knapsack problem. In both cases, we
prove that our algorithm achieves a sublinear regret bound when it is compared
to an oracle algorithm that knows the exact benefit information. Simulations
are carried out on a real-world dataset, whose results show that SEEN
significantly outperforms benchmark solutions.
Geometric Scattering for Graph Data Analysis
Authors: Feng Gao, Guy Wolf, Matthew Hirn
Date: 2018-10-07 00:00:00
Proceeding: ICLR 2019 5
Tasks: General Classification, Graph Classification, Image Classification

We explore the generalization of scattering transforms from traditional
(e.g., image or audio) signals to graph data, analogous to the generalization
of ConvNets in geometric deep learning, and the utility of extracted graph
features in graph data analysis. In particular, we focus on the capacity of
these features to retain informative variability and relations in the data
(e.g., between individual graphs, or in aggregate), while relating our
construction to previous theoretical results that establish the stability of
similar transforms to families of graph deformations. We demonstrate the
application the our geometric scattering features in graph classification of
social network data, and in data exploration of biochemistry data.
Geocoding Without Geotags: A Text-based Approach for reddit
Authors: Keith Harrigian
Date: 2018-10-07 00:00:00
Proceeding: WS 2018 11

In this paper, we introduce the first geolocation inference approach for
reddit, a social media platform where user pseudonymity has thus far made
supervised demographic inference difficult to implement and validate. In
particular, we design a text-based heuristic schema to generate ground truth
location labels for reddit users in the absence of explicitly geotagged data.
After evaluating the accuracy of our labeling procedure, we train and test
several geolocation inference models across our reddit data set and three
benchmark Twitter geolocation data sets. Ultimately, we show that geolocation
models trained and applied on the same domain substantially outperform models
attempting to transfer training data across domains, even more so on reddit
where platform-specific interest-group metadata can be used to improve
inferences.
Deep Model-Based 6D Pose Refinement in RGB
Authors: Fabian Manhardt, Wadim Kehl, Nassir Navab, Federico Tombari
Date: 2018-10-07 00:00:00
Proceeding: ECCV 2018 9
Tasks: Pose Tracking

We present a novel approach for model-based 6D pose refinement in color data.
Building on the established idea of contour-based pose tracking, we teach a
deep neural network to predict a translational and rotational update. At the
core, we propose a new visual loss that drives the pose update by aligning
object contours, thus avoiding the definition of any explicit appearance model.
In contrast to previous work our method is correspondence-free,
segmentation-free, can handle occlusion and is agnostic to geometrical symmetry
as well as visual ambiguities. Additionally, we observe a strong robustness
towards rough initialization. The approach can run in real-time and produces
pose accuracies that come close to 3D ICP without the need for depth data.
Furthermore, our networks are trained from purely synthetic data and will be
published together with the refinement code to ensure reproducibility.
CSI-Net: Unified Human Body Characterization and Pose Recognition
Authors: Fei Wang, Jinsong Han, Shiyuan Zhang, Xu He, Dong Huang
Date: 2018-10-07 00:00:00
Tasks: Person Recognition, Temporal Action Localization

We build CSI-Net, a unified Deep Neural Network~(DNN), to learn the
representation of WiFi signals. Using CSI-Net, we jointly solved two body
characterization problems: biometrics estimation (including body fat, muscle,
water, and bone rates) and person recognition. We also demonstrated the
application of CSI-Net on two distinctive pose recognition tasks: the hand sign
recognition (fine-scaled action of the hand) and falling detection
(coarse-scaled motion of the body).
Solving Large Sequential Games with the Excessive Gap Technique
Authors: Christian Kroer, Gabriele Farina, Tuomas Sandholm
Date: 2018-10-07 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: counterfactual, Form, GPU

There has been tremendous recent progress on equilibrium-finding algorithms
for zero-sum imperfect-information extensive-form games, but there has been a
puzzling gap between theory and practice. First-order methods have
significantly better theoretical convergence rates than any
counterfactual-regret minimization (CFR) variant. Despite this, CFR variants
have been favored in practice. Experiments with first-order methods have only
been conducted on small- and medium-sized games because those methods are
complicated to implement in this setting, and because CFR variants have been
enhanced extensively for over a decade they perform well in practice. In this
paper we show that a particular first-order method, a state-of-the-art variant
of the excessive gap technique---instantiated with the dilated entropy distance
function---can efficiently solve large real-world problems competitively with
CFR and its variants. We show this on large endgames encountered by the
Libratus poker AI, which recently beat top human poker specialist professionals
at no-limit Texas hold'em. We show experimental results on our variant of the
excessive gap technique as well as a prior version. We introduce a numerically
friendly implementation of the smoothed best response computation associated
with first-order methods for extensive-form game solving. We present, to our
knowledge, the first GPU implementation of a first-order method for
extensive-form games. We present comparisons of several excessive gap technique
and CFR variants.