pwc-archive/papers-with-abstracts huggingface dataset:
A Semi-Supervised and Inductive Embedding Model for Churn Prediction of Large-Scale Mobile Games
Authors: Xi Liu, Muhe Xie, Xidao Wen, Rui Chen, Yong Ge, Nick Duffield, Na Wang
Date: 2018-08-20 00:00:00
Tasks: Attribute

Mobile gaming has emerged as a promising market with billion-dollar revenues.
A variety of mobile game platforms and services have been developed around the
world. One critical challenge for these platforms and services is to understand
user churn behavior in mobile games. Accurate churn prediction will benefit
many stakeholders such as game developers, advertisers, and platform operators.
In this paper, we present the first large-scale churn prediction solution for
mobile games. In view of the common limitations of the state-of-the-art methods
built upon traditional machine learning models, we devise a novel
semi-supervised and inductive embedding model that jointly learns the
prediction function and the embedding function for user-app relationships. We
model these two functions by deep neural networks with a unique edge embedding
technique that is able to capture both contextual information and relationship
dynamics. We also design a novel attributed random walk technique that takes
into consideration both topological adjacency and attribute similarities. To
evaluate the performance of our solution, we collect real-world data from the
Samsung Game Launcher platform that includes tens of thousands of games and
hundreds of millions of user-app interactions. The experimental results with
this data demonstrate the superiority of our proposed model against existing
state-of-the-art methods.
Detecting cognitive impairments by agreeing on interpretations of linguistic features
Authors: Zining Zhu, Jekaterina Novikova, Frank Rudzicz
Date: 2018-08-20 00:00:00
Proceeding: NAACL 2019 6

Linguistic features have shown promising applications for detecting various
cognitive impairments. To improve detection accuracies, increasing the amount
of data or the number of linguistic features have been two applicable
approaches. However, acquiring additional clinical data can be expensive, and
hand-crafting features is burdensome. In this paper, we take a third approach,
proposing Consensus Networks (CNs), a framework to classify after reaching
agreements between modalities. We divide linguistic features into
non-overlapping subsets according to their modalities, and let neural networks
learn low-dimensional representations that agree with each other. These
representations are passed into a classifier network. All neural networks are
optimized iteratively.
  In this paper, we also present two methods that improve the performance of
CNs. We then present ablation studies to illustrate the effectiveness of
modality division. To understand further what happens in CNs, we visualize the
representations during training. Overall, using all of the 413 linguistic
features, our models significantly outperform traditional classifiers, which
are used by the state-of-the-art papers.
Multi-View Graph Embedding Using Randomized Shortest Paths
Authors: Anuththari Gamage, Brian Rappaport, Shuchin Aeron, Xiaozhe Hu
Date: 2018-08-20 00:00:00
Tasks: Clustering, Graph Embedding

Real-world data sets often provide multiple types of information about the
same set of entities. This data is well represented by multi-view graphs, which
consist of several distinct sets of edges over the same nodes. These can be
used to analyze how entities interact from different viewpoints. Combining
multiple views improves the quality of inferences drawn from the underlying
data, which has increased interest in developing efficient multi-view graph
embedding methods. We propose an algorithm, C-RSP, that generates a common (C)
embedding of a multi-view graph using Randomized Shortest Paths (RSP). This
algorithm generates a dissimilarity measure between nodes by minimizing the
expected cost of a random walk between any two nodes across all views of a
multi-view graph, in doing so encoding both the local and global structure of
the graph. We test C-RSP on both real and synthetic data and show that it
outperforms benchmark algorithms at embedding and clustering tasks while
remaining computationally efficient.
Triangle Lasso for Simultaneous Clustering and Optimization in Graph Datasets
Authors: Yawei Zhao, Kai Xu, Xinwang Liu, En Zhu, Xinzhong Zhu, Jianping Yin
Date: 2018-08-20 00:00:00
Tasks: Clustering, Missing Values

Recently, network lasso has drawn many attentions due to its remarkable
performance on simultaneous clustering and optimization. However, it usually
suffers from the imperfect data (noise, missing values etc), and yields
sub-optimal solutions. The reason is that it finds the similar instances
according to their features directly, which is usually impacted by the
imperfect data, and thus returns sub-optimal results. In this paper, we propose
triangle lasso to avoid its disadvantage. Triangle lasso finds the similar
instances according to their neighbours. If two instances have many common
neighbours, they tend to become similar. Although some instances are profiled
by the imperfect data, it is still able to find the similar counterparts.
Furthermore, we develop an efficient algorithm based on Alternating Direction
Method of Multipliers (ADMM) to obtain a moderately accurate solution. In
addition, we present a dual method to obtain the accurate solution with the low
additional time consumption. We demonstrate through extensive numerical
experiments that triangle lasso is robust to the imperfect data. It usually
yields a better performance than the state-of-the-art method when performing
data analysis tasks in practical scenarios.
Adaptive Document Retrieval for Deep Question Answering
Authors: Bernhard Kratzwald, Stefan Feuerriegel
Date: 2018-08-20 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Question Answering, Retrieval

State-of-the-art systems in deep question answering proceed as follows: (1)
an initial document retrieval selects relevant documents, which (2) are then
processed by a neural network in order to extract the final answer. Yet the
exact interplay between both components is poorly understood, especially
concerning the number of candidate documents that should be retrieved. We show
that choosing a static number of documents -- as used in prior research --
suffers from a noise-information trade-off and yields suboptimal results. As a
remedy, we propose an adaptive document retrieval model. This learns the
optimal candidate number for document retrieval, conditional on the size of the
corpus and the query. We report extensive experimental results showing that our
adaptive approach outperforms state-of-the-art methods on multiple benchmark
datasets, as well as in the context of corpora with variable sizes.
CU-Net: Coupled U-Nets
Authors: Zhiqiang Tang, Xi Peng, Shijie Geng, Yizhe Zhu, Dimitris N. Metaxas
Date: 2018-08-20 00:00:00
Tasks: Pose Estimation

We design a new connectivity pattern for the U-Net architecture. Given
several stacked U-Nets, we couple each U-Net pair through the connections of
their semantic blocks, resulting in the coupled U-Nets (CU-Net). The coupling
connections could make the information flow more efficiently across U-Nets. The
feature reuse across U-Nets makes each U-Net very parameter efficient. We
evaluate the coupled U-Nets on two benchmark datasets of human pose estimation.
Both the accuracy and model parameter number are compared. The CU-Net obtains
comparable accuracy as state-of-the-art methods. However, it only has at least
60% fewer parameters than other approaches.
Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
Authors: Mauricio Orbes-Arteaga, M. Jorge Cardoso, Lauge Sørensen, Marc Modat, Sébastien Ourselin, Mads Nielsen, Akshay Pai
Date: 2018-08-20 00:00:00
Tasks: Imputation, Segmentation

Segmenting vascular pathologies such as white matter lesions in Brain
magnetic resonance images (MRIs) require acquisition of multiple sequences such
as T1-weighted (T1-w) --on which lesions appear hypointense-- and fluid
attenuated inversion recovery (FLAIR) sequence --where lesions appear
hyperintense--. However, most of the existing retrospective datasets do not
consist of FLAIR sequences. Existing missing modality imputation methods
separate the process of imputation, and the process of segmentation. In this
paper, we propose a method to link both modality imputation and segmentation
using convolutional neural networks. We show that by jointly optimizing the
imputation network and the segmentation network, the method not only produces
more realistic synthetic FLAIR images from T1-w images, but also improves the
segmentation of WMH from T1-w images only.
Single-View Place Recognition under Seasonal Changes
Authors: Daniel Olid, José M. Fácil, Javier Civera
Date: 2018-08-20 00:00:00
Tasks: Autonomous Navigation, Triplet

Single-view place recognition, that we can define as finding an image that
corresponds to the same place as a given query image, is a key capability for
autonomous navigation and mapping. Although there has been a considerable
amount of research in the topic, the high degree of image variability (with
viewpoint, illumination or occlusions for example) makes it a research
challenge.
  One of the particular challenges, that we address in this work, is weather
variation. Seasonal changes can produce drastic appearance changes, that
classic low-level features do not model properly. Our contributions in this
paper are twofold. First we pre-process and propose a partition for the
Nordland dataset, frequently used for place recognition research without
consensus on the partitions. And second, we evaluate several neural network
architectures such as pre-trained, siamese and triplet for this problem. Our
best results outperform the state of the art of the field. A video showing our
results can be found in https://youtu.be/VrlxsYZoHDM. The partitioned version
of the Nordland dataset at http://webdiis.unizar.es/~jmfacil/pr-nordland/.
State-of-the-art Chinese Word Segmentation with Bi-LSTMs
Authors: Ji Ma, Kuzman Ganchev, David Weiss
Date: 2018-08-20 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Chinese Word Segmentation

A wide variety of neural-network architectures have been proposed for the
task of Chinese word segmentation.
  Surprisingly, we find that a bidirectional LSTM model, when combined with
standard deep learning techniques and best practices, can achieve better
accuracy on many of the popular datasets as compared to models based on more
complex neural-network architectures.
  Furthermore, our error analysis shows that out-of-vocabulary words remain
challenging for neural-network models, and many of the remaining errors are
unlikely to be fixed through architecture changes.
  Instead, more effort should be made on exploring resources for further
improvement.
Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies
Authors: Alessandro Achille, Tom Eccles, Loic Matthey, Christopher P. Burgess, Nick Watters, Alexander Lerchner, Irina Higgins
Date: 2018-08-20 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Representation Learning

Intelligent behaviour in the real-world requires the ability to acquire new
knowledge from an ongoing sequence of experiences while preserving and reusing
past knowledge. We propose a novel algorithm for unsupervised representation
learning from piece-wise stationary visual data: Variational Autoencoder with
Shared Embeddings (VASE). Based on the Minimum Description Length principle,
VASE automatically detects shifts in the data distribution and allocates spare
representational capacity to new knowledge, while simultaneously protecting
previously learnt representations from catastrophic forgetting. Our approach
encourages the learnt representations to be disentangled, which imparts a
number of desirable properties: VASE can deal sensibly with ambiguous inputs,
it can enhance its own representations through imagination-based exploration,
and most importantly, it exhibits semantically meaningful sharing of latents
between different datasets. Compared to baselines with entangled
representations, our approach is able to reason beyond surface-level statistics
and perform semantically meaningful cross-domain inference.
Goal-oriented Dialogue Policy Learning from Failures
Authors: Keting Lu, Shiqi Zhang, Xiaoping Chen
Date: 2018-08-20 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning methods have been used for learning dialogue policies.
However, learning an effective dialogue policy frequently requires
prohibitively many conversations. This is partly because of the sparse rewards
in dialogues, and the very few successful dialogues in early learning phase.
Hindsight experience replay (HER) enables learning from failures, but the
vanilla HER is inapplicable to dialogue learning due to the implicit goals. In
this work, we develop two complex HER methods providing different trade-offs
between complexity and performance, and, for the first time, enabled HER-based
dialogue policy learning. Experiments using a realistic user simulator show
that our HER methods perform better than existing experience replay methods (as
applied to deep Q-networks) in learning rate.
Translational Motion Compensation for Soft Tissue Velocity Images
Authors: Christina Koutsoumpa, Jennifer Keegan, David Firmin, Guang-Zhong Yang, Duncan Gillies
Date: 2018-08-20 00:00:00
Tasks: Motion Compensation

Purpose: Advancements in MRI Tissue Phase Velocity Mapping (TPM) allow for
the acquisition of higher quality velocity cardiac images providing better
assessment of regional myocardial deformation for accurate disease diagnosis,
pre-operative planning and post-operative patient surveillance. Translation of
TPM velocities from the scanner's reference coordinate system to the regional
cardiac coordinate system requires decoupling of translational motion and
motion due to myocardial deformation. Despite existing techniques for
respiratory motion compensation in TPM, there is still a remaining
translational velocity component due to the global motion of the beating heart.
To compensate for translational motion in cardiac TPM, we propose an
image-processing method, which we have evaluated on synthetic data and applied
on in vivo TPM data. Methods: Translational motion is estimated from a suitable
region of velocities automatically defined in the left-ventricular volume. The
region is generated by dilating the medial axis of myocardial masks in each
slice and the translational velocity is estimated by integration in this
region. The method was evaluated on synthetic data and in vivo data corrupted
with a translational velocity component (200% of the maximum measured
velocity). Accuracy and robustness were examined and the method was applied on
10 in vivo datasets. Results: The results from synthetic and in vivo corrupted
data show excellent performance with an estimation error less than 0.3% and
high robustness in both cases. The effectiveness of the method is confirmed
with visual observation of results from the 10 datasets. Conclusion: The
proposed method is accurate and suitable for translational motion correction of
the left ventricular velocity fields. The current method for translational
motion compensation could be applied to any annular contracting (tissue)
structure.
Towards Fine Grained Network Flow Prediction
Authors: Patrick Jahnke, Emmanuel Stapf, Jonas Mieseler, Gerhard Neumann, Patrick Eugster
Date: 2018-08-20 00:00:00
Tasks: Prediction, Traffic Prediction

One main challenge for the design of networks is that traffic load is not
generally known in advance. This makes it hard to adequately devote resources
such as to best prevent or mitigate bottlenecks. While several authors have
shown how to predict traffic in a coarse grained manner by aggregating flows,
fine grained prediction of traffic at the level of individual flows, including
bursty traffic, is widely considered to be impossible. This paper shows, to the
best of our knowledge, the first approach to fine grained per flow traffic
prediction. In short, we introduce the Frequency-based Kernel Kalman Filter
(FKKF), which predicts individual flows' behavior based on measurements. Our
FKKF relies on the well known Kalman Filter in combination with a kernel to
support the prediction of non linear functions. Furthermore we change the
operating space from time to frequency space. In this space, into which we
transform the input data via a Short-Time Fourier Transform (STFT), the peak
structures of flows can be predicted after gleaning their key characteristics,
with a Principal Component Analysis (PCA), from past and ongoing flows that
stem from the same socket-to-socket connection. We demonstrate the
effectiveness of our approach on popular benchmark traces from a university
data center. Our approach predicts traffic on average across 17 out of 20
groups of flows with an average prediction error of 6.43% around 0.49 (average)
seconds in advance, whilst existing coarse grained approaches exhibit
prediction errors of 77% at best.
Reproducible evaluation of classification methods in Alzheimer's disease: framework and application to MRI and PET data
Authors: Jorge Samper-González, Ninon Burgos, Simona Bottani, Sabrina Fontanella, Pascal Lu, Arnaud Marcoux, Alexandre Routier, Jérémy Guillon, Michael Bacci, Junhao Wen, Anne Bertrand, Hugo Bertin, Marie-Odile Habert, Stanley Durrleman, Theodoros Evgeniou, Olivier Colliot, for the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers, Lifestyle flagship study of ageing
Date: 2018-08-20 00:00:00
Tasks: Benchmarking, Classification, General Classification, image smoothing

A large number of papers have introduced novel machine learning and feature
extraction methods for automatic classification of AD. However, they are
difficult to reproduce because key components of the validation are often not
readily available. These components include selected participants and input
data, image preprocessing and cross-validation procedures. The performance of
the different approaches is also difficult to compare objectively. In
particular, it is often difficult to assess which part of the method provides a
real improvement, if any. We propose a framework for reproducible and objective
classification experiments in AD using three publicly available datasets (ADNI,
AIBL and OASIS). The framework comprises: i) automatic conversion of the three
datasets into BIDS format, ii) a modular set of preprocessing pipelines,
feature extraction and classification methods, together with an evaluation
framework, that provide a baseline for benchmarking the different components.
We demonstrate the use of the framework for a large-scale evaluation on 1960
participants using T1 MRI and FDG PET data. In this evaluation, we assess the
influence of different modalities, preprocessing, feature types, classifiers,
training set sizes and datasets. Performances were in line with the
state-of-the-art. FDG PET outperformed T1 MRI for all classification tasks. No
difference in performance was found for the use of different atlases, image
smoothing, partial volume correction of FDG PET images, or feature type. Linear
SVM and L2-logistic regression resulted in similar performance and both
outperformed random forests. The classification performance increased along
with the number of subjects used for training. Classifiers trained on ADNI
generalized well to AIBL and OASIS. All the code of the framework and the
experiments is publicly available at:
https://gitlab.icm-institute.org/aramislab/AD-ML.
Synthetic Patient Generation: A Deep Learning Approach Using Variational Autoencoders
Authors: Ally Salim Jr
Date: 2018-08-20 00:00:00
Tasks: Deep Learning

Artificial Intelligence in healthcare is a new and exciting frontier and the
possibilities are endless. With deep learning approaches beating human
performances in many areas, the logical next step is to attempt their
application in the health space. For these and other Machine Learning
approaches to produce good results and have their potential realized, the need
for, and importance of, large amounts of accurate data is second to none. This
is a challenge faced by many industries and more so in the healthcare space. We
present an approach of using Variational Autoencoders (VAE's) as an approach to
generating more data for training deeper networks, as well as uncovering
underlying patterns in diagnoses and the patients suffering from them. By
training a VAE, on available data, it was able to learn the latent distribution
of the patient features given the diagnosis. It is then possible, after
training, to sample from the learnt latent distribution to generate new
accurate patient records given the patient diagnosis.
CapsDeMM: Capsule network for Detection of Munro's Microabscess in skin biopsy images
Authors: Anabik Pal, Akshay Chaturvedi, Utpal Garain, Aditi Chandra, Raghunath Chatterjee, Swapan Senapati
Date: 2018-08-20 00:00:00
Tasks: General Classification

This paper presents an approach for automatic detection of Munro's
Microabscess in stratum corneum (SC) of human skin biopsy in order to realize a
machine assisted diagnosis of Psoriasis. The challenge of detecting neutrophils
in presence of nucleated cells is solved using the recent advances of deep
learning algorithms. Separation of SC layer, extraction of patches from the
layer followed by classification of patches with respect to presence or absence
of neutrophils form the basis of the overall approach which is effected through
an integration of a U-Net based segmentation network and a capsule network for
classification. The novel design of the present capsule net leads to a drastic
reduction in the number of parameters without any noticeable compromise in the
overall performance. The research further addresses the challenge of dealing
with Mega-pixel images (in 10X) vis-a-vis Giga-pixel ones (in 40X). The
promising result coming out of an experiment on a dataset consisting of 273
real-life images shows that a practical system is possible based on the present
research. The implementation of our system is available at
https://github.com/Anabik/CapsDeMM.
What Stands-in for a Missing Tool? A Prototypical Grounded Knowledge-based Approach to Tool Substitution
Authors: Madhura Thosar, Christian A. Mueller, Sebastian Zug
Date: 2018-08-20 00:00:00

When a robot is operating in a dynamic environment, it cannot be assumed that
a tool required to solve a given task will always be available. In case of a
missing tool, an ideal response would be to find a substitute to complete the
task. In this paper, we present a proof of concept of a grounded
knowledge-based approach to tool substitution. In order to validate the
suitability of a substitute, we conducted experiments involving 22 substitution
scenarios. The substitutes computed by the proposed approach were validated on
the basis of the experts' choices for each scenario. Our evaluation showed, in
20 out of 22 scenarios (91%), the approach identified the same substitutes as
experts.