pwc-archive/papers-with-abstracts huggingface dataset:
A Deep Learning Mechanism for Efficient Information Dissemination in Vehicular Floating Content
Authors: Gaetano Manzo, Juan Sebastian Otálora Montenegro, Gianluca Rizzo
Date: 2018-10-24 00:00:00

Handling the tremendous amount of network data, produced by the explosive
growth of mobile traffic volume, is becoming of main priority to achieve
desired performance targets efficiently. Opportunistic communication such as
FloatingContent (FC), can be used to offload part of the cellular traffic
volume to vehicular-to-vehicular communication (V2V), leaving the
infrastructure the task of coordinating the communication. Existing FC
dimensioning approaches have limitations, mainly due to unrealistic assumptions
and on a coarse partitioning of users, which results in over-dimensioning.
Shaping the opportunistic communication area is a crucial task to achieve
desired application performance efficiently. In this work, we propose a
solution for this open challenge. In particular, the broadcasting areas called
Anchor Zone (AZ), are selected via a deep learning approach to minimize
communication resources achieving desired message availability. No assumption
required to fit the classifier in both synthetic and real mobility. A numerical
study is made to validate the effectiveness and efficiency of the proposed
method. The predicted AZ configuration can achieve an accuracy of 89.7%within
98% of confidence level. By cause of the learning approach, the method performs
even better in real scenarios, saving up to 27% of resources compared to
previous work analytically modelled
Effective extractive summarization using frequency-filtered entity relationship graphs
Authors: Archit Sakhadeo, Nisheeth Srivastava
Date: 2018-10-24 00:00:00
Tasks: Extractive Summarization

Word frequency-based methods for extractive summarization are easy to
implement and yield reasonable results across languages. However, they have
significant limitations - they ignore the role of context, they offer uneven
coverage of topics in a document, and sometimes are disjointed and hard to
read. We use a simple premise from linguistic typology - that English sentences
are complete descriptors of potential interactions between entities, usually in
the order subject-verb-object - to address a subset of these difficulties. We
have developed a hybrid model of extractive summarization that combines
word-frequency based keyword identification with information from automatically
generated entity relationship graphs to select sentences for summaries.
Comparative evaluation with word-frequency and topic word-based methods shows
that the proposed method is competitive by conventional ROUGE standards, and
yields moderately more informative summaries on average, as assessed by a large
panel (N=94) of human raters.
Image-based Natural Language Understanding Using 2D Convolutional Neural Networks
Authors: Erinc Merdivan, Anastasios Vafeiadis, Dimitrios Kalatzis, Sten Hanke, Johannes Kropf, Konstantinos Votis, Dimitrios Giakoumis, Dimitrios Tzovaras, Liming Chen, Raouf Hamzaoui, Matthieu Geist
Date: 2018-10-24 00:00:00
Tasks: Classification, General Classification, Natural Language Understanding, Optical Character Recognition, Optical Character Recognition (OCR), text-classification, Text Classification

We propose a new approach to natural language understanding in which we
consider the input text as an image and apply 2D Convolutional Neural Networks
to learn the local and global semantics of the sentences from the variations
ofthe visual patterns of words. Our approach demonstrates that it is possible
to get semantically meaningful features from images with text without using
optical character recognition and sequential processing pipelines, techniques
that traditional Natural Language Understanding algorithms require. To validate
our approach, we present results for two applications: text classification and
dialog modeling. Using a 2D Convolutional Neural Network, we were able to
outperform the state-of-art accuracy results of non-Latin alphabet-based text
classification and achieved promising results for eight text classification
datasets. Furthermore, our approach outperformed the memory networks when using
out of vocabulary entities fromtask 4 of the bAbI dialog dataset.
Why every GBDT speed benchmark is wrong
Authors: Anna Veronika Dorogush, Vasily Ershov, Dmitriy Kruchinin
Date: 2018-10-24 00:00:00
Tasks: Benchmarking

This article provides a comprehensive study of different ways to make speed
benchmarks of gradient boosted decision trees algorithm. We show main problems
of several straight forward ways to make benchmarks, explain, why a speed
benchmarking is a challenging task and provide a set of reasonable requirements
for a benchmark to be fair and useful.
Scalable Gaussian Processes on Discrete Domains
Authors: Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch
Date: 2018-10-24 00:00:00
Tasks: Gaussian Processes, Point Processes

Kernel methods on discrete domains have shown great promise for many challenging data types, for instance, biological sequence data and molecular structure data. Scalable kernel methods like Support Vector Machines may offer good predictive performances but do not intrinsically provide uncertainty estimates. In contrast, probabilistic kernel methods like Gaussian Processes offer uncertainty estimates in addition to good predictive performance but fall short in terms of scalability. While the scalability of Gaussian processes can be improved using sparse inducing point approximations, the selection of these inducing points remains challenging. We explore different techniques for selecting inducing points on discrete domains, including greedy selection, determinantal point processes, and simulated annealing. We find that simulated annealing, which can select inducing points that are not in the training set, can perform competitively with support vector machines and full Gaussian processes on synthetic data, as well as on challenging real-world DNA sequence data.
G-SMOTE: A GMM-based synthetic minority oversampling technique for imbalanced learning
Authors: Tianlun Zhang, Xi Yang
Date: 2018-10-24 00:00:00

Imbalanced Learning is an important learning algorithm for the classification
models, which have enjoyed much popularity on many applications. Typically,
imbalanced learning algorithms can be partitioned into two types, i.e., data
level approaches and algorithm level approaches. In this paper, the focus is to
develop a robust synthetic minority oversampling technique which falls the
umbrella of data level approaches. On one hand, we proposed a method to
generate synthetic samples in a high dimensional feature space, instead of a
linear sampling space. On the other hand, in the proposed imbalanced learning
framework, Gaussian Mixture Model is employed to distinguish the outliers from
minority class instances and filter out the synthetic majority class instances.
Last and more importantly, an adaptive optimization method is proposed to
optimize these parameters in sampling process. By doing so, an effectiveness
and efficiency imbalanced learning framework is developed.
Generative adversarial networks and adversarial methods in biomedical image analysis
Authors: Jelmer M. Wolterink, Konstantinos Kamnitsas, Christian Ledig, Ivana Išgum
Date: 2018-10-24 00:00:00

Generative adversarial networks (GANs) and other adversarial methods are
based on a game-theoretical perspective on joint optimization of two neural
networks as players in a game. Adversarial techniques have been extensively
used to synthesize and analyze biomedical images. We provide an introduction to
GANs and adversarial methods, with an overview of biomedical image analysis
tasks that have benefited from such methods. We conclude with a discussion of
strengths and limitations of adversarial methods in biomedical image analysis,
and propose potential future research directions.
Label Propagation for Learning with Label Proportions
Authors: Rafael Poyiadzi, Raul Santos-Rodriguez, Niall Twomey
Date: 2018-10-24 00:00:00

Learning with Label Proportions (LLP) is the problem of recovering the
underlying true labels given a dataset when the data is presented in the form
of bags. This paradigm is particularly suitable in contexts where providing
individual labels is expensive and label aggregates are more easily obtained.
In the healthcare domain, it is a burden for a patient to keep a detailed diary
of their daily routines, but often they will be amenable to provide higher
level summaries of daily behavior. We present a novel and efficient graph-based
algorithm that encourages local smoothness and exploits the global structure of
the data, while preserving the `mass' of each bag.
The MeMAD Submission to the IWSLT 2018 Speech Translation Task
Authors: Umut Sulubacak, Jörg Tiedemann, Aku Rouhe, Stig-Arne Grönroos, Mikko Kurimo
Date: 2018-10-24 00:00:00
Proceeding: IWSLT (EMNLP) 2018 10
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, NMT, speech-recognition, Speech Recognition, Translation

This paper describes the MeMAD project entry to the IWSLT Speech Translation
Shared Task, addressing the translation of English audio into German text.
Between the pipeline and end-to-end model tracks, we participated only in the
former, with three contrastive systems. We tried also the latter, but were not
able to finish our end-to-end model in time.
  All of our systems start by transcribing the audio into text through an
automatic speech recognition (ASR) model trained on the TED-LIUM English Speech
Recognition Corpus (TED-LIUM). Afterwards, we feed the transcripts into
English-German text-based neural machine translation (NMT) models. Our systems
employ three different translation models trained on separate training sets
compiled from the English-German part of the TED Speech Translation Corpus
(TED-Trans) and the OpenSubtitles2018 section of the OPUS collection.
  In this paper, we also describe the experiments leading up to our final
systems. Our experiments indicate that using OpenSubtitles2018 in training
significantly improves translation performance. We also experimented with
various pre- and postprocessing routines for the NMT module, but we did not
have much success with these.
  Our best-scoring system attains a BLEU score of 16.45 on the test set for
this year's task.
Learning to Discriminate Noises for Incorporating External Information in Neural Machine Translation
Authors: Zaixiang Zheng, Shu-Jian Huang, Zewei Sun, Rongxiang Weng, Xin-yu Dai, Jia-Jun Chen
Date: 2018-10-24 00:00:00
Tasks: Machine Translation, NMT, Sentence, Translation

Previous studies show that incorporating external information could improve
the translation quality of Neural Machine Translation (NMT) systems. However,
there are inevitably noises in the external information, severely reducing the
benefit that the existing methods could receive from the incorporation. To
tackle the problem, this study pays special attention to the discrimination of
the noises during the incorporation. We argue that there exist two kinds of
noise in this external information, i.e. global noise and local noise, which
affect the translations for the whole sentence and for some specific words,
respectively. Accordingly, we propose a general framework that learns to
jointly discriminate both the global and local noises, so that the external
information could be better leveraged. Our model is trained on the dataset
derived from the original parallel corpus without any external labeled data or
annotation. Experimental results in various real-world scenarios, language
pairs, and neural architectures indicate that discriminating noises contributes
to significant improvements in translation quality by being able to better
incorporate the external information, even in very noisy conditions.
Dental pathology detection in 3D cone-beam CT
Authors: Adel Zakirov, Matvey Ezhov, Maxim Gusarev, Vladimir Alexandrovsky, Evgeny Shumilov
Date: 2018-10-24 00:00:00

Cone-beam computed tomography (CBCT) is a valuable imaging method in dental
diagnostics that provides information not available in traditional 2D imaging.
However, interpretation of CBCT images is a time-consuming process that
requires a physician to work with complicated software. In this work we propose
an automated pipeline composed of several deep convolutional neural networks
and algorithmic heuristics. Our task is two-fold: a) find locations of each
present tooth inside a 3D image volume, and b) detect several common tooth
conditions in each tooth. The proposed system achieves 96.3\% accuracy in tooth
localization and an average of 0.94 AUROC for 6 common tooth conditions.
A Proof-Theoretic Approach to Scope Ambiguity in Compositional Vector Space Models
Authors: Gijs Jasper Wijnholds
Date: 2018-10-24 00:00:00

We investigate the extent to which compositional vector space models can be
used to account for scope ambiguity in quantified sentences (of the form "Every
man loves some woman"). Such sentences containing two quantifiers introduce two
readings, a direct scope reading and an inverse scope reading. This ambiguity
has been treated in a vector space model using bialgebras by (Hedges and
Sadrzadeh, 2016) and (Sadrzadeh, 2016), though without an explanation of the
mechanism by which the ambiguity arises. We combine a polarised focussed
sequent calculus for the non-associative Lambek calculus NL, as described in
(Moortgat and Moot, 2011), with the vector based approach to quantifier scope
ambiguity. In particular, we establish a procedure for obtaining a vector space
model for quantifier scope ambiguity in a derivational way.
Coarse-to-fine volumetric segmentation of teeth in Cone-Beam CT
Authors: Matvey Ezhov, Adel Zakirov, Maxim Gusarev
Date: 2018-10-24 00:00:00
Tasks: Segmentation, Semantic Segmentation

We consider the problem of localizing and segmenting individual teeth inside
3D Cone-Beam Computed Tomography (CBCT) images. To handle large image sizes we
approach this task with a coarse-to-fine framework, where the whole volume is
first analyzed as a 33-class semantic segmentation (adults have up to 32 teeth)
in coarse resolution, followed by binary semantic segmentation of the cropped
region of interest in original resolution. To improve the performance of the
challenging 33-class segmentation, we first train the Coarse step model on a
large weakly labeled dataset, then fine-tune it on a smaller precisely labeled
dataset. The Fine step model is trained with precise labels only. Experiments
using our in-house dataset show significant improvement for both
weakly-supervised pretraining and for the addition of the Fine step.
Empirically, this framework yields precise teeth masks with low localization
errors sufficient for many real-world applications.
Mask Propagation Network for Video Object Segmentation
Authors: Jia Sun, Dongdong Yu, Yinghong Li, Changhu Wang
Date: 2018-10-24 00:00:00
Tasks: Instance Segmentation, Object, Optical Flow Estimation, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Segmentation, Video Semantic Segmentation

In this work, we propose a mask propagation network to treat the video
segmentation problem as a concept of the guided instance segmentation. Similar
to most MaskTrack based video segmentation methods, our method takes the mask
probability map of previous frame and the appearance of current frame as
inputs, and predicts the mask probability map for the current frame.
Specifically, we adopt the Xception backbone based DeepLab v3+ model as the
probability map predictor in our prediction pipeline. Besides, instead of the
full image and the original mask probability, our network takes the region of
interest of the instance, and the new mask probability which warped by the
optical flow between the previous and current frames as the inputs. We also
ensemble the modified One-Shot Video Segmentation Network to make the final
predictions in order to retrieve and segment the missing instance.
Learning color space adaptation from synthetic to real images of cirrus clouds
Authors: Qing Lyu, Minghao Chen, Xiang Chen
Date: 2018-10-24 00:00:00
Tasks: Segmentation, Semantic Segmentation

Cloud segmentation plays a crucial role in image analysis for climate modeling. Manually labeling the training data for cloud segmentation is time-consuming and error-prone. We explore to train segmentation networks with synthetic data due to the natural acquisition of pixel-level labels. Nevertheless, the domain gap between synthetic and real images significantly degrades the performance of the trained model. We propose a color space adaptation method to bridge the gap, by training a color-sensitive generator and discriminator to adapt synthetic data to real images in color space. Instead of transforming images by general convolutional kernels, we adopt a set of closed-form operations to make color-space adjustments while preserving the labels. We also construct a synthetic-to-real cirrus cloud dataset SynCloud and demonstrate the adaptation efficacy on the semantic segmentation task of cirrus clouds. With our adapted synthetic data for training the semantic segmentation, we achieve an improvement of 6:59% when applied to real images, superior to alternative methods.
Learn to Code-Switch: Data Augmentation using Copy Mechanism on Language Modeling
Authors: Genta Indra Winata, Andrea Madotto, Chien-Sheng Wu, Pascale Fung
Date: 2018-10-24 00:00:00
Tasks: Data Augmentation, Language Modeling, Language Modelling, Sentence

Building large-scale datasets for training code-switching language models is
challenging and very expensive. To alleviate this problem using parallel corpus
has been a major workaround. However, existing solutions use linguistic
constraints which may not capture the real data distribution. In this work, we
propose a novel method for learning how to generate code-switching sentences
from parallel corpora. Our model uses a Seq2Seq model in combination with
pointer networks to align and choose words from the monolingual sentences and
form a grammatical code-switching sentence. In our experiment, we show that by
training a language model using the augmented sentences we improve the
perplexity score by 10% compared to the LSTM baseline.
Multistep Speed Prediction on Traffic Networks: A Graph Convolutional Sequence-to-Sequence Learning Approach with Attention Mechanism
Authors: Zhengchao Zhang, Meng Li, Xi Lin, Yinhai Wang, Fang He
Date: 2018-10-24 00:00:00
Tasks: Prediction

Multistep traffic forecasting on road networks is a crucial task in
successful intelligent transportation system applications. To capture the
complex non-stationary temporal dynamics and spatial dependency in multistep
traffic-condition prediction, we propose a novel deep learning framework named
attention graph convolutional sequence-to-sequence model (AGC-Seq2Seq). In the
proposed deep learning framework, spatial and temporal dependencies are modeled
through the Seq2Seq model and graph convolution network separately, and the
attention mechanism along with a newly designed training method based on the
Seq2Seq architecture is proposed to overcome the difficulty in multistep
prediction and further capture the temporal heterogeneity of traffic pattern.
We conduct numerical tests to compare AGC-Seq2Seq with other benchmark models
using a real-world dataset. The results indicate that our model yields the best
prediction performance in terms of various prediction error measures.
Furthermore, the variation of spatiotemporal correlation of traffic conditions
under different perdition steps and road segments is revealed through
sensitivity analyses.
Textually Guided Ranking Network for Attentional Image Retweet Modeling
Authors: Zhou Zhao, Hanbing Zhan, Lingtao Meng, Jun Xiao, Jun Yu, Min Yang, Fei Wu, Deng Cai
Date: 2018-10-24 00:00:00
Tasks: Prediction

Retweet prediction is a challenging problem in social media sites (SMS). In
this paper, we study the problem of image retweet prediction in social media,
which predicts the image sharing behavior that the user reposts the image
tweets from their followees. Unlike previous studies, we learn user preference
ranking model from their past retweeted image tweets in SMS. We first propose
heterogeneous image retweet modeling network (IRM) that exploits users' past
retweeted image tweets with associated contexts, their following relations in
SMS and preference of their followees. We then develop a novel attentional
multi-faceted ranking network learning framework with textually guided
multi-modal neural networks for the proposed heterogenous IRM network to learn
the joint image tweet representations and user preference representations for
prediction task. The extensive experiments on a large-scale dataset from
Twitter site shows that our method achieves better performance than other
state-of-the-art solutions to the problem.
Universal Language Model Fine-Tuning with Subword Tokenization for Polish
Authors: Piotr Czapla, Jeremy Howard, Marcin Kardas
Date: 2018-10-24 00:00:00
Tasks: Language Modeling, Language Modelling, Transfer Learning

Universal Language Model for Fine-tuning [arXiv:1801.06146] (ULMFiT) is one
of the first NLP methods for efficient inductive transfer learning.
Unsupervised pretraining results in improvements on many NLP tasks for English.
In this paper, we describe a new method that uses subword tokenization to adapt
ULMFiT to languages with high inflection. Our approach results in a new
state-of-the-art for the Polish language, taking first place in Task 3 of
PolEval'18. After further training, our final model outperformed the second
best model by 35%. We have open-sourced our pretrained models and code.
Cross-Resolution Person Re-identification with Deep Antithetical Learning
Authors: Zijie Zhuang, Haizhou Ai, Long Chen, Chong Shang
Date: 2018-10-24 00:00:00
Tasks: Person Re-Identification

Images with different resolutions are ubiquitous in public person
re-identification (ReID) datasets and real-world scenes, it is thus crucial for
a person ReID model to handle the image resolution variations for improving its
generalization ability. However, most existing person ReID methods pay little
attention to this resolution discrepancy problem. One paradigm to deal with
this problem is to use some complicated methods for mapping all images into an
artificial image space, which however will disrupt the natural image
distribution and requires heavy image preprocessing. In this paper, we analyze
the deficiencies of several widely-used objective functions handling image
resolution discrepancies and propose a new framework called deep antithetical
learning that directly learns from the natural image space rather than creating
an arbitrary one. We first quantify and categorize original training images
according to their resolutions. Then we create an antithetical training set and
make sure that original training images have counterparts with antithetical
resolutions in this new set. At last, a novel Contrastive Center Loss(CCL) is
proposed to learn from images with different resolutions without being
interfered by their resolution discrepancies. Extensive experimental analyses
and evaluations indicate that the proposed framework, even using a vanilla deep
ReID network, exhibits remarkable performance improvements. Without bells and
whistles, our approach outperforms previous state-of-the-art methods by a large
margin.