pwc-archive/papers-with-abstracts huggingface dataset:
Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum
Authors: Adam Tsakalidis, Nikolaos Aletras, Alexandra I. Cristea, Maria Liakata
Date: 2018-08-26 00:00:00

Modelling user voting intention in social media is an important research
area, with applications in analysing electorate behaviour, online political
campaigning and advertising. Previous approaches mainly focus on predicting
national general elections, which are regularly scheduled and where data of
past results and opinion polls are available. However, there is no evidence of
how such models would perform during a sudden vote under time-constrained
circumstances. That poses a more challenging task compared to traditional
elections, due to its spontaneous nature. In this paper, we focus on the 2015
Greek bailout referendum, aiming to nowcast on a daily basis the voting
intention of 2,197 Twitter users. We propose a semi-supervised multiple
convolution kernel learning approach, leveraging temporally sensitive text and
network information. Our evaluation under a real-time simulation framework
demonstrates the effectiveness and robustness of our approach against
competitive baselines, achieving a significant 20% increase in F-score compared
to solely text-based models.
DeepTracker: Visualizing the Training Process of Convolutional Neural Networks
Authors: Dongyu Liu, Weiwei Cui, Kai Jin, YuXiao Guo, Huamin Qu
Date: 2018-08-26 00:00:00

Deep convolutional neural networks (CNNs) have achieved remarkable success in
various fields. However, training an excellent CNN is practically a
trial-and-error process that consumes a tremendous amount of time and computer
resources. To accelerate the training process and reduce the number of trials,
experts need to understand what has occurred in the training process and why
the resulting CNN behaves as such. However, current popular training platforms,
such as TensorFlow, only provide very little and general information, such as
training/validation errors, which is far from enough to serve this purpose. To
bridge this gap and help domain experts with their training tasks in a
practical environment, we propose a visual analytics system, DeepTracker, to
facilitate the exploration of the rich dynamics of CNN training processes and
to identify the unusual patterns that are hidden behind the huge amount of
training log. Specifically,we combine a hierarchical index mechanism and a set
of hierarchical small multiples to help experts explore the entire training log
from different levels of detail. We also introduce a novel cube-style
visualization to reveal the complex correlations among multiple types of
heterogeneous training data including neuron weights, validation images, and
training iterations. Three case studies are conducted to demonstrate how
DeepTracker provides its users with valuable knowledge in an industry-level CNN
training process, namely in our case, training ResNet-50 on the ImageNet
dataset. We show that our method can be easily applied to other
state-of-the-art "very deep" CNN models.
Word Sense Induction with Neural biLM and Symmetric Patterns
Authors: Asaf Amrami, Yoav Goldberg
Date: 2018-08-26 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Clustering, Language Modeling, Word Sense Induction

An established method for Word Sense Induction (WSI) uses a language model to
predict probable substitutes for target words, and induces senses by clustering
these resulting substitute vectors.
  We replace the ngram-based language model (LM) with a recurrent one. Beyond
being more accurate, the use of the recurrent LM allows us to effectively query
it in a creative way, using what we call dynamic symmetric patterns.
  The combination of the RNN-LM and the dynamic symmetric patterns results in
strong substitute vectors for WSI, allowing to surpass the current
state-of-the-art on the SemEval 2013 WSI shared task by a large margin.
An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams
Authors: Mahardhika Pratama, Witold Pedrycz, Geoffrey I. Webb
Date: 2018-08-26 00:00:00
Tasks: Continual Learning, Drift Detection, feature selection

Existing FNNs are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be automatically extracted from data streams or removed if they play limited role during their lifespan. The structure of the network can be deepened on demand by stacking additional layers using a drift detection method which not only detects the covariate drift, variations of input space, but also accurately identifies the real drift, dynamic changes of both feature space and target space. DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely gClass, drives the hidden layer. It is equipped by an automatic feature selection method which controls activation and deactivation of input attributes to induce varying subsets of input features. A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of dimensionality of input space due to the nature of feature augmentation approach in building a deep network structure. DEVFNN works in the sample-wise fashion and is compatible for data stream applications. The efficacy of DEVFNN has been thoroughly evaluated using seven datasets with non-stationary properties under the prequential test-then-train protocol. It has been compared with four popular continual learning algorithms and its shallow counterpart where DEVFNN demonstrates improvement of classification accuracy. Moreover, it is also shown that the concept drift detection method is an effective tool to control the depth of network structure while the hidden layer merging scenario is capable of simplifying the network complexity of a deep network with negligible compromise of generalization performance.
Efficient Single Image Super Resolution using Enhanced Learned Group Convolutions
Authors: Vandit Jain, Prakhar Bansal, Abhinav Kumar Singh, Rajeev Srivastava
Date: 2018-08-26 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Convolutional Neural Networks (CNNs) have demonstrated great results for the
single-image super-resolution (SISR) problem. Currently, most CNN algorithms
promote deep and computationally expensive models to solve SISR. However, we
propose a novel SISR method that uses relatively less number of computations.
On training, we get group convolutions that have unused connections removed. We
have refined this system specifically for the task at hand by removing
unnecessary modules from original CondenseNet. Further, a reconstruction
network consisting of deconvolutional layers has been used in order to upscale
to high resolution. All these steps significantly reduce the number of
computations required at testing time. Along with this, bicubic upsampled input
is added to the network output for easier learning. Our model is named
SRCondenseNet. We evaluate the method using various benchmark datasets and show
that it performs favourably against the state-of-the-art methods in terms of
both accuracy and number of computations required.
Event Detection with Neural Networks: A Rigorous Empirical Evaluation
Authors: J. Walker Orr, Prasad Tadepalli, Xiaoli Fern
Date: 2018-08-26 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Event Detection

Detecting events and classifying them into predefined types is an important
step in knowledge extraction from natural language texts. While the neural
network models have generally led the state-of-the-art, the differences in
performance between different architectures have not been rigorously studied.
In this paper we present a novel GRU-based model that combines syntactic
information along with temporal structure through an attention mechanism. We
show that it is competitive with other neural network architectures through
empirical evaluations under different random initializations and
training-validation-test splits of ACE2005 dataset.
FinBrain: When Finance Meets AI 2.0
Authors: Xiaolin Zheng, Mengying Zhu, Qibing Li, Chaochao Chen, Yanchao Tan
Date: 2018-08-26 00:00:00
Tasks: Decision Making, Management

Artificial intelligence (AI) is the core technology of technological
revolution and industrial transformation. As one of the new intelligent needs
in the AI 2.0 era, financial intelligence has elicited much attention from the
academia and industry. In our current dynamic capital market, financial
intelligence demonstrates a fast and accurate machine learning capability to
handle complex data and has gradually acquired the potential to become a
"financial brain". In this work, we survey existing studies on financial
intelligence. First, we describe the concept of financial intelligence and
elaborate on its position in the financial technology field. Second, we
introduce the development of financial intelligence and review state-of-the-art
techniques in wealth management, risk management, financial security, financial
consulting, and blockchain. Finally, we propose a research framework called
FinBrain and summarize four open issues, namely, explainable financial agents
and causality, perception and prediction under uncertainty, risk-sensitive and
robust decision making, and multi-agent game and mechanism design. We believe
that these research directions can lay the foundation for the development of AI
2.0 in the finance field.
Contextual Parameter Generation for Universal Neural Machine Translation
Authors: Emmanouil Antonios Platanios, Mrinmaya Sachan, Graham Neubig, Tom Mitchell
Date: 2018-08-26 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder, Domain Adaptation, Machine Translation, NMT, Translation

We propose a simple modification to existing neural machine translation (NMT)
models that enables using a single universal model to translate between
multiple languages while allowing for language specific parameterization, and
that can also be used for domain adaptation. Our approach requires no changes
to the model architecture of a standard NMT system, but instead introduces a
new component, the contextual parameter generator (CPG), that generates the
parameters of the system (e.g., weights in a neural network). This parameter
generator accepts source and target language embeddings as input, and generates
the parameters for the encoder and the decoder, respectively. The rest of the
model remains unchanged and is shared across all languages. We show how this
simple modification enables the system to use monolingual data for training and
also perform zero-shot translation. We further show it is able to surpass
state-of-the-art performance for both the IWSLT-15 and IWSLT-17 datasets and
that the learned language embeddings are able to uncover interesting
relationships between languages.
Deep Probabilistic Logic: A Unifying Framework for Indirect Supervision
Authors: Hai Wang, Hoifung Poon
Date: 2018-08-26 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Reading Comprehension, Representation Learning

Deep learning has emerged as a versatile tool for a wide range of NLP tasks,
due to its superior capacity in representation learning. But its applicability
is limited by the reliance on annotated examples, which are difficult to
produce at scale. Indirect supervision has emerged as a promising direction to
address this bottleneck, either by introducing labeling functions to
automatically generate noisy examples from unlabeled text, or by imposing
constraints over interdependent label decisions. A plethora of methods have
been proposed, each with respective strengths and limitations. Probabilistic
logic offers a unifying language to represent indirect supervision, but
end-to-end modeling with probabilistic logic is often infeasible due to
intractable inference and learning. In this paper, we propose deep
probabilistic logic (DPL) as a general framework for indirect supervision, by
composing probabilistic logic with deep learning. DPL models label decisions as
latent variables, represents prior knowledge on their relations using weighted
first-order logical formulas, and alternates between learning a deep neural
network for the end task and refining uncertain formula weights for indirect
supervision, using variational EM. This framework subsumes prior indirect
supervision methods as special cases, and enables novel combination via
infusion of rich domain and linguistic knowledge. Experiments on biomedical
machine reading demonstrate the promise of this approach.
Painting Outside the Box: Image Outpainting with GANs
Authors: Mark Sabini, Gili Rusak
Date: 2018-08-25 00:00:00
Tasks: Deep Learning, Image Inpainting, Image Outpainting

The challenging task of image outpainting (extrapolation) has received
comparatively little attention in relation to its cousin, image inpainting
(completion). Accordingly, we present a deep learning approach based on Iizuka
et al. for adversarially training a network to hallucinate past image
boundaries. We use a three-phase training schedule to stably train a DCGAN
architecture on a subset of the Places365 dataset. In line with Iizuka et al.,
we also use local discriminators to enhance the quality of our output. Once
trained, our model is able to outpaint $128 \times 128$ color images relatively
realistically, thus allowing for recursive outpainting. Our results show that
deep learning approaches to image outpainting are both feasible and promising.
Exploring Recombination for Efficient Decoding of Neural Machine Translation
Authors: Zhisong Zhang, Rui Wang, Masao Utiyama, Eiichiro Sumita, Hai Zhao
Date: 2018-08-25 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder, Machine Translation, NMT, Translation

In Neural Machine Translation (NMT), the decoder can capture the features of
the entire prediction history with neural connections and representations. This
means that partial hypotheses with different prefixes will be regarded
differently no matter how similar they are. However, this might be inefficient
since some partial hypotheses can contain only local differences that will not
influence future predictions. In this work, we introduce recombination in NMT
decoding based on the concept of the "equivalence" of partial hypotheses.
Heuristically, we use a simple $n$-gram suffix based equivalence function and
adapt it into beam search decoding. Through experiments on large-scale
Chinese-to-English and English-to-Germen translation tasks, we show that the
proposed method can obtain similar translation quality with a smaller beam
size, making NMT decoding more efficient.
Deep-Learning Ensembles for Skin-Lesion Segmentation, Analysis, Classification: RECOD Titans at ISIC Challenge 2018
Authors: Alceu Bissoto, Fábio Perez, Vinícius Ribeiro, Michel Fornaciali, Sandra Avila, Eduardo Valle
Date: 2018-08-25 00:00:00
Tasks: Attribute, Classification, General Classification, Lesion Segmentation, Segmentation, Skin Lesion Segmentation

This extended abstract describes the participation of RECOD Titans in parts 1
to 3 of the ISIC Challenge 2018 "Skin Lesion Analysis Towards Melanoma
Detection" (MICCAI 2018). Although our team has a long experience with melanoma
classification and moderate experience with lesion segmentation, the ISIC
Challenge 2018 was the very first time we worked on lesion attribute detection.
For each task we submitted 3 different ensemble approaches, varying
combinations of models and datasets. Our best results on the official testing
set, regarding the official metric of each task, were: 0.728 (segmentation),
0.344 (attribute detection) and 0.803 (classification). Those submissions
reached, respectively, the 56th, 14th and 9th places.
Network Inference from Temporal-Dependent Grouped Observations
Authors: Yunpeng Zhao
Date: 2018-08-25 00:00:00
Tasks: valid

In social network analysis, the observed data is usually some social
behavior, such as the formation of groups, rather than an explicit network
structure. Zhao and Weko (2017) propose a model-based approach called the hub
model to infer implicit networks from grouped observations. The hub model
assumes independence between groups, which sometimes is not valid in practice.
In this article, we generalize the idea of the hub model into the case of
grouped observations with temporal dependence. As in the hub model, we assume
that the group at each time point is gathered by one leader. Unlike in the hub
model, the group leaders are not sampled independently but follow a Markov
chain, and other members in adjacent groups can also be correlated.
  An expectation-maximization (EM) algorithm is developed for this model and a
polynomial-time algorithm is proposed for the E-step. The performance of the
new model is evaluated under different simulation settings. We apply this model
to a data set of the Kibale Chimpanzee Project.
Human-centric Indoor Scene Synthesis Using Stochastic Grammar
Authors: Siyuan Qi, Yixin Zhu, Siyuan Huang, Chenfanfu Jiang, Song-Chun Zhu
Date: 2018-08-25 00:00:00
Proceeding: CVPR 2018 6
Tasks: Indoor Scene Synthesis

We present a human-centric method to sample and synthesize 3D room layouts
and 2D images thereof, to obtain large-scale 2D/3D image data with perfect
per-pixel ground truth. An attributed spatial And-Or graph (S-AOG) is proposed
to represent indoor scenes. The S-AOG is a probabilistic grammar model, in
which the terminal nodes are object entities. Human contexts as contextual
relations are encoded by Markov Random Fields (MRF) on the terminal nodes. We
learn the distributions from an indoor scene dataset and sample new layouts
using Monte Carlo Markov Chain. Experiments demonstrate that our method can
robustly sample a large variety of realistic room layouts based on three
criteria: (i) visual realism comparing to a state-of-the-art room arrangement
method, (ii) accuracy of the affordance maps with respect to groundtruth, and
(ii) the functionality and naturalness of synthesized rooms evaluated by human
subjects. The code is available at
https://github.com/SiyuanQi/human-centric-scene-synthesis.
Representing Social Media Users for Sarcasm Detection
Authors: Y. Alex Kolchinski, Christopher Potts
Date: 2018-08-25 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Sarcasm Detection

We explore two methods for representing authors in the context of textual
sarcasm detection: a Bayesian approach that directly represents authors'
propensities to be sarcastic, and a dense embedding approach that can learn
interactions between the author and the text. Using the SARC dataset of Reddit
comments, we show that augmenting a bidirectional RNN with these
representations improves performance; the Bayesian approach suffices in
homogeneous contexts, whereas the added power of the dense embeddings proves
valuable in more diverse ones.
Optimal Nonparametric Inference with Two-Scale Distributional Nearest Neighbors
Authors: Emre Demirkaya, Yingying Fan, Lan Gao, Jinchi Lv, Patrick Vossler, Jingbo Wang
Date: 2018-08-25 00:00:00
Tasks: Causal Inference, regression, Vocal Bursts Valence Prediction

The weighted nearest neighbors (WNN) estimator has been popularly used as a flexible and easy-to-implement nonparametric tool for mean regression estimation. The bagging technique is an elegant way to form WNN estimators with weights automatically generated to the nearest neighbors; we name the resulting estimator as the distributional nearest neighbors (DNN) for easy reference. Yet, there is a lack of distributional results for such estimator, limiting its application to statistical inference. Moreover, when the mean regression function has higher-order smoothness, DNN does not achieve the optimal nonparametric convergence rate, mainly because of the bias issue. In this work, we provide an in-depth technical analysis of the DNN, based on which we suggest a bias reduction approach for the DNN estimator by linearly combining two DNN estimators with different subsampling scales, resulting in the novel two-scale DNN (TDNN) estimator. The two-scale DNN estimator has an equivalent representation of WNN with weights admitting explicit forms and some being negative. We prove that, thanks to the use of negative weights, the two-scale DNN estimator enjoys the optimal nonparametric rate of convergence in estimating the regression function under the fourth-order smoothness condition. We further go beyond estimation and establish that the DNN and two-scale DNN are both asymptotically normal as the subsampling scales and sample size diverge to infinity. For the practical implementation, we also provide variance estimators and a distribution estimator using the jackknife and bootstrap techniques for the two-scale DNN. These estimators can be exploited for constructing valid confidence intervals for nonparametric inference of the regression function. The theoretical results and appealing finite-sample performance of the suggested two-scale DNN method are illustrated with several numerical examples.
The Social Cost of Strategic Classification
Authors: Smitha Milli, John Miller, Anca D. Dragan, Moritz Hardt
Date: 2018-08-25 00:00:00
Tasks: Classification, Decision Making, Fairness, General Classification

Consequential decision-making typically incentivizes individuals to behave
strategically, tailoring their behavior to the specifics of the decision rule.
A long line of work has therefore sought to counteract strategic behavior by
designing more conservative decision boundaries in an effort to increase
robustness to the effects of strategic covariate shift. We show that these
efforts benefit the institutional decision maker at the expense of the
individuals being classified. Introducing a notion of social burden, we prove
that any increase in institutional utility necessarily leads to a corresponding
increase in social burden. Moreover, we show that the negative externalities of
strategic classification can disproportionately harm disadvantaged groups in
the population. Our results highlight that strategy-robustness must be weighed
against considerations of social welfare and fairness.
Comparing CNN and LSTM character-level embeddings in BiLSTM-CRF models for chemical and disease named entity recognition
Authors: Zenan Zhai, Dat Quoc Nguyen, Karin Verspoor
Date: 2018-08-25 00:00:00
Proceeding: WS 2018 10
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Word Embeddings

We compare the use of LSTM-based and CNN-based character-level word
embeddings in BiLSTM-CRF models to approach chemical and disease named entity
recognition (NER) tasks. Empirical results over the BioCreative V CDR corpus
show that the use of either type of character-level word embeddings in
conjunction with the BiLSTM-CRF models leads to comparable state-of-the-art
performance. However, the models using CNN-based character-level word
embeddings have a computational performance advantage, increasing training time
over word-based models by 25% while the LSTM-based character-level word
embeddings more than double the required training time.