pwc-archive/papers-with-abstracts huggingface dataset:
Evaluation of Preference of Multimedia Content using Deep Neural Networks for Electroencephalography
Authors: Seong-Eun Moon, Soobeom Jang, Jong-Seok Lee
Date: 2018-09-11 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Evaluation of quality of experience (QoE) based on electroencephalography
(EEG) has received great attention due to its capability of real-time QoE
monitoring of users. However, it still suffers from rather low recognition
accuracy. In this paper, we propose a novel method using deep neural networks
toward improved modeling of EEG and thereby improved recognition accuracy. In
particular, we aim to model spatio-temporal characteristics relevant for QoE
analysis within learning models. The results demonstrate the effectiveness of
the proposed method.
Tuning metaheuristics by sequential optimization of regression models
Authors: Áthila R. Trindade, Felipe Campelo
Date: 2018-09-11 00:00:00
Tasks: Multiobjective Optimization, regression

Tuning parameters is an important step for the application of metaheuristics
to problem classes of interest. In this work we present a tuning framework
based on the sequential optimization of perturbed regression models. Besides
providing algorithm configurations with good expected performance, the proposed
methodology can also provide insights on the relevance of each parameter and
their interactions, as well as models of expected algorithm performance for a
given problem class, conditional on the parameter values. A test case is
presented for the tuning of six parameters of a decomposition-based
multiobjective optimization algorithm, in which an instantiation of the
proposed framework is compared against the results obtained by the most recent
version the Iterated Racing (Irace) procedure. The results suggest that the
proposed approach returns solutions that are as good as those of Irace in terms
of mean performance, with the advantage of providing more information on the
relevance and effect of each parameter on the expected performance of the
algorithm.
Unsupervised Cross-lingual Transfer of Word Embedding Spaces
Authors: Ruochen Xu, Yiming Yang, Naoki Otani, Yuexin Wu
Date: 2018-09-10 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Bilingual Lexicon Induction, Cross-Lingual Transfer, text-classification, Text Classification, Word Embeddings, Word Similarity

Cross-lingual transfer of word embeddings aims to establish the semantic
mappings among words in different languages by learning the transformation
functions over the corresponding word embedding spaces. Successfully solving
this problem would benefit many downstream tasks such as to translate text
classification models from resource-rich languages (e.g. English) to
low-resource languages. Supervised methods for this problem rely on the
availability of cross-lingual supervision, either using parallel corpora or
bilingual lexicons as the labeled data for training, which may not be available
for many low resource languages. This paper proposes an unsupervised learning
approach that does not require any cross-lingual labeled data. Given two
monolingual word embedding spaces for any language pair, our algorithm
optimizes the transformation functions in both directions simultaneously based
on distributional matching as well as minimizing the back-translation losses.
We use a neural network implementation to calculate the Sinkhorn distance, a
well-defined distributional similarity measure, and optimize our objective
through back-propagation. Our evaluation on benchmark datasets for bilingual
lexicon induction and cross-lingual word similarity prediction shows stronger
or competitive performance of the proposed method compared to other
state-of-the-art supervised and unsupervised baseline methods over many
language pairs.
Detecting Gang-Involved Escalation on Social Media Using Context
Authors: Serina Chang, Ruiqi Zhong, Ethan Adams, Fei-Tzin Lee, Siddharth Varia, Desmond Patton, William Frey, Chris Kedzie, Kathleen McKeown
Date: 2018-09-10 00:00:00
Proceeding: EMNLP 2018 10

Gang-involved youth in cities such as Chicago have increasingly turned to
social media to post about their experiences and intents online. In some
situations, when they experience the loss of a loved one, their online
expression of emotion may evolve into aggression towards rival gangs and
ultimately into real-world violence. In this paper, we present a novel system
for detecting Aggression and Loss in social media. Our system features the use
of domain-specific resources automatically derived from a large unlabeled
corpus, and contextual representations of the emotional and semantic content of
the user's recent tweets as well as their interactions with other users.
Incorporating context in our Convolutional Neural Network (CNN) leads to a
significant improvement.
ClusterGAN : Latent Space Clustering in Generative Adversarial Networks
Authors: Sudipto Mukherjee, Himanshu Asnani, Eugene Lin, Sreeram Kannan
Date: 2018-09-10 00:00:00
Tasks: Clustering

Generative Adversarial networks (GANs) have obtained remarkable success in
many unsupervised learning tasks and unarguably, clustering is an important
unsupervised learning problem. While one can potentially exploit the
latent-space back-projection in GANs to cluster, we demonstrate that the
cluster structure is not retained in the GAN latent space.
  In this paper, we propose ClusterGAN as a new mechanism for clustering using
GANs. By sampling latent variables from a mixture of one-hot encoded variables
and continuous latent variables, coupled with an inverse network (which
projects the data to the latent space) trained jointly with a clustering
specific loss, we are able to achieve clustering in the latent space. Our
results show a remarkable phenomenon that GANs can preserve latent space
interpolation across categories, even though the discriminator is never exposed
to such vectors. We compare our results with various clustering baselines and
demonstrate superior performance on both synthetic and real datasets.
Improved Techniques for Adversarial Discriminative Domain Adaptation
Authors: Aaron Chadha, Yiannis Andreopoulos
Date: 2018-09-10 00:00:00
Tasks: Denoising, Domain Adaptation, image-classification, Image Classification, Sign Language Recognition, Unsupervised Domain Adaptation

Adversarial discriminative domain adaptation (ADDA) is an efficient framework for unsupervised domain adaptation in image classification, where the source and target domains are assumed to have the same classes, but no labels are available for the target domain. We investigate whether we can improve performance of ADDA with a new framework and new loss formulations. Following the framework of semi-supervised GANs, we first extend the discriminator output over the source classes, in order to model the joint distribution over domain and task. We thus leverage on the distribution over the source encoder posteriors (which is fixed during adversarial training) and propose maximum mean discrepancy (MMD) and reconstruction-based loss functions for aligning the target encoder distribution to the source domain. We compare and provide a comprehensive analysis of how our framework and loss formulations extend over simple multi-class extensions of ADDA and other discriminative variants of semi-supervised GANs. In addition, we introduce various forms of regularization for stabilizing training, including treating the discriminator as a denoising autoencoder and regularizing the target encoder with source examples to reduce overfitting under a contraction mapping (i.e., when the target per-class distributions are contracting during alignment with the source). Finally, we validate our framework on standard domain adaptation datasets, such as SVHN and MNIST. We also examine how our framework benefits recognition problems based on modalities that lack training data, by introducing and evaluating on a neuromorphic vision sensing (NVS) sign language recognition dataset, where the source and target domains constitute emulated and real neuromorphic spike events respectively. Our results on all datasets show that our proposal competes or outperforms the state-of-the-art in unsupervised domain adaptation.
URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision
Authors: Mohamed R. Ibrahim, James Haworth, Tao Cheng
Date: 2018-09-10 00:00:00
Tasks: Autonomous Vehicles, Deep Learning

Within the burgeoning expansion of deep learning and computer vision across
the different fields of science, when it comes to urban development, deep
learning and computer vision applications are still limited towards the notions
of smart cities and autonomous vehicles. Indeed, a wide gap of knowledge
appears when it comes to cities and urban regions in less developed countries
where the chaos of informality is the dominant scheme. How can deep learning
and Artificial Intelligence (AI) untangle the complexities of informality to
advance urban modelling and our understanding of cities? Various questions and
debates can be raised concerning the future of cities of the North and the
South in the paradigm of AI and computer vision. In this paper, we introduce a
new method for multipurpose realistic-dynamic urban modelling relying on deep
learning and computer vision, using deep Convolutional Neural Networks (CNN),
to sense and detect informality and slums in urban scenes from aerial and
street view images in addition to detection of pedestrian and transport modes.
The model has been trained on images of urban scenes in cities across the
globe. The model shows a good validation of understanding a wide spectrum of
nuances among the planned and the unplanned regions, including informal and
slum areas. We attempt to advance urban modelling for better understanding the
dynamics of city developments. We also aim to exemplify the significant impacts
of AI in cities beyond how smart cities are discussed and perceived in the
mainstream. The algorithms of the URBAN-i model are fully-coded in Python
programming with the pre-trained deep learning models to be used as a tool for
mapping and city modelling in the various corner of the globe, including
informal settlements and slum regions.
PedX: Benchmark Dataset for Metric 3D Pose Estimation of Pedestrians in Complex Urban Intersections
Authors: Wonhui Kim, Manikandasriram Srinivasan Ramanagopal, Charles Barto, Ming-Yuan Yu, Karl Rosaen, Nick Goumas, Ram Vasudevan, Matthew Johnson-Roberson
Date: 2018-09-10 00:00:00
Tasks: 3D Pose Estimation, Pose Estimation

This paper presents a novel dataset titled PedX, a large-scale multimodal
collection of pedestrians at complex urban intersections. PedX consists of more
than 5,000 pairs of high-resolution (12MP) stereo images and LiDAR data along
with providing 2D and 3D labels of pedestrians. We also present a novel 3D
model fitting algorithm for automatic 3D labeling harnessing constraints across
different modalities and novel shape and temporal priors. All annotated 3D
pedestrians are localized into the real-world metric space, and the generated
3D models are validated using a mocap system configured in a controlled outdoor
environment to simulate pedestrians in urban intersections. We also show that
the manual 2D labels can be replaced by state-of-the-art automated labeling
approaches, thereby facilitating automatic generation of large scale datasets.
Learning Named Entity Tagger using Domain-Specific Dictionary
Authors: Jingbo Shang, Liyuan Liu, Xiang Ren, Xiaotao Gu, Teng Ren, Jiawei Han
Date: 2018-09-10 00:00:00
Proceeding: EMNLP 2018 10
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Recent advances in deep neural models allow us to build reliable named entity
recognition (NER) systems without handcrafting features. However, such methods
require large amounts of manually-labeled training data. There have been
efforts on replacing human annotations with distant supervision (in conjunction
with external dictionaries), but the generated noisy labels pose significant
challenges on learning effective neural models. Here we propose two neural
models to suit noisy distant supervision from the dictionary. First, under the
traditional sequence labeling framework, we propose a revised fuzzy CRF layer
to handle tokens with multiple possible labels. After identifying the nature of
noisy labels in distant supervision, we go beyond the traditional framework and
propose a novel, more effective neural model AutoNER with a new Tie or Break
scheme. In addition, we discuss how to refine distant supervision for better
NER performance. Extensive experiments on three benchmark datasets demonstrate
that AutoNER achieves the best performance when only using dictionaries with no
additional human effort, and delivers competitive results with state-of-the-art
supervised benchmarks.
Collapsed Variational Inference for Nonparametric Bayesian Group Factor Analysis
Authors: Sikun Yang, Heinz Koeppl
Date: 2018-09-10 00:00:00
Tasks: Variational Inference

Group factor analysis (GFA) methods have been widely used to infer the common
structure and the group-specific signals from multiple related datasets in
various fields including systems biology and neuroimaging. To date, most
available GFA models require Gibbs sampling or slice sampling to perform
inference, which prevents the practical application of GFA to large-scale data.
In this paper we present an efficient collapsed variational inference (CVI)
algorithm for the nonparametric Bayesian group factor analysis (NGFA) model
built upon an hierarchical beta Bernoulli process. Our CVI algorithm proceeds
by marginalizing out the group-specific beta process parameters, and then
approximating the true posterior in the collapsed space using mean field
methods. Experimental results on both synthetic and real-world data demonstrate
the effectiveness of our CVI algorithm for the NGFA compared with
state-of-the-art GFA methods.
Quantile Regression for Qualifying Match of GEFCom2017 Probabilistic Load Forecasting
Authors: Florian Ziel
Date: 2018-09-10 00:00:00
Tasks: Load Forecasting, quantile regression, regression

We present a simple quantile regression-based forecasting method that was
applied in a probabilistic load forecasting framework of the Global Energy
Forecasting Competition 2017 (GEFCom2017). The hourly load data is log
transformed and split into a long-term trend component and a remainder term.
The key forecasting element is the quantile regression approach for the
remainder term that takes into account weekly and annual seasonalities such as
their interactions. Temperature information is only used to stabilize the
forecast of the long-term trend component. Public holidays information is
ignored. Still, the forecasting method placed second in the open data track and
fourth in the definite data track with our forecasting method, which is
remarkable given simplicity of the model. The method also outperforms the
Vanilla benchmark consistently.
Deep Learning Towards Mobile Applications
Authors: Ji Wang, Bokai Cao, Philip S. Yu, Lichao Sun, Weidong Bao, Xiaomin Zhu
Date: 2018-09-10 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, User Identification

Recent years have witnessed an explosive growth of mobile devices. Mobile
devices are permeating every aspect of our daily lives. With the increasing
usage of mobile devices and intelligent applications, there is a soaring demand
for mobile applications with machine learning services. Inspired by the
tremendous success achieved by deep learning in many machine learning tasks, it
becomes a natural trend to push deep learning towards mobile applications.
However, there exist many challenges to realize deep learning in mobile
applications, including the contradiction between the miniature nature of
mobile devices and the resource requirement of deep neural networks, the
privacy and security concerns about individuals' data, and so on. To resolve
these challenges, during the past few years, great leaps have been made in this
area. In this paper, we provide an overview of the current challenges and
representative achievements about pushing deep learning on mobile devices from
three aspects: training with mobile data, efficient inference on mobile
devices, and applications of mobile deep learning. The former two aspects cover
the primary tasks of deep learning. Then, we go through our two recent
applications that apply the data collected by mobile devices to inferring mood
disturbance and user identification. Finally, we conclude this paper with the
discussion of the future of this area.
Partial Recovery of Erdős-Rényi Graph Alignment via $k$-Core Alignment
Authors: Daniel Cullina, Negar Kiyavash, Prateek Mittal, H. Vincent Poor
Date: 2018-09-10 00:00:00

We determine information theoretic conditions under which it is possible to
partially recover the alignment used to generate a pair of sparse, correlated
Erd\H{o}s-R\'enyi graphs. To prove our achievability result, we introduce the
$k$-core alignment estimator. This estimator searches for an alignment in which
the intersection of the correlated graphs using this alignment has a minimum
degree of $k$. We prove a matching converse bound. As the number of vertices
grows, recovery of the alignment for a fraction of the vertices tending to one
is possible when the average degree of the intersection of the graph pair tends
to infinity. It was previously known that exact alignment is possible when this
average degree grows faster than the logarithm of the number of vertices.
Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise
Authors: Albert Akhriev, Jakub Marecek, Andrea Simonetto
Date: 2018-09-10 00:00:00

In tracking of time-varying low-rank models of time-varying matrices, we present a method robust to both uniformly-distributed measurement noise and arbitrarily-distributed ``sparse'' noise. In theory, we bound the tracking error. In practice, our use of randomised coordinate descent is scalable and allows for encouraging results on changedetection net, a benchmark.
VPE: Variational Policy Embedding for Transfer Reinforcement Learning
Authors: Isac Arnekvist, Danica Kragic, Johannes A. Stork
Date: 2018-09-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Reinforcement Learning

Reinforcement Learning methods are capable of solving complex problems, but
resulting policies might perform poorly in environments that are even slightly
different. In robotics especially, training and deployment conditions often
vary and data collection is expensive, making retraining undesirable.
Simulation training allows for feasible training times, but on the other hand
suffers from a reality-gap when applied in real-world settings. This raises the
need of efficient adaptation of policies acting in new environments. We
consider this as a problem of transferring knowledge within a family of similar
Markov decision processes.
  For this purpose we assume that Q-functions are generated by some
low-dimensional latent variable. Given such a Q-function, we can find a master
policy that can adapt given different values of this latent variable. Our
method learns both the generative mapping and an approximate posterior of the
latent variables, enabling identification of policies for new tasks by
searching only in the latent space, rather than the space of all policies. The
low-dimensional space, and master policy found by our method enables policies
to quickly adapt to new environments. We demonstrate the method on both a
pendulum swing-up task in simulation, and for simulation-to-real transfer on a
pushing task.
Bayesian Patchworks: An Approach to Case-Based Reasoning
Authors: Ramin Moghaddass, Cynthia Rudin
Date: 2018-09-10 00:00:00
Tasks: Computational Efficiency, Diagnostic, Disease Prediction

Doctors often rely on their past experience in order to diagnose patients.
For a doctor with enough experience, almost every patient would have
similarities to key cases seen in the past, and each new patient could be
viewed as a mixture of these key past cases. Because doctors often tend to
reason this way, an efficient computationally aided diagnostic tool that thinks
in the same way might be helpful in locating key past cases of interest that
could assist with diagnosis. This article develops a novel mathematical model
to mimic the type of logical thinking that physicians use when considering past
cases. The proposed model can also provide physicians with explanations that
would be similar to the way they would naturally reason about cases. The
proposed method is designed to yield predictive accuracy, computational
efficiency, and insight into medical data; the key element is the insight into
medical data, in some sense we are automating a complicated process that
physicians might perform manually. We finally implemented the result of this
work on two publicly available healthcare datasets, for heart disease
prediction and breast cancer prediction.
Annotating shadows, highlights and faces: the contribution of a 'human in the loop' for digital art history
Authors: Maarten W. A. Wijntjes
Date: 2018-09-10 00:00:00

While automatic computational techniques appear to reveal novel insights in
digital art history, a complementary approach seems to get less attention: that
of human annotation. We argue and exemplify that a 'human in the loop' can
reveal insights that may be difficult to detect automatically. Specifically, we
focussed on perceptual aspects within pictorial art. Using rather simple
annotation tasks (e.g. delineate human lengths, indicate highlights and
classify gaze direction) we could both replicate earlier findings and reveal
novel insights into pictorial conventions. We found that Canaletto depicted
human figures in rather accurate perspective, varied viewpoint elevation
between approximately 3 and 9 meters and highly preferred light directions
parallel to the projection plane. Furthermore, we found that taking the
averaged images of leftward looking faces reveals a woman, and for rightward
looking faces showed a male, confirming earlier accounts on lateral gender bias
in pictorial art. Lastly, we confirmed and refined the well-known
light-from-the-left bias. Together, the annotations, analyses and results
exemplify how human annotation can contribute and complement to technical and
digital art history.
Convolutional Graph Auto-encoder: A Deep Generative Neural Architecture for Probabilistic Spatio-temporal Solar Irradiance Forecasting
Authors: Mahdi Khodayar, Saeed Mohammadi, Mohammad Khodayar, Jianhui Wang, Guangyi Liu
Date: 2018-09-10 00:00:00
Tasks: Anomaly Detection, Bayesian Inference, Solar Irradiance Forecasting

Machine Learning on graph-structured data is an important and omnipresent
task for a vast variety of applications including anomaly detection and dynamic
network analysis. In this paper, a deep generative model is introduced to
capture continuous probability densities corresponding to the nodes of an
arbitrary graph. In contrast to all learning formulations in the area of
discriminative pattern recognition, we propose a scalable generative
optimization/algorithm theoretically proved to capture distributions at the
nodes of a graph. Our model is able to generate samples from the probability
densities learned at each node. This probabilistic data generation model, i.e.
convolutional graph auto-encoder (CGAE), is devised based on the localized
first-order approximation of spectral graph convolutions, deep learning, and
the variational Bayesian inference. We apply our CGAE to a new problem, the
spatio-temporal probabilistic solar irradiance prediction. Multiple solar
radiation measurement sites in a wide area in northern states of the US are
modeled as an undirected graph. Using our proposed model, the distribution of
future irradiance given historical radiation observations is estimated for
every site/node. Numerical results on the National Solar Radiation Database
show state-of-the-art performance for probabilistic radiation prediction on
geographically distributed irradiance data in terms of reliability, sharpness,
and continuous ranked probability score.