pwc-archive/papers-with-abstracts huggingface dataset:
AIQ: Measuring Intelligence of Business AI Software
Authors: Moshe Ben-Bassat
Date: 2018-08-10 00:00:00
Tasks: Decision Making, Scheduling

Focusing on Business AI, this article introduces the AIQ quadrant that
enables us to measure AI for business applications in a relative comparative
manner, i.e. to judge that software A has more or less intelligence than
software B. Recognizing that the goal of Business software is to maximize value
in terms of business results, the dimensions of the quadrant are the key
factors that determine the business value of AI software: Level of Output
Quality (Smartness) and Level of Automation. The use of the quadrant is
illustrated by several software solutions to support the real life business
challenge of field service scheduling. The role of machine learning and
conversational digital assistants in increasing the business value are also
discussed and illustrated with a recent integration of existing intelligent
digital assistants for factory floor decision making with the new version of
Google Glass. Such hands free AI solutions elevate the AIQ level to its
ultimate position.
Hybrid approach for transliteration of Algerian arabizi: a primary study
Authors: Imane Guellil, Faical Azouaou, Fodil Benali, Ala-eddine Hachani, Houda Saadane
Date: 2018-08-10 00:00:00
Tasks: Transliteration

A hybrid approach for the transliteration of Algerian Arabizi: A primary
study In this paper, we present a hybrid approach for the transliteration of
the Algerian Arabizi. We define a set of rules enable us the passage from
Arabizi to Arabic. Through these rules, we generate a set of candidates for the
transliteration of each Arabizi word into arabic. Then, we extract the best
candidate. This approach was evaluated by using three test corpora, and the
obtained results show an improvement of the precision score which is equal to
75.11% for the best result. These results allow us to verify that our approach
is very competitive comparing to others works that treat Arabizi
transliteration in general.
  Keywords: Arabizi, Dialecte Alg\'erien, Arabizi Alg\'erien,
Translit\'eration.
Lingke: A Fine-grained Multi-turn Chatbot for Customer Service
Authors: Pengfei Zhu, Zhuosheng Zhang, Jiangtong Li, Yafang Huang, Hai Zhao
Date: 2018-08-10 00:00:00
Proceeding: COLING 2018 8
Tasks: BIG-bench Machine Learning, Chatbot, Information Retrieval, Question Answering, Retrieval

Traditional chatbots usually need a mass of human dialogue data, especially
when using supervised machine learning method. Though they can easily deal with
single-turn question answering, for multi-turn the performance is usually
unsatisfactory. In this paper, we present Lingke, an information retrieval
augmented chatbot which is able to answer questions based on given product
introduction document and deal with multi-turn conversations. We will introduce
a fine-grained pipeline processing to distill responses based on unstructured
documents, and attentive sequential context-response matching for multi-turn
conversations.
WonDerM: Skin Lesion Classification with Fine-tuned Neural Networks
Authors: Yeong Chan Lee, Sang-Hyuk Jung, Hong-Hee Won
Date: 2018-08-10 00:00:00
Tasks: Classification, General Classification, Lesion Classification, Skin Lesion Classification

As skin cancer is one of the most frequent cancers globally, accurate, non-invasive dermoscopy-based diagnosis becomes essential and promising. A task of the Part 3 of the ISIC Skin Image Analysis Challenge at MICCAI 2018 is to predict seven disease classes with skin lesion images, including melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis / Bowen's disease (intraepithelial carcinoma) (AKIEC), benign keratosis (solar lentigo / seborrheic keratosis / lichen planus-like keratosis) (BKL), dermatofibroma (DF) and vascular lesion (VASC) as defined by the International Dermatology Society. In this work, we design the WonDerM pipeline, that resamples the preprocessed skin lesion images, builds neural network architecture fine-tuned with segmentation task data (the Part 1), and uses an ensemble method to classify the seven skin diseases. Our model achieved an accuracy of 0.899 and 0.785 in the validation set and test set, respectively.
Learning and Inference on Generative Adversarial Quantum Circuits
Authors: Jinfeng Zeng, Yufeng Wu, Jin-Guo Liu, Lei Wang, Jiangping Hu
Date: 2018-08-10 00:00:00

Quantum mechanics is inherently probabilistic in light of Born's rule. Using
quantum circuits as probabilistic generative models for classical data exploits
their superior expressibility and efficient direct sampling ability. However,
training of quantum circuits can be more challenging compared to classical
neural networks due to lack of efficient differentiable learning algorithm. We
devise an adversarial quantum-classical hybrid training scheme via coupling a
quantum circuit generator and a classical neural network discriminator
together. After training, the quantum circuit generative model can infer
missing data with quadratic speed up via amplitude amplification. We
numerically simulate the learning and inference of generative adversarial
quantum circuit using the prototypical Bars-and-Stripes dataset. Generative
adversarial quantum circuits is a fresh approach to machine learning which may
enjoy the practically useful quantum advantage on near-term quantum devices.
Hierarchical Block Sparse Neural Networks
Authors: Dharma Teja Vooturi, Dheevatsa Mudigere, Sasikanth Avancha
Date: 2018-08-10 00:00:00

Sparse deep neural networks(DNNs) are efficient in both memory and compute
when compared to dense DNNs. But due to irregularity in computation of sparse
DNNs, their efficiencies are much lower than that of dense DNNs on regular
parallel hardware such as TPU. This inefficiency leads to poor/no performance
benefits for sparse DNNs. Performance issue for sparse DNNs can be alleviated
by bringing structure to the sparsity and leveraging it for improving runtime
efficiency. But such structural constraints often lead to suboptimal
accuracies. In this work, we jointly address both accuracy and performance of
sparse DNNs using our proposed class of sparse neural networks called HBsNN
(Hierarchical Block sparse Neural Networks). For a given sparsity, HBsNN models
achieve better runtime performance than unstructured sparse models and better
accuracy than highly structured sparse models.
DeepWrinkles: Accurate and Realistic Clothing Modeling
Authors: Zorah Laehner, Daniel Cremers, Tony Tung
Date: 2018-08-10 00:00:00
Proceeding: ECCV 2018 9
Tasks: Generative Adversarial Network

We present a novel method to generate accurate and realistic clothing
deformation from real data capture. Previous methods for realistic cloth
modeling mainly rely on intensive computation of physics-based simulation (with
numerous heuristic parameters), while models reconstructed from visual
observations typically suffer from lack of geometric details. Here, we propose
an original framework consisting of two modules that work jointly to represent
global shape deformation as well as surface details with high fidelity. Global
shape deformations are recovered from a subspace model learned from 3D data of
clothed people in motion, while high frequency details are added to normal maps
created using a conditional Generative Adversarial Network whose architecture
is designed to enforce realism and temporal consistency. This leads to
unprecedented high-quality rendering of clothing deformation sequences, where
fine wrinkles from (real) high resolution observations can be recovered. In
addition, as the model is learned independently from body shape and pose, the
framework is suitable for applications that require retargeting (e.g., body
animation). Our experiments show original high quality results with a flexible
model. We claim an entirely data-driven approach to realistic cloth wrinkle
generation is possible.
A Unified Analysis of AdaGrad with Weighted Aggregation and Momentum Acceleration
Authors: Li Shen, Congliang Chen, Fangyu Zou, Zequn Jie, Ju Sun, Wei Liu
Date: 2018-08-10 00:00:00
Tasks: Stochastic Optimization

Integrating adaptive learning rate and momentum techniques into SGD leads to a large class of efficiently accelerated adaptive stochastic algorithms, such as AdaGrad, RMSProp, Adam, AccAdaGrad, \textit{etc}. In spite of their effectiveness in practice, there is still a large gap in their theories of convergences, especially in the difficult non-convex stochastic setting. To fill this gap, we propose \emph{weighted AdaGrad with unified momentum}, dubbed AdaUSM, which has the main characteristics that (1) it incorporates a unified momentum scheme which covers both the heavy ball momentum and the Nesterov accelerated gradient momentum; (2) it adopts a novel weighted adaptive learning rate that can unify the learning rates of AdaGrad, AccAdaGrad, Adam, and RMSProp. Moreover, when we take polynomially growing weights in AdaUSM, we obtain its $\mathcal{O}(\log(T)/\sqrt{T})$ convergence rate in the non-convex stochastic setting. We also show that the adaptive learning rates of Adam and RMSProp correspond to taking exponentially growing weights in AdaUSM, thereby providing a new perspective for understanding Adam and RMSProp. Lastly, comparative experiments of AdaUSM against SGD with momentum, AdaGrad, AdaEMA, Adam, and AMSGrad on various deep learning models and datasets are also carried out.
End-to-end Active Object Tracking and Its Real-world Deployment via Reinforcement Learning
Authors: Wenhan Luo, Peng Sun, Fangwei Zhong, Wei Liu, Tong Zhang, Yizhou Wang
Date: 2018-08-10 00:00:00
Tasks: Deep Reinforcement Learning, Object, Object Tracking, Reinforcement Learning, Reinforcement Learning (RL)

We study active object tracking, where a tracker takes visual observations
(i.e., frame sequences) as input and produces the corresponding camera control
signals as output (e.g., move forward, turn left, etc.). Conventional methods
tackle tracking and camera control tasks separately, and the resulting system
is difficult to tune jointly. These methods also require significant human
efforts for image labeling and expensive trial-and-error system tuning in the
real world. To address these issues, we propose, in this paper, an end-to-end
solution via deep reinforcement learning. A ConvNet-LSTM function approximator
is adopted for the direct frame-to-action prediction. We further propose an
environment augmentation technique and a customized reward function, which are
crucial for successful training. The tracker trained in simulators (ViZDoom and
Unreal Engine) demonstrates good generalization behaviors in the case of unseen
object moving paths, unseen object appearances, unseen backgrounds, and
distracting objects. The system is robust and can restore tracking after
occasional lost of the target being tracked. We also find that the tracking
ability, obtained solely from simulators, can potentially transfer to
real-world scenarios. We demonstrate successful examples of such transfer, via
experiments over the VOT dataset and the deployment of a real-world robot using
the proposed active tracker trained in simulation.
Distinctiveness, complexity, and repeatability of online signature templates
Authors: NapaSae-Bae, NasirMemon, Pitikhate Sooraksa
Date: 2018-08-10 00:00:00

This paper proposes three measures to quantify the characteristics of online
signature templates in terms of distinctiveness, complexity and repeatability.
A distinctiveness measure of a signature template is computed from a set of
enrolled signature samples and a statistical assumption about random
signatures. Secondly, a complexity measure of the template is derived from a
set of enrolled signature samples. Finally, given a signature template, a
measure to quantify the repeatability of the online signature is derived from a
validation set of samples. These three measures can then be used as an
indicator for the performance of the system in rejecting random forgery samples
and skilled forgery samples and the performance of users in providing accepted
genuine samples, respectively. The effectiveness of these three measures and
their applications are demonstrated through experiments performed on three
online signature datasets and one keystroke dynamics dataset using different
verification algorithms.
Code-division multiplexed resistive pulse sensor networks for spatio-temporal detection of particles in microfluidic devices
Authors: Ningquan Wang, Ruxiu Liu, Roozbeh Khodambashi, Norh Asmare, A. Fatih Sarioglu
Date: 2018-08-10 00:00:00

Spatial separation of suspended particles based on contrast in their physical
or chemical properties forms the basis of various biological assays performed
on lab-on-achip devices. To electronically acquire this information, we have
recently introduced a microfluidic sensing platform, called Microfluidic CODES,
which combines the resistive pulse sensing with the code division multiple
access in multiplexing a network of integrated electrical sensors. In this
paper, we enhance the multiplexing capacity of the Microfluidic CODES by
employing sensors that generate non-orthogonal code waveforms and a new
decoding algorithm that combines machine learning techniques with minimum
mean-squared error estimation. As a proof of principle, we fabricated a
microfluidic device with a network of 10 code-multiplexed sensors and
characterized it using cells suspended in phosphate buffer saline solution.
Fast Flexible Function Dispatch in Julia
Authors: Jeff Bezanson, Jake Bolewski, Jiahao Chen
Date: 2018-08-09 00:00:00

Technical computing is a challenging application area for programming
languages to address. This is evinced by the unusually large number of
specialized languages in the area (e.g. MATLAB, R), and the complexity of
common software stacks, often involving multiple languages and custom code
generators. We believe this is ultimately due to key characteristics of the
domain: highly complex operators, a need for extensive code specialization for
performance, and a desire for permissive high-level programming styles allowing
productive experimentation. The Julia language attempts to provide a more
effective structure for this kind of programming by allowing programmers to
express complex polymorphic behaviors using dynamic multiple dispatch over
parametric types. The forms of extension and reuse permitted by this paradigm
have proven valuable for technical computing. We report on how this approach
has allowed domain experts to express useful abstractions while simultaneously
providing a natural path to better performance for high-level technical code.
A Panel Quantile Approach to Attrition Bias in Big Data: Evidence from a Randomized Experiment
Authors: Matthew Harding, Carlos Lamarche
Date: 2018-08-09 00:00:00
Tasks: quantile regression

This paper introduces a quantile regression estimator for panel data models
with individual heterogeneity and attrition. The method is motivated by the
fact that attrition bias is often encountered in Big Data applications. For
example, many users sign-up for the latest program but few remain active users
several months later, making the evaluation of such interventions inherently
very challenging. Building on earlier work by Hausman and Wise (1979), we
provide a simple identification strategy that leads to a two-step estimation
procedure. In the first step, the coefficients of interest in the selection
equation are consistently estimated using parametric or nonparametric methods.
In the second step, standard panel quantile methods are employed on a subset of
weighted observations. The estimator is computationally easy to implement in
Big Data applications with a large number of subjects. We investigate the
conditions under which the parameter estimator is asymptotically Gaussian and
we carry out a series of Monte Carlo simulations to investigate the finite
sample properties of the estimator. Lastly, using a simulation exercise, we
apply the method to the evaluation of a recent Time-of-Day electricity pricing
experiment inspired by the work of Aigner and Hausman (1980).
Error Forward-Propagation: Reusing Feedforward Connections to Propagate Errors in Deep Learning
Authors: Adam A. Kohan, Edward A. Rietman, Hava T. Siegelmann
Date: 2018-08-09 00:00:00

We introduce Error Forward-Propagation, a biologically plausible mechanism to
propagate error feedback forward through the network. Architectural constraints
on connectivity are virtually eliminated for error feedback in the brain;
systematic backward connectivity is not used or needed to deliver error
feedback. Feedback as a means of assigning credit to neurons earlier in the
forward pathway for their contribution to the final output is thought to be
used in learning in the brain. How the brain solves the credit assignment
problem is unclear. In machine learning, error backpropagation is a highly
successful mechanism for credit assignment in deep multilayered networks.
Backpropagation requires symmetric reciprocal connectivity for every neuron.
From a biological perspective, there is no evidence of such an architectural
constraint, which makes backpropagation implausible for learning in the brain.
This architectural constraint is reduced with the use of random feedback
weights. Models using random feedback weights require backward connectivity
patterns for every neuron, but avoid symmetric weights and reciprocal
connections. In this paper, we practically remove this architectural
constraint, requiring only a backward loop connection for effective error
feedback. We propose reusing the forward connections to deliver the error
feedback by feeding the outputs into the input receiving layer. This mechanism,
Error Forward-Propagation, is a plausible basis for how error feedback occurs
deep in the brain independent of and yet in support of the functionality
underlying intricate network architectures. We show experimentally that
recurrent neural networks with two and three hidden layers can be trained using
Error Forward-Propagation on the MNIST and Fashion MNIST datasets, achieving
$1.90\%$ and $11\%$ generalization errors respectively.
Efficient human-like semantic representations via the Information Bottleneck principle
Authors: Noga Zaslavsky, Charles Kemp, Terry Regier, Naftali Tishby
Date: 2018-08-09 00:00:00
Tasks: Open-Ended Question Answering

Maintaining efficient semantic representations of the environment is a major
challenge both for humans and for machines. While human languages represent
useful solutions to this problem, it is not yet clear what computational
principle could give rise to similar solutions in machines. In this work we
propose an answer to this open question. We suggest that languages compress
percepts into words by optimizing the Information Bottleneck (IB) tradeoff
between the complexity and accuracy of their lexicons. We present empirical
evidence that this principle may give rise to human-like semantic
representations, by exploring how human languages categorize colors. We show
that color naming systems across languages are near-optimal in the IB sense,
and that these natural systems are similar to artificial IB color naming
systems with a single tradeoff parameter controlling the cross-language
variability. In addition, the IB systems evolve through a sequence of
structural phase transitions, demonstrating a possible adaptation process. This
work thus identifies a computational principle that characterizes human
semantic systems, and that could usefully inform semantic representations in
machines.
Exploiting Structure for Fast Kernel Learning
Authors: Trefor W. Evans, Prasanth B. Nair
Date: 2018-08-09 00:00:00
Tasks: Missing Values, Video Reconstruction

We propose two methods for exact Gaussian process (GP) inference and learning
on massive image, video, spatial-temporal, or multi-output datasets with
missing values (or "gaps") in the observed responses. The first method ignores
the gaps using sparse selection matrices and a highly effective low-rank
preconditioner is introduced to accelerate computations. The second method
introduces a novel approach to GP training whereby response values are inferred
on the gaps before explicitly training the model. We find this second approach
to be greatly advantageous for the class of problems considered. Both of these
novel approaches make extensive use of Kronecker matrix algebra to design
massively scalable algorithms which have low memory requirements. We
demonstrate exact GP inference for a spatial-temporal climate modelling problem
with 3.7 million training points as well as a video reconstruction problem with
1 billion points.
Uncovering the Spread of Chagas Disease in Argentina and Mexico
Authors: Juan de Monasterio, Alejo Salles, Carolina Lang, Diego Weinberg, Martin Minnoni, Matias Travizano, Carlos Sarraute
Date: 2018-08-09 00:00:00

Chagas disease is a neglected disease, and information about its geographical
spread is very scarse. We analyze here mobility and calling patterns in order
to identify potential risk zones for the disease, by using public health
information and mobile phone records. Geolocalized call records are rich in
social and mobility information, which can be used to infer whether an
individual has lived in an endemic area. We present two case studies in Latin
American countries. Our objective is to generate risk maps which can be used by
public health campaign managers to prioritize detection campaigns and target
specific areas. Finally, we analyze the value of mobile phone data to infer
long-term migrations, which play a crucial role in the geographical spread of
Chagas disease.
Deep Learning for Single Image Super-Resolution: A Brief Review
Authors: Wenming Yang, Xuechen Zhang, Yapeng Tian, Wei Wang, Jing-Hao Xue
Date: 2018-08-09 00:00:00
Tasks: Deep Learning, Efficient Neural Network, Image Super-Resolution, Super-Resolution

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.
DeepMag: Source Specific Motion Magnification Using Gradient Ascent
Authors: Weixuan Chen, Daniel McDuff
Date: 2018-08-09 00:00:00
Tasks: Motion Magnification

Many important physical phenomena involve subtle signals that are difficult
to observe with the unaided eye, yet visualizing them can be very informative.
Current motion magnification techniques can reveal these small temporal
variations in video, but require precise prior knowledge about the target
signal, and cannot deal with interference motions at a similar frequency. We
present DeepMag an end-to-end deep neural video-processing framework based on
gradient ascent that enables automated magnification of subtle color and motion
signals from a specific source, even in the presence of large motions of
various velocities. While the approach is generalizable, the advantages of
DeepMag are highlighted via the task of video-based physiological
visualization. Through systematic quantitative and qualitative evaluation of
the approach on videos with different levels of head motion, we compare the
magnification of pulse and respiration to existing state-of-the-art methods.
Our method produces magnified videos with substantially fewer artifacts and
blurring whilst magnifying the physiological changes by a similar degree.