pwc-archive/papers-with-abstracts huggingface dataset:
Infrastructure for the representation and electronic exchange of design knowledge
Authors: Laurent Buzon, Abdelaziz Bouras, Yacine Ouzrout
Date: 2018-10-31 00:00:00

This paper develops the concept of knowledge and its exchange using Semantic
Web technologies. It points out that knowledge is more than information because
it embodies the meaning, that is to say semantic and context. These
characteristics will influence our approach to represent and to treat the
knowledge. In order to be adopted, the developed system needs to be simple and
to use standards. The goal of the paper is to find standards to model knowledge
and exchange it with an other person. Therefore, we propose to model knowledge
using UML models to show a graphical representation and to exchange it with XML
to ensure the portability at low cost. We introduce the concept of ontology for
organizing knowledge and for facilitating the knowledge exchange. Proposals
have been tested by implementing an application on the design knowledge of a
pen.
WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community
Authors: Yiqing Hua, Cristian Danescu-Niculescu-Mizil, Dario Taraborelli, Nithum Thain, Jeffery Sorensen, Lucas Dixon
Date: 2018-10-31 00:00:00
Proceeding: EMNLP 2018 10

We present a corpus that encompasses the complete history of conversations
between contributors to Wikipedia, one of the largest online collaborative
communities. By recording the intermediate states of conversations---including
not only comments and replies, but also their modifications, deletions and
restorations---this data offers an unprecedented view of online conversation.
This level of detail supports new research questions pertaining to the process
(and challenges) of large-scale online collaboration. We illustrate the corpus'
potential with two case studies that highlight new perspectives on earlier
work. First, we explore how a person's conversational behavior depends on how
they relate to the discussion's venue. Second, we show that community
moderation of toxic behavior happens at a higher rate than previously
estimated. Finally the reconstruction framework is designed to be language
agnostic, and we show that it can extract high quality conversational data in
both Chinese and English.
Face Presentation Attack Detection in Learned Color-liked Space
Authors: Lei Li, Zhaoqiang Xia, Xiaoyue Jiang, Fabio Roli, Xiaoyi Feng
Date: 2018-10-31 00:00:00
Tasks: Face Presentation Attack Detection, Generative Adversarial Network, Triplet

Face presentation attack detection (PAD) has become a thorny problem for
biometric systems and numerous countermeasures have been proposed to address
it. However, majority of them directly extract feature descriptors and
distinguish fake faces from the real ones in existing color spaces (e.g. RGB,
HSV and YCbCr). Unfortunately, it is unknown for us which color space is the
best or how to combine different spaces together. To make matters worse, the
real and fake faces are overlapped in existing color spaces. So, in this paper,
a learned distinguishable color-liked space is generated to deal with the
problem of face PAD. More specifically, we present an end-to-end deep learning
network that can map existing color spaces to a new learned color-liked space.
Inspired by the generator of generative adversarial network (GAN), the proposed
network consists of a space generator and a feature extractor. When training
the color-liked space, a new triplet combination mechanism of points-to-center
is explored to maximize interclass distance and minimize intraclass distance,
and also keep a safe margin between the real and presented fake faces.
Extensive experiments on two standard face PAD databases, i.e., Relay-Attack
and OULU-NPU, indicate that our proposed color-liked space analysis based
countermeasure significantly outperforms the state-of-the-art methods and show
excellent generalization capability.
Inception-Residual Block based Neural Network for Thermal Image Denoising
Authors: Seongmin Hwang, Gwanghyun Yu, Huy Toan Nguyen, Nazeer Shahid, Doseong Sin, Jin-Young Kim, Seungyou Na
Date: 2018-10-31 00:00:00
Tasks: Denoising, Image Denoising, Thermal Image Denoising

Thermal cameras show noisy images due to their limited thermal resolution,
especially for the scenes of a low temperature difference. In order to deal
with a noise problem, this paper proposes a novel neural network architecture
with repeatable denoising inception-residual blocks(DnIRB) for noise learning.
Each DnIRB has two sub-blocks with difference receptive fields and one shortcut
connection to prevent a vanishing gradient problem. The proposed approach is
tested for thermal images. The experimental results indicate that the proposed
approach shows the best SQNR performance and reasonable processing time
compared with state-of-the-art denoising methods.
Don't forget, there is more than forgetting: new metrics for Continual Learning
Authors: Natalia Díaz-Rodríguez, Vincenzo Lomonaco, David Filliat, Davide Maltoni
Date: 2018-10-31 00:00:00
Tasks: Attribute, Computational Efficiency, Continual Learning, Transfer Learning

Continual learning consists of algorithms that learn from a stream of
data/tasks continuously and adaptively thought time, enabling the incremental
development of ever more complex knowledge and skills. The lack of consensus in
evaluating continual learning algorithms and the almost exclusive focus on
forgetting motivate us to propose a more comprehensive set of implementation
independent metrics accounting for several factors we believe have practical
implications worth considering in the deployment of real AI systems that learn
continually: accuracy or performance over time, backward and forward knowledge
transfer, memory overhead as well as computational efficiency. Drawing
inspiration from the standard Multi-Attribute Value Theory (MAVT) we further
propose to fuse these metrics into a single score for ranking purposes and we
evaluate our proposal with five continual learning strategies on the iCIFAR-100
continual learning benchmark.
A tutorial on MDL hypothesis testing for graph analysis
Authors: Peter Bloem, Steven de Rooij
Date: 2018-10-31 00:00:00
Tasks: Two-sample testing

This document provides a tutorial description of the use of the MDL principle
in complex graph analysis. We give a brief summary of the preliminary subjects,
and describe the basic principle, using the example of analysing the size of
the largest clique in a graph. We also provide a discussion of how to interpret
the results of such an analysis, making note of several common pitfalls.
Structure Learning of Deep Neural Networks with Q-Learning
Authors: Guoqiang Zhong, Wencong Jiao, Wei Gao
Date: 2018-10-31 00:00:00
Tasks: image-classification, Image Classification, Q-Learning, Reinforcement Learning

Recently, with convolutional neural networks gaining significant achievements
in many challenging machine learning fields, hand-crafted neural networks no
longer satisfy our requirements as designing a network will cost a lot, and
automatically generating architectures has attracted increasingly more
attention and focus. Some research on auto-generated networks has achieved
promising results. However, they mainly aim at picking a series of single
layers such as convolution or pooling layers one by one. There are many elegant
and creative designs in the carefully hand-crafted neural networks, such as
Inception-block in GoogLeNet, residual block in residual network and dense
block in dense convolutional network. Based on reinforcement learning and
taking advantages of the superiority of these networks, we propose a novel
automatic process to design a multi-block neural network, whose architecture
contains multiple types of blocks mentioned above, with the purpose to do
structure learning of deep neural networks and explore the possibility whether
different blocks can be composed together to form a well-behaved neural
network. The optimal network is created by the Q-learning agent who is trained
to sequentially pick different types of blocks. To verify the validity of our
proposed method, we use the auto-generated multi-block neural network to
conduct experiments on image benchmark datasets MNIST, SVHN and CIFAR-10 image
classification task with restricted computational resources. The results
demonstrate that our method is very effective, achieving comparable or better
performance than hand-crafted networks and advanced auto-generated neural
networks.
Towards a more efficient use of process and product traceability data for continuous improvement of industrial performances
Authors: Thierno Diallo, Sébastien Henry, Yacine Ouzrout
Date: 2018-10-31 00:00:00
Tasks: Prognosis

Nowadays all industrial sectors are increasingly faced with the explosion in
the amount of data. Therefore, it raises the question of the efficient use of
this large amount of data. In this research work, we are concerned with process
and product traceability data. In some sectors (e.g. pharmaceutical and
agro-food), the collection and storage of these data are required. Beyond this
constraint (regulatory and / or contractual), we are interested in the use of
these data for continuous improvements of industrial performances. Two research
axes were identified: product recall and responsiveness towards production
hazards. For the first axis, a procedure for product recall exploiting
traceability data will be propose. The development of detection and prognosis
functions combining process and product data is envisaged for the second axis.
Adaptive Extreme Learning Machine for Recurrent Beta-basis Function Neural Network Training
Authors: Naima Chouikhi, Adel M. ALIMI
Date: 2018-10-31 00:00:00
Tasks: Time Series Analysis, Time Series Prediction

Beta Basis Function Neural Network (BBFNN) is a special kind of kernel basis
neural networks. It is a feedforward network typified by the use of beta
function as a hidden activation function. Beta is a flexible transfer function
representing richer forms than the common existing functions. As in every
network, the architecture setting as well as the learning method are two main
gauntlets faced by BBFNN. In this paper, new architecture and training
algorithm are proposed for the BBFNN. An Extreme Learning Machine (ELM) is used
as a training approach of BBFNN with the aim of quickening the training
process. The peculiarity of ELM is permitting a certain decrement of the
computing time and complexity regarding the already used BBFNN learning
algorithms such as backpropagation, OLS, etc. For the architectural design, a
recurrent structure is added to the common BBFNN architecture in order to make
it more able to deal with complex, non linear and time varying problems.
Throughout this paper, the conceived recurrent ELM-trained BBFNN is tested on a
number of tasks related to time series prediction, classification and
regression. Experimental results show noticeable achievements of the proposed
network compared to common feedforward and recurrent networks trained by ELM
and using hyperbolic tangent as activation function. These achievements are in
terms of accuracy and robustness against data breakdowns such as noise signals.
The Effect of Learning Strategy versus Inherent Architecture Properties on the Ability of Convolutional Neural Networks to Develop Transformation Invariance
Authors: Megha Srivastava, Kalanit Grill-Spector
Date: 2018-10-31 00:00:00
Tasks: Object Recognition

As object recognition becomes an increasingly common ML task, and recent
research demonstrating CNNs vulnerability to attacks and small image
perturbations necessitate fully understanding the foundations of object
recognition. We focus on understanding the mechanisms behind how neural
networks generalize to spatial transformations of complex objects. While humans
excel at discriminating between objects shown at new positions, orientations,
and scales, past results demonstrate that this may be limited to familiar
objects - humans demonstrate low tolerance of spatial-variances for
purposefully constructed novel objects. Because training artificial neural
networks from scratch is similar to showing novel objects to humans, we seek to
understand the factors influencing the tolerance of CNNs to spatial
transformations. We conduct a thorough empirical examination of seven
Convolutional Neural Network (CNN) architectures. By training on a controlled
face image dataset, we measure model accuracy across different degrees of 5
transformations: position, size, rotation, Gaussian blur, and resolution
transformation due to resampling. We also examine how learning strategy affects
generalizability by examining how different amounts of pre-training have on
model robustness. Overall, we find that the most significant contributor to
transformation invariance is pre-training on a large, diverse image dataset.
Moreover, while AlexNet tends to be the least robust network, VGG and ResNet
architectures demonstrate higher robustness for different transformations.
Along with kernel visualizations and qualitative analyses, we examine
differences between learning strategy and inherent architectural properties in
contributing to invariance of transformations, providing valuable information
towards understanding how to achieve greater robustness to transformations in
CNNs.
Compact Generalized Non-local Network
Authors: Kaiyu Yue, Ming Sun, Yuchen Yuan, Feng Zhou, Errui Ding, Fuxin Xu
Date: 2018-10-31 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Object Detection, Object Recognition, Video Classification

The non-local module is designed for capturing long-range spatio-temporal
dependencies in images and videos. Although having shown excellent performance,
it lacks the mechanism to model the interactions between positions across
channels, which are of vital importance in recognizing fine-grained objects and
actions. To address this limitation, we generalize the non-local module and
take the correlations between the positions of any two channels into account.
This extension utilizes the compact representation for multiple kernel
functions with Taylor expansion that makes the generalized non-local module in
a fast and low-complexity computation flow. Moreover, we implement our
generalized non-local method within channel groups to ease the optimization.
Experimental results illustrate the clear-cut improvements and practical
applicability of the generalized non-local module on both fine-grained object
recognition and video classification. Code is available at:
https://github.com/KaiyuYue/cgnl-network.pytorch.
SplineNets: Continuous Neural Decision Graphs
Authors: Cem Keskin, Shahram Izadi
Date: 2018-10-31 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Position

We present SplineNets, a practical and novel approach for using conditioning
in convolutional neural networks (CNNs). SplineNets are continuous
generalizations of neural decision graphs, and they can dramatically reduce
runtime complexity and computation costs of CNNs, while maintaining or even
increasing accuracy. Functions of SplineNets are both dynamic (i.e.,
conditioned on the input) and hierarchical (i.e., conditioned on the
computational path). SplineNets employ a unified loss function with a desired
level of smoothness over both the network and decision parameters, while
allowing for sparse activation of a subset of nodes for individual samples. In
particular, we embed infinitely many function weights (e.g. filters) on smooth,
low dimensional manifolds parameterized by compact B-splines, which are indexed
by a position parameter. Instead of sampling from a categorical distribution to
pick a branch, samples choose a continuous position to pick a function weight.
We further show that by maximizing the mutual information between spline
positions and class labels, the network can be optimally utilized and
specialized for classification tasks. Experiments show that our approach can
significantly increase the accuracy of ResNets with negligible cost in speed,
matching the precision of a 110 level ResNet with a 32 level SplineNet.
Giving Space to Your Message: Assistive Word Segmentation for the Electronic Typing of Digital Minorities
Authors: Won Ik Cho, Sung Jun Cheon, Woo Hyun Kang, Ji Won Kim, Nam Soo Kim
Date: 2018-10-31 00:00:00
Tasks: Segmentation

For readability and disambiguation of the written text, appropriate word segmentation is recommended for documentation, and it also holds for the digitized texts. If the language is agglutinative while far from scriptio continua, for instance in the Korean language, the problem becomes more significant. However, some device users these days find it challenging to communicate via key stroking, not only for handicap but also for being unskilled. In this study, we propose a real-time assistive technology that utilizes an automatic word segmentation, designed for digital minorities who are not familiar with electronic typing. We propose a data-driven system trained upon a spoken Korean language corpus with various non-canonical expressions and dialects, guaranteeing the comprehension of contextual information. Through quantitative and qualitative comparison with other text processing toolkits, we show the reliability of the proposed system and its fit with colloquial and non-normalized texts, which fulfills the aim of supportive technology.
A general system of differential equations to model first order adaptive algorithms
Authors: André Belotto da Silva, Maxime Gazeau
Date: 2018-10-31 00:00:00

First order optimization algorithms play a major role in large scale machine learning. A new class of methods, called adaptive algorithms, were recently introduced to adjust iteratively the learning rate for each coordinate. Despite great practical success in deep learning, their behavior and performance on more general loss functions are not well understood. In this paper, we derive a non-autonomous system of differential equations, which is the continuous time limit of adaptive optimization methods. We prove global well-posedness of the system and we investigate the numerical time convergence of its forward Euler approximation. We study, furthermore, the convergence of its trajectories and give conditions under which the differential system, underlying all adaptive algorithms, is suitable for optimization. We discuss convergence to a critical point in the non-convex case and give conditions for the dynamics to avoid saddle points and local maxima. For convex and deterministic loss function, we introduce a suitable Lyapunov functional which allow us to study its rate of convergence. Several other properties of both the continuous and discrete systems are briefly discussed. The differential system studied in the paper is general enough to encompass many other classical algorithms (such as Heavy ball and Nesterov's accelerated method) and allow us to recover several known results for these algorithms.
End-to-End Feedback Loss in Speech Chain Framework via Straight-Through Estimator
Authors: Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
Date: 2018-10-31 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

The speech chain mechanism integrates automatic speech recognition (ASR) and
text-to-speech synthesis (TTS) modules into a single cycle during training. In
our previous work, we applied a speech chain mechanism as a semi-supervised
learning. It provides the ability for ASR and TTS to assist each other when
they receive unpaired data and let them infer the missing pair and optimize the
model with reconstruction loss. If we only have speech without transcription,
ASR generates the most likely transcription from the speech data, and then TTS
uses the generated transcription to reconstruct the original speech features.
However, in previous papers, we just limited our back-propagation to the
closest module, which is the TTS part. One reason is that back-propagating the
error through the ASR is challenging due to the output of the ASR are discrete
tokens, creating non-differentiability between the TTS and ASR. In this paper,
we address this problem and describe how to thoroughly train a speech chain
end-to-end for reconstruction loss using a straight-through estimator (ST).
Experimental results revealed that, with sampling from ST-Gumbel-Softmax, we
were able to update ASR parameters and improve the ASR performances by 11\%
relative CER reduction compared to the baseline.
DBSCAN++: Towards fast and scalable density clustering
Authors: Jennifer Jang, Heinrich Jiang
Date: 2018-10-31 00:00:00
Tasks: Clustering

DBSCAN is a classical density-based clustering procedure with tremendous practical relevance. However, DBSCAN implicitly needs to compute the empirical density for each sample point, leading to a quadratic worst-case time complexity, which is too slow on large datasets. We propose DBSCAN++, a simple modification of DBSCAN which only requires computing the densities for a chosen subset of points. We show empirically that, compared to traditional DBSCAN, DBSCAN++ can provide not only competitive performance but also added robustness in the bandwidth hyperparameter while taking a fraction of the runtime. We also present statistical consistency guarantees showing the trade-off between computational cost and estimation rates. Surprisingly, up to a certain point, we can enjoy the same estimation rates while lowering computational cost, showing that DBSCAN++ is a sub-quadratic algorithm that attains minimax optimal rates for level-set estimation, a quality that may be of independent interest.
Audio Source Separation Using Variational Autoencoders and Weak Class Supervision
Authors: Ertuğ Karamatlı, Ali Taylan Cemgil, Serap Kırbız
Date: 2018-10-31 00:00:00
Tasks: Audio Source Separation, Denoising

In this paper, we propose a source separation method that is trained by observing the mixtures and the class labels of the sources present in the mixture without any access to isolated sources. Since our method does not require source class labels for every time-frequency bin but only a single label for each source constituting the mixture signal, we call this scenario as weak class supervision. We associate a variational autoencoder (VAE) with each source class within a non-negative (compositional) model. Each VAE provides a prior model to identify the signal from its associated class in a sound mixture. After training the model on mixtures, we obtain a generative model for each source class and demonstrate our method on one-second mixtures of utterances of digits from 0 to 9. We show that the separation performance obtained by source class supervision is as good as the performance obtained by source signal supervision.