pwc-archive/papers-with-abstracts huggingface dataset:
Web-Based Implementation of Travelling Salesperson Problem Using Genetic Algorithm
Authors: Aryo Pinandito, Novanto Yudistira, Fajar Pradana
Date: 2018-02-09 00:00:00
Tasks: Cloud Computing, Scheduling

The world is connected through the Internet. As the abundance of Internet
users connected into the Web and the popularity of cloud computing research,
the need of Artificial Intelligence (AI) is demanding. In this research,
Genetic Algorithm (GA) as AI optimization method through natural selection and
genetic evolution is utilized. There are many applications of GA such as web
mining, load balancing, routing, and scheduling or web service selection.
Hence, it is a challenging task to discover whether the code mainly server side
and web based language technology affects the performance of GA. Travelling
Salesperson Problem (TSP) as Non Polynomial-hard (NP-hard) problem is provided
to be a problem domain to be solved by GA. While many scientists prefer Python
in GA implementation, another popular high-level interpreter programming
language such as PHP (PHP Hypertext Preprocessor) and Ruby were benchmarked.
Line of codes, file sizes, and performances based on GA implementation and
runtime were found varies among these programming languages. Based on the
result, the use of Ruby in GA implementation is recommended.
Relational Autoencoder for Feature Extraction
Authors: Qinxue Meng, Daniel Catchpoole, David Skillicorn, Paul J. Kennedy
Date: 2018-02-09 00:00:00
Tasks: Denoising, General Classification, Skeleton Based Action Recognition

Feature extraction becomes increasingly important as data grows high
dimensional. Autoencoder as a neural network based feature extraction method
achieves great success in generating abstract features of high dimensional
data. However, it fails to consider the relationships of data samples which may
affect experimental results of using original and new features. In this paper,
we propose a Relation Autoencoder model considering both data features and
their relationships. We also extend it to work with other major autoencoder
models including Sparse Autoencoder, Denoising Autoencoder and Variational
Autoencoder. The proposed relational autoencoder models are evaluated on a set
of benchmark datasets and the experimental results show that considering data
relationships can generate more robust features which achieve lower
construction loss and then lower error rate in further classification compared
to the other variants of autoencoders.
Neural Dynamic Programming for Musical Self Similarity
Authors: Christian J. Walder, Dongwoo Kim
Date: 2018-02-09 00:00:00
Proceeding: ICML 2018 7

We present a neural sequence model designed specifically for symbolic music.
The model is based on a learned edit distance mechanism which generalises a
classic recursion from computer sci- ence, leading to a neural dynamic program.
Re- peated motifs are detected by learning the transfor- mations between them.
We represent the arising computational dependencies using a novel data
structure, the edit tree; this perspective suggests natural approximations
which afford the scaling up of our otherwise cubic time algorithm. We
demonstrate our model on real and synthetic data; in all cases it out-performs
a strong stacked long short-term memory benchmark.
Pretraining Deep Actor-Critic Reinforcement Learning Algorithms With Expert Demonstrations
Authors: Xiaoqin Zhang, Huimin Ma
Date: 2018-01-31 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Pretraining with expert demonstrations have been found useful in speeding up
the training process of deep reinforcement learning algorithms since less
online simulation data is required. Some people use supervised learning to
speed up the process of feature learning, others pretrain the policies by
imitating expert demonstrations. However, these methods are unstable and not
suitable for actor-critic reinforcement learning algorithms. Also, some
existing methods rely on the global optimum assumption, which is not true in
most scenarios. In this paper, we employ expert demonstrations in a
actor-critic reinforcement learning framework, and meanwhile ensure that the
performance is not affected by the fact that expert demonstrations are not
global optimal. We theoretically derive a method for computing policy gradients
and value estimators with only expert demonstrations. Our method is
theoretically plausible for actor-critic reinforcement learning algorithms that
pretrains both policy and value functions. We apply our method to two of the
typical actor-critic reinforcement learning algorithms, DDPG and ACER, and
demonstrate with experiments that our method not only outperforms the RL
algorithms without pretraining process, but also is more simulation efficient.
Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluation
Authors: Ali Can Kocabiyikoglu, Laurent Besacier, Olivier Kraif
Date: 2018-02-09 00:00:00
Proceeding: LREC 2018 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, Sentence, speech-recognition, Speech Recognition, Speech-to-Text, Speech-to-Text Translation, Translation

Recent works in spoken language translation (SLT) have attempted to build
end-to-end speech-to-text translation without using source language
transcription during learning or decoding. However, while large quantities of
parallel texts (such as Europarl, OpenSubtitles) are available for training
machine translation systems, there are no large (100h) and open source parallel
corpora that include speech in a source language aligned to text in a target
language. This paper tries to fill this gap by augmenting an existing
(monolingual) corpus: LibriSpeech. This corpus, used for automatic speech
recognition, is derived from read audiobooks from the LibriVox project, and has
been carefully segmented and aligned. After gathering French e-books
corresponding to the English audio-books from LibriSpeech, we align speech
segments at the sentence level with their respective translations and obtain
236h of usable parallel data. This paper presents the details of the processing
as well as a manual evaluation conducted on a small subset of the corpus. This
evaluation shows that the automatic alignments scores are reasonably correlated
with the human judgments of the bilingual alignment quality. We believe that
this corpus (which is made available online) is useful for replicable
experiments in direct speech translation or more general spoken language
translation experiments.
Brain EEG Time Series Selection: A Novel Graph-Based Approach for Classification
Authors: Chenglong Dai, Jia Wu, Dechang Pi, Lin Cui
Date: 2018-01-14 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification, Time Series, Time Series Analysis

Brain Electroencephalography (EEG) classification is widely applied to
analyze cerebral diseases in recent years. Unfortunately, invalid/noisy EEGs
degrade the diagnosis performance and most previously developed methods ignore
the necessity of EEG selection for classification. To this end, this paper
proposes a novel maximum weight clique-based EEG selection approach, named
mwcEEGs, to map EEG selection to searching maximum similarity-weighted cliques
from an improved Fr\'{e}chet distance-weighted undirected EEG graph
simultaneously considering edge weights and vertex weights. Our mwcEEGs
improves the classification performance by selecting intra-clique pairwise
similar and inter-clique discriminative EEGs with similarity threshold
$\delta$. Experimental results demonstrate the algorithm effectiveness compared
with the state-of-the-art time series selection algorithms on real-world EEG
datasets.
Boosted nonparametric hazards with time-dependent covariates
Authors: Donald K. K. Lee, Ningyuan Chen, Hemant Ishwaran
Date: 2017-01-27 00:00:00

Given functional data from a survival process with time-dependent covariates, we derive a smooth convex representation for its nonparametric log-likelihood functional and obtain its functional gradient. From this, we devise a generic gradient boosting procedure for estimating the hazard function nonparametrically. An illustrative implementation of the procedure using regression trees is described to show how to recover the unknown hazard. The generic estimator is consistent if the model is correctly specified; alternatively, an oracle inequality can be demonstrated for tree-based models. To avoid overfitting, boosting employs several regularization devices. One of them is step-size restriction, but the rationale for this is somewhat mysterious from the viewpoint of consistency. Our work brings some clarity to this issue by revealing that step-size restriction is a mechanism for preventing the curvature of the risk from derailing convergence.
A Good Practice Towards Top Performance of Face Recognition: Transferred Deep Feature Fusion
Authors: Lin Xiong, Jayashree Karlekar, Jian Zhao, Yi Cheng, Yan Xu, Jiashi Feng, Sugiri Pranata, ShengMei Shen
Date: 2017-04-03 00:00:00
Tasks: Face Recognition, Transfer Learning

Unconstrained face recognition performance evaluations have traditionally
focused on Labeled Faces in the Wild (LFW) dataset for imagery and the
YouTubeFaces (YTF) dataset for videos in the last couple of years. Spectacular
progress in this field has resulted in saturation on verification and
identification accuracies for those benchmark datasets. In this paper, we
propose a unified learning framework named Transferred Deep Feature Fusion
(TDFF) targeting at the new IARPA Janus Benchmark A (IJB-A) face recognition
dataset released by NIST face challenge. The IJB-A dataset includes real-world
unconstrained faces from 500 subjects with full pose and illumination
variations which are much harder than the LFW and YTF datasets. Inspired by
transfer learning, we train two advanced deep convolutional neural networks
(DCNN) with two different large datasets in source domain, respectively. By
exploring the complementarity of two distinct DCNNs, deep feature fusion is
utilized after feature extraction in target domain. Then, template specific
linear SVMs is adopted to enhance the discrimination of framework. Finally,
multiple matching scores corresponding different templates are merged as the
final results. This simple unified framework exhibits excellent performance on
IJB-A dataset. Based on the proposed approach, we have submitted our IJB-A
results to National Institute of Standards and Technology (NIST) for official
evaluation. Moreover, by introducing new data and advanced neural architecture,
our method outperforms the state-of-the-art by a wide margin on IJB-A dataset.
Robust and Sparse Regression in GLM by Stochastic Optimization
Authors: Takayuki Kawashima, Hironori Fujisawa
Date: 2018-02-09 00:00:00
Tasks: regression, Stochastic Optimization

The generalized linear model (GLM) plays a key role in regression analyses.
In high-dimensional data, the sparse GLM has been used but it is not robust
against outliers. Recently, the robust methods have been proposed for the
specific example of the sparse GLM. Among them, we focus on the robust and
sparse linear regression based on the $\gamma$-divergence. The estimator of the
$\gamma$-divergence has strong robustness under heavy contamination. In this
paper, we extend the robust and sparse linear regression based on the
$\gamma$-divergence to the robust and sparse GLM based on the
$\gamma$-divergence with a stochastic optimization approach in order to obtain
the estimate. We adopt the randomized stochastic projected gradient descent as
a stochastic optimization approach and extend the established convergence
property to the classical first-order necessary condition. By virtue of the
stochastic optimization approach, we can efficiently estimate parameters for
very large problems. Particularly, we show the linear regression, logistic
regression and Poisson regression with $L_1$ regularization in detail as
specific examples of robust and sparse GLM. In numerical experiments and real
data analysis, the proposed method outperformed comparative methods.
Zero-Resource Neural Machine Translation with Multi-Agent Communication Game
Authors: Yun Chen, Yang Liu, Victor O. K. Li
Date: 2018-02-09 00:00:00
Tasks: Decoder, Image Captioning, Image Description, Machine Translation, NMT, Translation

While end-to-end neural machine translation (NMT) has achieved notable
success in the past years in translating a handful of resource-rich language
pairs, it still suffers from the data scarcity problem for low-resource
language pairs and domains. To tackle this problem, we propose an interactive
multimodal framework for zero-resource neural machine translation. Instead of
being passively exposed to large amounts of parallel corpora, our learners
(implemented as encoder-decoder architecture) engage in cooperative image
description games, and thus develop their own image captioning or neural
machine translation model from the need to communicate in order to succeed at
the game. Experimental results on the IAPR-TC12 and Multi30K datasets show that
the proposed learning mechanism significantly improves over the
state-of-the-art methods.
Exact upper and lower bounds on the misclassification probability
Authors: Iosif Pinelis
Date: 2017-12-03 00:00:00

Exact lower and upper bounds on the best possible misclassification
probability for a finite number of classes are obtained in terms of the total
variation norms of the differences between the sub-distributions over the
classes. These bounds are compared with the exact bounds in terms of the
conditional entropy obtained by Feder and Merhav.
Convolutional Hashing for Automated Scene Matching
Authors: Martin Loncaric, Bowei Liu, Ryan Weber
Date: 2018-02-09 00:00:00
Tasks: Information Retrieval, Retrieval

We present a powerful new loss function and training scheme for learning
binary hash functions. In particular, we demonstrate our method by creating for
the first time a neural network that outperforms state-of-the-art Haar wavelets
and color layout descriptors at the task of automated scene matching. By
accurately relating distance on the manifold of network outputs to distance in
Hamming space, we achieve a 100-fold reduction in nontrivial false positive
rate and significantly higher true positive rate. We expect our insights to
provide large wins for hashing models applied to other information retrieval
hashing tasks as well.
Automatic segmenting teeth in X-ray images: Trends, a novel data set, benchmarking and future perspectives
Authors: Gil Jader, Luciano Oliveira, Matheus Pithon
Date: 2018-02-09 00:00:00
Tasks: Benchmarking, Image Segmentation, Segmentation, Semantic Segmentation

This review presents an in-depth study of the literature on segmentation
methods applied in dental imaging. Ten segmentation methods were studied and
categorized according to the type of the segmentation method (region-based,
threshold-based, cluster-based, boundary-based or watershed-based), type of
X-ray images used (intra-oral or extra-oral) and characteristics of the dataset
used to evaluate the methods in the state-of-the-art works. We found that the
literature has primarily focused on threshold-based segmentation methods (54%).
80% of the reviewed papers have used intra-oral X-ray images in their
experiments, demonstrating preference to perform segmentation on images of
already isolated parts of the teeth, rather than using extra-oral X-rays, which
show tooth structure of the mouth and bones of the face. To fill a scientific
gap in the field, a novel data set based on extra-oral X-ray images are
proposed here. A statistical comparison of the results found with the 10 image
segmentation methods over our proposed data set comprised of 1,500 images is
also carried out, providing a more comprehensive source of performance
assessment. Discussion on limitations of the methods conceived over the past
year as well as future perspectives on exploiting learning-based segmentation
methods to improve performance are also provided.
Deep Learning with Apache SystemML
Authors: Niketan Pansare, Michael Dusenberry, Nakul Jindal, Matthias Boehm, Berthold Reinwald, Prithviraj Sen
Date: 2018-02-08 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

Enterprises operate large data lakes using Hadoop and Spark frameworks that
(1) run a plethora of tools to automate powerful data
preparation/transformation pipelines, (2) run on shared, large clusters to (3)
perform many different analytics tasks ranging from model preparation,
building, evaluation, and tuning for both machine learning and deep learning.
Developing machine/deep learning models on data in such shared environments is
challenging. Apache SystemML provides a unified framework for implementing
machine learning and deep learning algorithms in a variety of shared deployment
scenarios. SystemML's novel compilation approach automatically generates
runtime execution plans for machine/deep learning algorithms that are composed
of single-node and distributed runtime operations depending on data and cluster
characteristics such as data size, data sparsity, cluster size, and memory
configurations, while still exploiting the capabilities of the underlying big
data frameworks.
Hole Filling with Multiple Reference Views in DIBR View Synthesis
Authors: Shuai Li, Ce Zhu, Ming-Ting Sun
Date: 2018-02-08 00:00:00

Depth-image-based rendering (DIBR) oriented view synthesis has been widely
employed in the current depth-based 3D video systems by synthesizing a virtual
view from an arbitrary viewpoint. However, holes may appear in the synthesized
view due to disocclusion, thus significantly degrading the quality.
Consequently, efforts have been made on developing effective and efficient hole
filling algorithms. Current hole filling techniques generally
extrapolate/interpolate the hole regions with the neighboring information based
on an assumption that the texture pattern in the holes is similar to that of
the neighboring background information. However, in many scenarios especially
of complex texture, the assumption may not hold. In other words, hole filling
techniques can only provide an estimation for a hole which may not be good
enough or may even be erroneous considering a wide variety of complex scene of
images. In this paper, we first examine the view interpolation with multiple
reference views, demonstrating that the problem of emerging holes in a target
virtual view can be greatly alleviated by making good use of other neighboring
complementary views in addition to its two (commonly used) most neighboring
primary views. The effects of using multiple views for view extrapolation in
reducing holes are also investigated in this paper. In view of the 3D Video and
ongoing free-viewpoint TV standardization, we propose a new view synthesis
framework which employs multiple views to synthesize output virtual views.
Furthermore, a scheme of selective warping of complementary views is developed
by efficiently locating a small number of useful pixels in the complementary
views for hole reduction, to avoid a full warping of additional complementary
views thus lowering greatly the warping complexity.
GAR: An efficient and scalable Graph-based Activity Regularization for semi-supervised learning
Authors: Ozsel Kilinc, Ismail Uysal
Date: 2017-05-19 00:00:00

In this paper, we propose a novel graph-based approach for semi-supervised learning problems, which considers an adaptive adjacency of the examples throughout the unsupervised portion of the training. Adjacency of the examples is inferred using the predictions of a neural network model which is first initialized by a supervised pretraining. These predictions are then updated according to a novel unsupervised objective which regularizes another adjacency, now linking the output nodes. Regularizing the adjacency of the output nodes, inferred from the predictions of the network, creates an easier optimization problem and ultimately provides that the predictions of the network turn into the optimal embedding. Ultimately, the proposed framework provides an effective and scalable graph-based solution which is natural to the operational mechanism of deep neural networks. Our results show comparable performance with state-of-the-art generative approaches for semi-supervised learning on an easier-to-train, low-cost framework.
Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization
Authors: Ozsel Kilinc, Ismail Uysal
Date: 2018-02-08 00:00:00
Proceeding: ICLR 2018 1
Tasks: Clustering, Unsupervised Image Classification

In this paper, we propose a novel unsupervised clustering approach exploiting
the hidden information that is indirectly introduced through a pseudo
classification objective. Specifically, we randomly assign a pseudo
parent-class label to each observation which is then modified by applying the
domain specific transformation associated with the assigned label. Generated
pseudo observation-label pairs are subsequently used to train a neural network
with Auto-clustering Output Layer (ACOL) that introduces multiple softmax nodes
for each pseudo parent-class. Due to the unsupervised objective based on
Graph-based Activity Regularization (GAR) terms, softmax duplicates of each
parent-class are specialized as the hidden information captured through the
help of domain specific transformations is propagated during training.
Ultimately we obtain a k-means friendly latent representation. Furthermore, we
demonstrate how the chosen transformation type impacts performance and helps
propagate the latent information that is useful in revealing unknown clusters.
Our results show state-of-the-art performance for unsupervised clustering tasks
on MNIST, SVHN and USPS datasets, with the highest accuracies reported to date
in the literature.
Stabilizing Adversarial Nets With Prediction Methods
Authors: Abhay Yadav, Sohil Shah, Zheng Xu, David Jacobs, Tom Goldstein
Date: 2017-05-20 00:00:00
Proceeding: ICLR 2018 1
Tasks: Prediction

Adversarial neural networks solve many important problems in data science,
but are notoriously difficult to train. These difficulties come from the fact
that optimal weights for adversarial nets correspond to saddle points, and not
minimizers, of the loss function. The alternating stochastic gradient methods
typically used for such problems do not reliably converge to saddle points, and
when convergence does happen it is often highly sensitive to learning rates. We
propose a simple modification of stochastic gradient descent that stabilizes
adversarial networks. We show, both in theory and practice, that the proposed
method reliably converges to saddle points, and is stable with a wider range of
training parameters than a non-prediction method. This makes adversarial
networks less likely to "collapse," and enables faster training with larger
learning rates.