pwc-archive/papers-with-abstracts huggingface dataset:
Coupling the reduced-order model and the generative model for an importance sampling estimator
Authors: Xiaoliang Wan, Shuangqing Wei
Date: 2019-01-23 00:00:00
Tasks: Dimensionality Reduction, model, Uncertainty Quantification

In this work, we develop an importance sampling estimator by coupling the
reduced-order model and the generative model in a problem setting of
uncertainty quantification. The target is to estimate the probability that the
quantity of interest (QoI) in a complex system is beyond a given threshold. To
avoid the prohibitive cost of sampling a large scale system, the reduced-order
model is usually considered for a trade-off between efficiency and accuracy.
However, the Monte Carlo estimator given by the reduced-order model is biased
due to the error from dimension reduction. To correct the bias, we still need
to sample the fine model. An effective technique to reduce the variance
reduction is importance sampling, where we employ the generative model to
estimate the distribution of the data from the reduced-order model and use it
for the change of measure in the importance sampling estimator. To compensate
the approximation errors of the reduced-order model, more data that induce a
slightly smaller QoI than the threshold need to be included into the training
set. Although the amount of these data can be controlled by a posterior error
estimate, redundant data, which may outnumber the effective data, will be kept
due to the epistemic uncertainty. To deal with this issue, we introduce a
weighted empirical distribution to process the data from the reduced-order
model. The generative model is then trained by minimizing the cross entropy
between it and the weighted empirical distribution. We also introduce a penalty
term into the objective function to deal with the overfitting for more
robustness. Numerical results are presented to demonstrate the effectiveness of
the proposed methodology.
DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images
Authors: Yuying Ge, Ruimao Zhang, Lingyun Wu, Xiaogang Wang, Xiaoou Tang, Ping Luo
Date: 2019-01-23 00:00:00
Proceeding: CVPR 2019 6
Tasks: Pose Estimation, Retrieval, Semantic Segmentation

Understanding fashion images has been advanced by benchmarks with rich
annotations such as DeepFashion, whose labels include clothing categories,
landmarks, and consumer-commercial image pairs. However, DeepFashion has
nonnegligible issues such as single clothing-item per image, sparse landmarks
(4~8 only), and no per-pixel masks, making it had significant gap from
real-world scenarios. We fill in the gap by presenting DeepFashion2 to address
these issues. It is a versatile benchmark of four tasks including clothes
detection, pose estimation, segmentation, and retrieval. It has 801K clothing
items where each item has rich annotations such as style, scale, viewpoint,
occlusion, bounding box, dense landmarks and masks. There are also 873K
Commercial-Consumer clothes pairs. A strong baseline is proposed, called Match
R-CNN, which builds upon Mask R-CNN to solve the above four tasks in an
end-to-end manner. Extensive evaluations are conducted with different
criterions in DeepFashion2.
CTCModel: a Keras Model for Connectionist Temporal Classification
Authors: Yann Soullard, Cyprien Ruffino, Thierry Paquet
Date: 2019-01-23 00:00:00
Tasks: Classification, General Classification, model

We report an extension of a Keras Model, called CTCModel, to perform the
Connectionist Temporal Classification (CTC) in a transparent way. Combined with
Recurrent Neural Networks, the Connectionist Temporal Classification is the
reference method for dealing with unsegmented input sequences, i.e. with data
that are a couple of observation and label sequences where each label is
related to a subset of observation frames. CTCModel makes use of the CTC
implementation in the Tensorflow backend for training and predicting sequences
of labels using Keras. It consists of three branches made of Keras models: one
for training, computing the CTC loss function; one for predicting, providing
sequences of labels; and one for evaluating that returns standard metrics for
analyzing sequences of predictions.
Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework
Authors: He Zhang, Xingrui Yu, Peng Ren, Chunbo Luo, Geyong Min
Date: 2019-01-23 00:00:00
Tasks: Data Augmentation, Intrusion Detection, Network Intrusion Detection

Intrusion detection systems (IDSs) play an important role in identifying
malicious attacks and threats in networking systems. As fundamental tools of
IDSs, learning based classification methods have been widely employed. When it
comes to detecting network intrusions in small sample sizes (e.g., emerging
intrusions), the limited number and imbalanced proportion of training samples
usually cause significant challenges in training supervised and semi-supervised
classifiers. In this paper, we propose a general network intrusion detection
framework to address the challenges of both \emph{data scarcity} and \emph{data
imbalance}. The novelty of the proposed framework focuses on incorporating deep
adversarial learning with statistical learning and exploiting learning based
data augmentation. Given a small set of network intrusion samples, it first
derives a Poisson-Gamma joint probabilistic generative model to generate
synthesised intrusion data using Monte Carlo methods. Those synthesised data
are then augmented by deep generative neural networks through adversarial
learning. Finally, it adopts the augmented intrusion data to train supervised
models for detecting network intrusions. Comprehensive experimental validations
on KDD Cup 99 dataset show that the proposed framework outperforms the existing
learning based IDSs in terms of improved accuracy, precision, recall, and
F1-score.
Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge
Authors: Ondřej Dušek, Jekaterina Novikova, Verena Rieser
Date: 2019-01-23 00:00:00
Tasks: Text Generation

This paper provides a comprehensive analysis of the first shared task on End-to-End Natural Language Generation (NLG) and identifies avenues for future research based on the results. This shared task aimed to assess whether recent end-to-end NLG systems can generate more complex output by learning from datasets containing higher lexical richness, syntactic complexity and diverse discourse phenomena. Introducing novel automatic and human metrics, we compare 62 systems submitted by 17 institutions, covering a wide range of approaches, including machine learning architectures -- with the majority implementing sequence-to-sequence models (seq2seq) -- as well as systems based on grammatical rules and templates. Seq2seq-based systems have demonstrated a great potential for NLG in the challenge. We find that seq2seq systems generally score high in terms of word-overlap metrics and human evaluations of naturalness -- with the winning SLUG system (Juraska et al., 2018) being seq2seq-based. However, vanilla seq2seq models often fail to correctly express a given meaning representation if they lack a strong semantic control mechanism applied during decoding. Moreover, seq2seq models can be outperformed by hand-engineered systems in terms of overall quality, as well as complexity, length and diversity of outputs. This research has influenced, inspired and motivated a number of recent studies outwith the original competition, which we also summarise as part of this paper.
U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans
Authors: José Ignacio Orlando, Philipp Seeböck, Hrvoje Bogunović, Sophie Klimscha, Christoph Grechenig, Sebastian Waldstein, Bianca S. Gerendas, Ursula Schmidt-Erfurth
Date: 2019-01-23 00:00:00
Tasks: Image Matting

In this paper, we introduce a Bayesian deep learning based model for segmenting the photoreceptor layer in pathological OCT scans. Our architecture provides accurate segmentations of the photoreceptor layer and produces pixel-wise epistemic uncertainty maps that highlight potential areas of pathologies or segmentation errors. We empirically evaluated this approach in two sets of pathological OCT scans of patients with age-related macular degeneration, retinal vein oclussion and diabetic macular edema, improving the performance of the baseline U-Net both in terms of the Dice index and the area under the precision/recall curve. We also observed that the uncertainty estimates were inversely correlated with the model performance, underlying its utility for highlighting areas where manual inspection/correction might be needed.
Interpolation and Denoising of Seismic Data using Convolutional Neural Networks
Authors: Sara Mandelli, Vincenzo Lipari, Paolo Bestagini, Stefano Tubaro
Date: 2019-01-23 00:00:00
Tasks: Denoising

Seismic data processing algorithms greatly benefit from regularly sampled and reliable data. Therefore, interpolation and denoising play a fundamental role as one of the starting steps of most seismic processing workflows. We exploit convolutional neural networks for the joint tasks of interpolation and random noise attenuation of 2D common shot gathers. Inspired by the great contributions achieved in image processing and computer vision, we investigate a particular architecture of convolutional neural network referred to as U-net, which implements a convolutional autoencoder able to describe the complex features of clean and regularly sampled data for reconstructing the corrupted ones. In training phase we exploit part of the data for tailoring the network to the specific tasks of interpolation, denoising and joint denoising/interpolation, while during the system deployment we are able to recover the remaining corrupted shot gathers in a computationally efficient procedure. We consider a plurality of data corruptions in our numerical experiments, including different noise models and different distributions of missing traces. Several examples on synthetic and field data illustrate the appealing features of the aforementioned strategy. Comparative examples show improvements with respect to recently proposed solutions for joint denoising and interpolation.
ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features
Authors: Xin Wu, Danfeng Hong, Jiaojiao Tian, Jocelyn Chanussot, Wei Li, Ran Tao
Date: 2019-01-23 00:00:00
Tasks: Novel Object Detection, object-detection, Object Detection

With the rapid development of spaceborne imaging techniques, object detection
in optical remote sensing imagery has drawn much attention in recent decades.
While many advanced works have been developed with powerful learning
algorithms, the incomplete feature representation still cannot meet the demand
for effectively and efficiently handling image deformations, particularly
objective scaling and rotation. To this end, we propose a novel object
detection framework, called optical remote sensing imagery detector (ORSIm
detector), integrating diverse channel features extraction, feature learning,
fast image pyramid matching, and boosting strategy. ORSIm detector adopts a
novel spatial-frequency channel feature (SFCF) by jointly considering the
rotation-invariant channel features constructed in frequency domain and the
original spatial channel features (e.g., color channel, gradient magnitude).
Subsequently, we refine SFCF using learning-based strategy in order to obtain
the high-level or semantically meaningful features. In the test phase, we
achieve a fast and coarsely-scaled channel computation by mathematically
estimating a scaling factor in the image domain. Extensive experimental results
conducted on the two different airborne datasets are performed to demonstrate
the superiority and effectiveness in comparison with previous state-of-the-art
methods.
Online Learning with Diverse User Preferences
Authors: Chao Gan, Jing Yang, Ruida Zhou, Cong Shen
Date: 2019-01-23 00:00:00

In this paper, we investigate the impact of diverse user preference on learning under the stochastic multi-armed bandit (MAB) framework. We aim to show that when the user preferences are sufficiently diverse and each arm can be optimal for certain users, the O(log T) regret incurred by exploring the sub-optimal arms under the standard stochastic MAB setting can be reduced to a constant. Our intuition is that to achieve sub-linear regret, the number of times an optimal arm being pulled should scale linearly in time; when all arms are optimal for certain users and pulled frequently, the estimated arm statistics can quickly converge to their true values, thus reducing the need of exploration dramatically. We cast the problem into a stochastic linear bandits model, where both the users preferences and the state of arms are modeled as {independent and identical distributed (i.i.d)} d-dimensional random vectors. After receiving the user preference vector at the beginning of each time slot, the learner pulls an arm and receives a reward as the linear product of the preference vector and the arm state vector. We also assume that the state of the pulled arm is revealed to the learner once its pulled. We propose a Weighted Upper Confidence Bound (W-UCB) algorithm and show that it can achieve a constant regret when the user preferences are sufficiently diverse. The performance of W-UCB under general setups is also completely characterized and validated with synthetic data.
Incremental Principal Component Analysis Exact implementation and continuity corrections
Authors: Vittorio Lippi, Giacomo Ceccarelli
Date: 2019-01-23 00:00:00

This paper describes some applications of an incremental implementation of the principal component analysis (PCA). The algorithm updates the transformation coefficients matrix on-line for each new sample, without the need to keep all the samples in memory. The algorithm is formally equivalent to the usual batch version, in the sense that given a sample set the transformation coefficients at the end of the process are the same. The implications of applying the PCA in real time are discussed with the help of data analysis examples. In particular we focus on the problem of the continuity of the PCs during an on-line analysis.
Predicting the Results of LTL Model Checking using Multiple Machine Learning Algorithms
Authors: Weijun Zhu, Mingliang Xu, Jianwei Wang
Date: 2019-01-23 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we study how to predict the results of LTL model checking
using some machine learning algorithms. Some Kripke structures and LTL formulas
and their model checking results are made up data set. The approaches based on
the Random Forest (RF), K-Nearest Neighbors (KNN), Decision tree (DT), and
Logistic Regression (LR) are used to training and prediction. The experiment
results show that the predictive accuracy of the RF, KNN, DT and LR-based
approaches are 97.9%, 98.2%, 97.1% and 98.2%, respectively, as well as the
average computation efficiencies of the RF, KNN, DT and LR-based approaches are
7102500, 598, 4132364 and 5543415 times than that of the existing approach,
respectively, if the length of each LTL formula is 500.
Phonetic-enriched Text Representation for Chinese Sentiment Analysis with Reinforcement Learning
Authors: Haiyun Peng, Yukun Ma, Soujanya Poria, Yang Li, Erik Cambria
Date: 2019-01-23 00:00:00
Tasks: Chinese Sentiment Analysis, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentiment Analysis

The Chinese pronunciation system offers two characteristics that distinguish
it from other languages: deep phonemic orthography and intonation variations.
We are the first to argue that these two important properties can play a major
role in Chinese sentiment analysis. Particularly, we propose two effective
features to encode phonetic information. Next, we develop a Disambiguate
Intonation for Sentiment Analysis (DISA) network using a reinforcement network.
It functions as disambiguating intonations for each Chinese character (pinyin).
Thus, a precise phonetic representation of Chinese is learned. Furthermore, we
also fuse phonetic features with textual and visual features in order to mimic
the way humans read and understand Chinese text. Experimental results on five
different Chinese sentiment analysis datasets show that the inclusion of
phonetic features significantly and consistently improves the performance of
textual and visual representations and outshines the state-of-the-art Chinese
character level representations.
"Is this an example image?" -- Predicting the Relative Abstractness Level of Image and Text
Authors: Christian Otto, Sebastian Holzki, Ralph Ewerth
Date: 2019-01-23 00:00:00
Tasks: Retrieval

Successful multimodal search and retrieval requires the automatic
understanding of semantic cross-modal relations, which, however, is still an
open research problem. Previous work has suggested the metrics cross-modal
mutual information and semantic correlation to model and predict cross-modal
semantic relations of image and text. In this paper, we present an approach to
predict the (cross-modal) relative abstractness level of a given image-text
pair, that is whether the image is an abstraction of the text or vice versa.
For this purpose, we introduce a new metric that captures this specific
relationship between image and text at the Abstractness Level (ABS). We present
a deep learning approach to predict this metric, which relies on an autoencoder
architecture that allows us to significantly reduce the required amount of
labeled training data. A comprehensive set of publicly available scientific
documents has been gathered. Experimental results on a challenging test set
demonstrate the feasibility of the approach.
A Review on Quantile Regression for Stochastic Computer Experiments
Authors: Léonard Torossian, Victor Picheny, Robert Faivre, Aurélien Garivier
Date: 2019-01-23 00:00:00
Tasks: Diversity, quantile regression, regression

We report on an empirical study of the main strategies for quantile regression in the context of stochastic computer experiments. To ensure adequate diversity, six metamodels are presented, divided into three categories based on order statistics, functional approaches, and those of Bayesian inspiration. The metamodels are tested on several problems characterized by the size of the training set, the input dimension, the signal-to-noise ratio and the value of the probability density function at the targeted quantile. The metamodels studied reveal good contrasts in our set of experiments, enabling several patterns to be extracted. Based on our results, guidelines are proposed to allow users to select the best method for a given problem.
Analysis of the $(μ/μ_I,λ)$-CSA-ES with Repair by Projection Applied to a Conically Constrained Problem
Authors: Patrick Spettel, Hans-Georg Beyer
Date: 2019-01-23 00:00:00

Theoretical analyses of evolution strategies are indispensable for gaining a deep understanding of their inner workings. For constrained problems, rather simple problems are of interest in the current research. This work presents a theoretical analysis of a multi-recombinative evolution strategy with cumulative step size adaptation applied to a conically constrained linear optimization problem. The state of the strategy is modeled by random variables and a stochastic iterative mapping is introduced. For the analytical treatment, fluctuations are neglected and the mean value iterative system is considered. Non-linear difference equations are derived based on one-generation progress rates. Based on that, expressions for the steady state of the mean value iterative system are derived. By comparison with real algorithm runs, it is shown that for the considered assumptions, the theoretical derivations are able to predict the dynamics and the steady state values of the real runs.
Constant Time Graph Neural Networks
Authors: Ryoma Sato, Makoto Yamada, Hisashi Kashima
Date: 2019-01-23 00:00:00
Tasks: Graph Attention, Question Answering, Recommendation Systems

The recent advancements in graph neural networks (GNNs) have led to state-of-the-art performances in various applications, including chemo-informatics, question-answering systems, and recommender systems. However, scaling up these methods to huge graphs, such as social networks and Web graphs, remains a challenge. In particular, the existing methods for accelerating GNNs either are not theoretically guaranteed in terms of the approximation error or incur at least a linear time computation cost. In this study, we reveal the query complexity of the uniform node sampling scheme for Message Passing Neural Networks, including GraphSAGE, graph attention networks (GATs), and graph convolutional networks (GCNs). Surprisingly, our analysis reveals that the complexity of the node sampling method is completely independent of the number of the nodes, edges, and neighbors of the input and depends only on the error tolerance and confidence probability while providing a theoretical guarantee for the approximation error. To the best of our knowledge, this is the first paper to provide a theoretical guarantee of approximation for GNNs within constant time. Through experiments with synthetic and real-world datasets, we investigated the speed and precision of the node sampling scheme and validated our theoretical results.
Context based Analysis of Lexical Semantics for Hindi Language
Authors: Mohd Zeeshan Ansari, Lubna Khan
Date: 2019-01-23 00:00:00
Tasks: Word Sense Disambiguation

A word having multiple senses in a text introduces the lexical semantic task
to find out which particular sense is appropriate for the given context. One
such task is Word sense disambiguation which refers to the identification of
the most appropriate meaning of the polysemous word in a given context using
computational algorithms. The language processing research in Hindi, the
official language of India, and other Indian languages is restricted by
unavailability of the standard corpus. For Hindi word sense disambiguation
also, the large corpus is not available. In this work, we prepared the text
containing new senses of certain words leading to the enrichment of the
sense-tagged Hindi corpus of sixty polysemous words. Furthermore, we analyzed
two novel lexical associations for Hindi word sense disambiguation based on the
contextual features of the polysemous word. The evaluation of these methods is
carried out over learning algorithms and favorable results are achieved.
Trust Region Value Optimization using Kalman Filtering
Authors: Shirli Di-Castro Shashua, Shie Mannor
Date: 2019-01-23 00:00:00
Tasks: Reinforcement Learning

Policy evaluation is a key process in reinforcement learning. It assesses a
given policy using estimation of the corresponding value function. When using a
parameterized function to approximate the value, it is common to optimize the
set of parameters by minimizing the sum of squared Bellman Temporal Differences
errors. However, this approach ignores certain distributional properties of
both the errors and value parameters. Taking these distributions into account
in the optimization process can provide useful information on the amount of
confidence in value estimation. In this work we propose to optimize the value
by minimizing a regularized objective function which forms a trust region over
its parameters. We present a novel optimization method, the Kalman Optimization
for Value Approximation (KOVA), based on the Extended Kalman Filter. KOVA
minimizes the regularized objective function by adopting a Bayesian perspective
over both the value parameters and noisy observed returns. This distributional
property provides information on parameter uncertainty in addition to value
estimates. We provide theoretical results of our approach and analyze the
performance of our proposed optimizer on domains with large state and action
spaces.