pwc-archive/papers-with-abstracts huggingface dataset:
A Possibility Distribution Based Multi-Criteria Decision Algorithm for Resilient Supplier Selection Problems
Authors: Dizuo Jiang, Md Mahmudul Hassan, Tasnim Ibn Faiz, Md. Noor-E-Alam
Date: 2018-06-04 00:00:00
Tasks: Decision Making

Thus far, limited research has been performed on resilient supplier selection
- a problem that requires simultaneous consideration of a set of numerical and
linguistic evaluation criteria, which are substantially different from
traditional supplier selection problem. Essentially, resilient supplier
selection entails key sourcing decision for an organization to gain competitive
advantage. In the presence of multiple conflicting evaluation criteria,
contradicting decision makers, and imprecise decision relevant information
(DRI), this problem becomes even more difficult to solve with the classical
optimization approaches. However, prior research focusing on MCDA based
supplier selection problem has been lacking in the ability to provide a
seamless integration of numerical and linguistic evaluation criteria along with
the consideration of multiple decision makers. To address these challenges, we
present a comprehensive decision-making framework for ranking a set of
suppliers from resiliency perspective. The proposed algorithm is capable of
leveraging imprecise and aggregated DRI obtained from crisp numerical
assessments and reliability adjusted linguistic appraisals from a group of
decision makers. We adapt two popular tools - Single Valued Neutrosophic Sets
(SVNS) and Interval-valued fuzzy sets (IVFS), and for the first time extend
them to incorporate both crisp and linguistic evaluations in a group decision
making platform to obtain aggregated SVNS and IVFS decision matrix. This
information is then used to rank the resilient suppliers by using TOPSIS
method. We present a case study to illustrate the mechanism of the proposed
algorithm.
An Information-Theoretic Analysis for Thompson Sampling with Many Actions
Authors: Shi Dong, Benjamin Van Roy
Date: 2018-05-30 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Thompson Sampling

Information-theoretic Bayesian regret bounds of Russo and Van Roy capture the dependence of regret on prior uncertainty. However, this dependence is through entropy, which can become arbitrarily large as the number of actions increases. We establish new bounds that depend instead on a notion of rate-distortion. Among other things, this allows us to recover through information-theoretic arguments a near-optimal bound for the linear bandit. We also offer a bound for the logistic bandit that dramatically improves on the best previously available, though this bound depends on an information-theoretic statistic that we have only been able to quantify via computation.
Recursive Optimization of Convex Risk Measures: Mean-Semideviation Models
Authors: Dionysios S. Kalogerias, Warren B. Powell
Date: 2018-04-02 00:00:00

We develop recursive, data-driven, stochastic subgradient methods for
optimizing a new, versatile, and application-driven class of convex risk
measures, termed here as mean-semideviations, strictly generalizing the
well-known and popular mean-upper-semideviation. We introduce the MESSAGEp
algorithm, which is an efficient compositional subgradient procedure for
iteratively solving convex mean-semideviation risk-averse problems to
optimality. We analyze the asymptotic behavior of the MESSAGEp algorithm under
a flexible and structure-exploiting set of problem assumptions. In particular:
1) Under appropriate stepsize rules, we establish pathwise convergence of the
MESSAGEp algorithm in a strong technical sense, confirming its asymptotic
consistency. 2) Assuming a strongly convex cost, we show that, for fixed
semideviation order $p>1$ and for $\epsilon\in\left[0,1\right)$, the MESSAGEp
algorithm achieves a squared-${\cal L}_{2}$ solution suboptimality rate of the
order of ${\cal O}(n^{-\left(1-\epsilon\right)/2})$ iterations, where, for
$\epsilon>0$, pathwise convergence is simultaneously guaranteed. This result
establishes a rate of order arbitrarily close to ${\cal O}(n^{-1/2})$, while
ensuring strongly stable pathwise operation. For $p\equiv1$, the rate order
improves to ${\cal O}(n^{-2/3})$, which also suffices for pathwise convergence,
and matches previous results. 3) Likewise, in the general case of a convex
cost, we show that, for any $\epsilon\in\left[0,1\right)$, the MESSAGEp
algorithm with iterate smoothing achieves an ${\cal L}_{1}$ objective
suboptimality rate of the order of ${\cal
O}(n^{-\left(1-\epsilon\right)/\left(4\bf{1}_{\left\{ p>1\right\} }+4\right)})$
iterations. This result provides maximal rates of ${\cal O}(n^{-1/4})$, if
$p\equiv1$, and ${\cal O}(n^{-1/8})$, if $p>1$, matching the state of the art,
as well.
Holographic Neural Architectures
Authors: Tariq Daouda, Jeremie Zumer, Claude Perreault, Sébastien Lemieux
Date: 2018-06-04 00:00:00
Tasks: Denoising, Representation Learning

Representation learning is at the heart of what makes deep learning
effective. In this work, we introduce a new framework for representation
learning that we call "Holographic Neural Architectures" (HNAs). In the same
way that an observer can experience the 3D structure of a holographed object by
looking at its hologram from several angles, HNAs derive Holographic
Representations from the training set. These representations can then be
explored by moving along a continuous bounded single dimension. We show that
HNAs can be used to make generative networks, state-of-the-art regression
models and that they are inherently highly resistant to noise. Finally, we
argue that because of their denoising abilities and their capacity to
generalize well from very few examples, models based upon HNAs are particularly
well suited for biological applications where training examples are rare or
noisy.
NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition
Authors: Fenfen Sheng, Zhineng Chen, Bo Xu
Date: 2018-06-04 00:00:00
Tasks: Decoder, Optical Character Recognition (OCR), Scene Text Recognition

Scene text recognition has attracted a great many researches due to its importance to various applications. Existing methods mainly adopt recurrence or convolution based networks. Though have obtained good performance, these methods still suffer from two limitations: slow training speed due to the internal recurrence of RNNs, and high complexity due to stacked convolutional layers for long-term feature extraction. This paper, for the first time, proposes a no-recurrence sequence-to-sequence text recognizer, named NRTR, that dispenses with recurrences and convolutions entirely. NRTR follows the encoder-decoder paradigm, where the encoder uses stacked self-attention to extract image features, and the decoder applies stacked self-attention to recognize texts based on encoder output. NRTR relies solely on self-attention mechanism thus could be trained with more parallelization and less complexity. Considering scene image has large variation in text and background, we further design a modality-transform block to effectively transform 2D input images to 1D sequences, combined with the encoder to extract more discriminative features. NRTR achieves state-of-the-art or highly competitive performance on both regular and irregular benchmarks, while requires only a small fraction of training time compared to the best model from the literature (at least 8 times faster).
Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing
Authors: Jian Zhao, Jianshu Li, Yu Cheng, Li Zhou, Terence Sim, Shuicheng Yan, Jiashi Feng
Date: 2018-04-10 00:00:00
Tasks: Autonomous Driving, Clustering, Generative Adversarial Network, Human Parsing, Instance Segmentation, Multi-Human Parsing, Person Re-Identification, Saliency Prediction, Semantic Segmentation

Despite the noticeable progress in perceptual tasks like detection, instance
segmentation and human parsing, computers still perform unsatisfactorily on
visually understanding humans in crowded scenes, such as group behavior
analysis, person re-identification and autonomous driving, etc. To this end,
models need to comprehensively perceive the semantic information and the
differences between instances in a multi-human image, which is recently defined
as the multi-human parsing task. In this paper, we present a new large-scale
database "Multi-Human Parsing (MHP)" for algorithm development and evaluation,
and advances the state-of-the-art in understanding humans in crowded scenes.
MHP contains 25,403 elaborately annotated images with 58 fine-grained semantic
category labels, involving 2-26 persons per image and captured in real-world
scenes from various viewpoints, poses, occlusion, interactions and background.
We further propose a novel deep Nested Adversarial Network (NAN) model for
multi-human parsing. NAN consists of three Generative Adversarial Network
(GAN)-like sub-nets, respectively performing semantic saliency prediction,
instance-agnostic parsing and instance-aware clustering. These sub-nets form a
nested structure and are carefully designed to learn jointly in an end-to-end
way. NAN consistently outperforms existing state-of-the-art solutions on our
MHP and several other datasets, and serves as a strong baseline to drive the
future research for multi-human parsing.
Automatic catheter detection in pediatric X-ray images using a scale-recurrent network and synthetic data
Authors: Xin Yi, Scott Adams, Paul Babyn, Abdul Elnajmi
Date: 2018-06-04 00:00:00
Tasks: Position

Catheters are commonly inserted life supporting devices. X-ray images are
used to assess the position of a catheter immediately after placement as
serious complications can arise from malpositioned catheters. Previous computer
vision approaches to detect catheters on X-ray images either relied on
low-level cues that are not sufficiently robust or only capable of processing a
limited number or type of catheters. With the resurgence of deep learning,
supervised training approaches are begining to showing promising results.
However, dense annotation maps are required, and the work of a human annotator
is hard to scale. In this work, we proposed a simple way of synthesizing
catheters on X-ray images and a scale recurrent network for catheter detection.
By training on adult chest X-rays, the proposed network exhibits promising
detection results on pediatric chest/abdomen X-rays in terms of both precision
and recall.
DRCD: a Chinese Machine Reading Comprehension Dataset
Authors: Chih Chieh Shao, Trois Liu, Yuting Lai, Yiying Tseng, Sam Tsai
Date: 2018-06-04 00:00:00
Tasks: Articles, Machine Reading Comprehension, Reading Comprehension, Transfer Learning

In this paper, we introduce DRCD (Delta Reading Comprehension Dataset), an open domain traditional Chinese machine reading comprehension (MRC) dataset. This dataset aimed to be a standard Chinese machine reading comprehension dataset, which can be a source dataset in transfer learning. The dataset contains 10,014 paragraphs from 2,108 Wikipedia articles and 30,000+ questions generated by annotators. We build a baseline model that achieves an F1 score of 89.59%. F1 score of Human performance is 93.30%.
Adversarial confidence and smoothness regularizations for scalable unsupervised discriminative learning
Authors: Yi-Qing Wang
Date: 2018-06-04 00:00:00
Tasks: Clustering

In this paper, we consider a generic probabilistic discriminative learner
from the functional viewpoint and argue that, to make it learn well, it is
necessary to constrain its hypothesis space to a set of non-trivial piecewise
constant functions. To achieve this goal, we present a scalable unsupervised
regularization framework. On the theoretical front, we prove that this
framework is conducive to a factually confident and smooth discriminative model
and connect it to an adversarial Taboo game, spectral clustering and virtual
adversarial training. Experimentally, we take deep neural networks as our
learners and demonstrate that, when trained under our framework in the
unsupervised setting, they not only achieve state-of-the-art clustering results
but also generalize well on both synthetic and real data.
How Much Are You Willing to Share? A "Poker-Styled" Selective Privacy Preserving Framework for Recommender Systems
Authors: Manoj Reddy Dareddy, Ariyam Das, Junghoo Cho, Carlo Zaniolo
Date: 2018-06-04 00:00:00
Tasks: Collaborative Filtering, Privacy Preserving, Recommendation Systems

Most industrial recommender systems rely on the popular collaborative
filtering (CF) technique for providing personalized recommendations to its
users. However, the very nature of CF is adversarial to the idea of user
privacy, because users need to share their preferences with others in order to
be grouped with like-minded people and receive accurate recommendations. While
previous privacy preserving approaches have been successful inasmuch as they
concealed user preference information to some extent from a centralized
recommender system, they have also, nevertheless, incurred significant
trade-offs in terms of privacy, scalability, and accuracy. They are also
vulnerable to privacy breaches by malicious actors. In light of these
observations, we propose a novel selective privacy preserving (SP2) paradigm
that allows users to custom define the scope and extent of their individual
privacies, by marking their personal ratings as either public (which can be
shared) or private (which are never shared and stored only on the user device).
Our SP2 framework works in two steps: (i) First, it builds an initial
recommendation model based on the sum of all public ratings that have been
shared by users and (ii) then, this public model is fine-tuned on each user's
device based on the user private ratings, thus eventually learning a more
accurate model. Furthermore, in this work, we introduce three different
algorithms for implementing an end-to-end SP2 framework that can scale
effectively from thousands to hundreds of millions of items. Our user survey
shows that an overwhelming fraction of users are likely to rate much more items
to improve the overall recommendations when they can control what ratings will
be publicly shared with others.
Self-Normalization Properties of Language Modeling
Authors: Jacob Goldberger, Oren Melamud
Date: 2018-06-04 00:00:00
Proceeding: COLING 2018 8
Tasks: Language Modeling, Language Modelling

Self-normalizing discriminative models approximate the normalized probability
of a class without having to compute the partition function. In the context of
language modeling, this property is particularly appealing as it may
significantly reduce run-times due to large word vocabularies. In this study,
we provide a comprehensive investigation of language modeling
self-normalization. First, we theoretically analyze the inherent
self-normalization properties of Noise Contrastive Estimation (NCE) language
models. Then, we compare them empirically to softmax-based approaches, which
are self-normalized using explicit regularization, and suggest a hybrid model
with compelling properties. Finally, we uncover a surprising negative
correlation between self-normalization and perplexity across the board, as well
as some regularity in the observed errors, which may potentially be used for
improving self-normalization algorithms in the future.
Bayesian Semantic Instance Segmentation in Open Set World
Authors: Trung Pham, Vijay Kumar B G, Thanh-Toan Do, Gustavo Carneiro, Ian Reid
Date: 2018-06-04 00:00:00
Proceeding: ECCV 2018 9
Tasks: Instance Segmentation, Object, Segmentation, Semantic Segmentation

This paper addresses the semantic instance segmentation task in the open-set
conditions, where input images can contain known and unknown object classes.
The training process of existing semantic instance segmentation methods
requires annotation masks for all object instances, which is expensive to
acquire or even infeasible in some realistic scenarios, where the number of
categories may increase boundlessly. In this paper, we present a novel open-set
semantic instance segmentation approach capable of segmenting all known and
unknown object classes in images, based on the output of an object detector
trained on known object classes. We formulate the problem using a Bayesian
framework, where the posterior distribution is approximated with a simulated
annealing optimization equipped with an efficient image partition sampler. We
show empirically that our method is competitive with state-of-the-art
supervised methods on known classes, but also performs well on unknown classes
when compared with unsupervised methods.
Effect of antipsychotics on community structure in functional brain networks
Authors: Ryan Flanagan, Lucas Lacasa, Emma K. Towlson, Sang Hoon Lee, Mason A. Porter
Date: 2018-05-31 00:00:00

Schizophrenia, a mental disorder that is characterized by abnormal social
behavior and failure to distinguish one's own thoughts and ideas from reality,
has been associated with structural abnormalities in the architecture of
functional brain networks. Using various methods from network analysis, we
examine the effect of two classical therapeutic antipsychotics --- Aripiprazole
and Sulpiride --- on the structure of functional brain networks of healthy
controls and patients who have been diagnosed with schizophrenia. We compare
the community structures of functional brain networks of different individuals
using mesoscopic response functions, which measure how community structure
changes across different scales of a network. We are able to do a reasonably
good job of distinguishing patients from controls, and we are most successful
at this task on people who have been treated with Aripiprazole. We demonstrate
that this increased separation between patients and controls is related only to
a change in the control group, as the functional brain networks of the patient
group appear to be predominantly unaffected by this drug. This suggests that
Aripiprazole has a significant and measurable effect on community structure in
healthy individuals but not in individuals who are diagnosed with
schizophrenia. In contrast, we find for individuals are given the drug
Sulpiride that it is more difficult to separate the networks of patients from
those of controls. Overall, we observe differences in the effects of the drugs
(and a placebo) on community structure in patients and controls and also that
this effect differs across groups. We thereby demonstrate that different types
of antipsychotic drugs selectively affect mesoscale structures of brain
networks, providing support that mesoscale structures such as communities are
meaningful functional units in the brain.
An unsupervised and customizable misspelling generator for mining noisy health-related text sources
Authors: Abeed Sarker, Graciela Gonzalez-Hernandez
Date: 2018-06-04 00:00:00
Tasks: Retrieval

In this paper, we present a customizable datacentric system that
automatically generates common misspellings for complex health-related terms.
The spelling variant generator relies on a dense vector model learned from
large unlabeled text, which is used to find semantically close terms to the
original/seed keyword, followed by the filtering of terms that are lexically
dissimilar beyond a given threshold. The process is executed recursively,
converging when no new terms similar (lexically and semantically) to the seed
keyword are found. Weighting of intra-word character sequence similarities
allows further problem-specific customization of the system. On a dataset
prepared for this study, our system outperforms the current state-of-the-art
for medication name variant generation with best F1-score of 0.69 and
F1/4-score of 0.78. Extrinsic evaluation of the system on a set of
cancer-related terms showed an increase of over 67% in retrieval rate from
Twitter posts when the generated variants are included. Our proposed spelling
variant generator has several advantages over the current state-of-the-art and
other types of variant generators-(i) it is capable of filtering out lexically
similar but semantically dissimilar terms, (ii) the number of variants
generated is low as many low-frequency and ambiguous misspellings are filtered
out, and (iii) the system is fully automatic, customizable and easily
executable. While the base system is fully unsupervised, we show how
supervision maybe employed to adjust weights for task-specific customization.
The performance and significant relative simplicity of our proposed approach
makes it a much needed misspelling generation resource for health-related text
mining from noisy sources. The source code for the system has been made
publicly available for research purposes.
Large-scale Land Cover Classification in GaoFen-2 Satellite Imagery
Authors: Xin-Yi Tong, Qikai Lu, Gui-Song Xia, Liangpei Zhang
Date: 2018-06-04 00:00:00
Tasks: Change Detection, Classification, General Classification, Land Cover Classification

Many significant applications need land cover information of remote sensing
images that are acquired from different areas and times, such as change
detection and disaster monitoring. However, it is difficult to find a generic
land cover classification scheme for different remote sensing images due to the
spectral shift caused by diverse acquisition condition. In this paper, we
develop a novel land cover classification method that can deal with large-scale
data captured from widely distributed areas and different times. Additionally,
we establish a large-scale land cover classification dataset consisting of 150
Gaofen-2 imageries as data support for model training and performance
evaluation. Our experiments achieve outstanding classification accuracy
compared with traditional methods.
Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced
Authors: Simon S. Du, Wei Hu, Jason D. Lee
Date: 2018-06-04 00:00:00
Proceeding: NeurIPS 2018 12

We study the implicit regularization imposed by gradient descent for learning
multi-layer homogeneous functions including feed-forward fully connected and
convolutional deep neural networks with linear, ReLU or Leaky ReLU activation.
We rigorously prove that gradient flow (i.e. gradient descent with
infinitesimal step size) effectively enforces the differences between squared
norms across different layers to remain invariant without any explicit
regularization. This result implies that if the weights are initially small,
gradient flow automatically balances the magnitudes of all layers. Using a
discretization argument, we analyze gradient descent with positive step size
for the non-convex low-rank asymmetric matrix factorization problem without any
regularization. Inspired by our findings for gradient flow, we prove that
gradient descent with step sizes $\eta_t = O\left(t^{-\left(
\frac12+\delta\right)} \right)$ ($0<\delta\le\frac12$) automatically balances
two low-rank factors and converges to a bounded global optimum. Furthermore,
for rank-$1$ asymmetric matrix factorization we give a finer analysis showing
gradient descent with constant step size converges to the global minimum at a
globally linear rate. We believe that the idea of examining the invariance
imposed by first order algorithms in learning homogeneous models could serve as
a fundamental building block for studying optimization for learning deep
models.