pwc-archive/papers-with-abstracts huggingface dataset:
Fourier analysis perspective for sufficient dimension reduction problem
Authors: Rustem Takhanov
Date: 2018-08-19 00:00:00
Tasks: Dimensionality Reduction

A theory of sufficient dimension reduction (SDR) is developed from an
optimizational perspective. In our formulation of the problem, instead of
dealing with raw data, we assume that our ground truth includes a mapping
${\mathbf f}: {\mathbb R}^n\rightarrow {\mathbb R}^m$ and a probability
distribution function $p$ over ${\mathbb R}^n$, both given analytically. We
formulate SDR as a problem of finding a function ${\mathbf g}: {\mathbb
R}^k\rightarrow {\mathbb R}^m$ and a matrix $P\in {\mathbb R}^{k\times n}$ such
that ${\mathbb E}_{{\mathbf x}\sim p({\mathbf x})} \left|{\mathbf f}({\mathbf
x}) - {\mathbf g}(P{\mathbf x})\right|^2$ is minimal. It turns out that the
latter problem allows a reformulation in the dual space, i.e. instead of
searching for ${\mathbf g}(P{\mathbf x})$ we suggest searching for its Fourier
transform. First, we characterize all tempered distributions that can serve as
the Fourier transform of such functions. The reformulation in the dual space
can be interpreted as a problem of finding a $k$-dimensional linear subspace
$S$ and a tempered distribution ${\mathbf t}$ supported in $S$ such that
${\mathbf t}$ is "close" in a certain sense to the Fourier transform of
${\mathbf f}$.
  Instead of optimizing over generalized functions with a $k$-dimensional
support, we suggest minimizing over ordinary functions but with an additional
term $R$ that penalizes a strong distortion of the support from any
$k$-dimensional linear subspace. For a specific case of $R$, we develop an
algorithm that can be formulated for functions given in the initial form as
well as for their Fourier transforms. Eventually, we report results of
numerical experiments with a discretized version of the latter algorithm.
Deep Multiple Instance Learning for Airplane Detection in High Resolution Imagery
Authors: Mohammad Reza Mohammadi
Date: 2018-08-19 00:00:00
Tasks: Multiple Instance Learning, Vocal Bursts Intensity Prediction

Automatic airplane detection in aerial imagery has a variety of applications. Two of the significant challenges in this task are variations in the scale and direction of the airplanes. To solve these challenges, we present a rotation-and-scale invariant airplane proposal generator. We call this generator symmetric line segments (SLS) that is developed based on the symmetric and regular boundaries of airplanes from the top view. Then, the generated proposals are used to train a deep convolutional neural network for removing non-airplane proposals. Since each airplane can have multiple SLS proposals, where some of them are not in the direction of the fuselage, we collect all proposals corresponding to one ground-truth as a positive bag and the others as the negative instances. To have multiple instance deep learning, we modify the loss function of the network to learn from each positive bag at least one instance as well as all negative instances. Finally, we employ non-maximum suppression to remove duplicate detections. Our experiments on NWPU VHR-10 and DOTA datasets show that our method is a promising approach for automatic airplane detection in very high-resolution images. Moreover, we estimate the direction of the airplanes using box-level annotations as an extra achievement.
Source-Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language
Authors: He Bai, Yu Zhou, Jiajun Zhang, Liang Zhao, Mei-Yuh Hwang, Cheng-qing Zong
Date: 2018-08-19 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, domain classification, General Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), slot-filling, Slot Filling, Spoken Language Understanding

To deploy a spoken language understanding (SLU) model to a new language,
language transferring is desired to avoid the trouble of acquiring and labeling
a new big SLU corpus. Translating the original SLU corpus into the target
language is an attractive strategy. However, SLU corpora consist of plenty of
semantic labels (slots), which general-purpose translators cannot handle well,
not to mention additional culture differences. This paper focuses on the
language transferring task given a tiny in-domain parallel SLU corpus. The
in-domain parallel corpus can be used as the first adaptation on the general
translator. But more importantly, we show how to use reinforcement learning
(RL) to further finetune the adapted translator, where translated sentences
with more proper slot tags receive higher rewards. We evaluate our approach on
Chinese to English language transferring for SLU systems. The experimental
results show that the generated English SLU corpus via adaptation and
reinforcement learning gives us over 97% in the slot F1 score and over 84%
accuracy in domain classification. It demonstrates the effectiveness of the
proposed language transferring method. Compared with naive translation, our
proposed method improves domain classification accuracy by relatively 22%, and
the slot filling F1 score by relatively more than 71%.
Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts
Authors: Di Jin, Peter Szolovits
Date: 2018-08-19 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Benchmarking, Classification, General Classification, Sentence, Sentence Classification, Structured Prediction

Prevalent models based on artificial neural network (ANN) for sentence
classification often classify sentences in isolation without considering the
context in which sentences appear. This hampers the traditional sentence
classification approaches to the problem of sequential sentence classification,
where structured prediction is needed for better overall classification
performance. In this work, we present a hierarchical sequential labeling
network to make use of the contextual information within surrounding sentences
to help classify the current sentence. Our model outperforms the
state-of-the-art results by 2%-3% on two benchmarking datasets for sequential
sentence classification in medical scientific abstracts.
On Design of Problem Token Questions in Quality of Experience Surveys
Authors: Jayant Gupchup, Ebrahim Beyrami, Martin Ellis, Yasaman Hosseinkashi, Sam Johnson, Ross Cutler
Date: 2018-08-19 00:00:00

User surveys for Quality of Experience (QoE) are a critical source of
information. In addition to the common "star rating" used to estimate Mean
Opinion Score (MOS), more detailed survey questions (problem tokens) about
specific areas provide valuable insight into the factors impacting QoE. This
paper explores two aspects of the problem token questionnaire design. First, we
study the bias introduced by fixed question order, and second, we study the
challenge of selecting a subset of questions to keep the token set small. Based
on 900,000 calls gathered using a randomized controlled experiment from a live
system, we find that the order bias can be significantly reduced by randomizing
the display order of tokens. The difference in response rate varies based on
token position and display design. It is worth noting that the users respond to
the randomized-order variant at levels that are comparable to the fixed-order
variant. The effective selection of a subset of token questions is achieved by
extracting tokens that provide the highest information gain over user ratings.
This selection is known to be in the class of NP-hard problems. We apply a
well-known greedy submodular maximization method on our dataset to capture 94%
of the information using just 30% of the questions.
In Defense of Single-column Networks for Crowd Counting
Authors: Ze Wang, Zehao Xiao, Kai Xie, Qiang Qiu, Xian-Tong Zhen, Xian-Bin Cao
Date: 2018-08-18 00:00:00
Tasks: Crowd Counting, Data Augmentation, Density Estimation

Crowd counting usually addressed by density estimation becomes an
increasingly important topic in computer vision due to its widespread
applications in video surveillance, urban planning, and intelligence gathering.
However, it is essentially a challenging task because of the greatly varied
sizes of objects, coupled with severe occlusions and vague appearance of
extremely small individuals. Existing methods heavily rely on multi-column
learning architectures to extract multi-scale features, which however suffer
from heavy computational cost, especially undesired for crowd counting. In this
paper, we propose the single-column counting network (SCNet) for efficient
crowd counting without relying on multi-column networks. SCNet consists of
residual fusion modules (RFMs) for multi-scale feature extraction, a pyramid
pooling module (PPM) for information fusion, and a sub-pixel convolutional
module (SPCM) followed by a bilinear upsampling layer for resolution recovery.
Those proposed modules enable our SCNet to fully capture multi-scale features
in a compact single-column architecture and estimate high-resolution density
map in an efficient way. In addition, we provide a principled paradigm for
density map generation and data augmentation for training, which shows further
improved performance. Extensive experiments on three benchmark datasets show
that our SCNet delivers new state-of-the-art performance and surpasses previous
methods by large margins, which demonstrates the great effectiveness of SCNet
as a single-column network for crowd counting.
Effect of secular trend in drug effectiveness study in real world data
Authors: Sharon Hensley Alford, Piyush Madan, Shilpa Mahatma, Italo Buleje, Yanyan Han, Fang Lu
Date: 2018-08-18 00:00:00
Tasks: Attribute, Causal Inference

We discovered secular trend bias in a drug effectiveness study for a recently
approved drug. We compared treatment outcomes between patients who received the
newly approved drug and patients exposed to the standard treatment. All
patients diagnosed after the new drug's approval date were considered. We built
a machine learning causal inference model to determine patient subpopulations
likely to respond better to the newly approved drug. After identifying the
presence of secular trend bias in our data, we attempted to adjust for the bias
in two different ways. First, we matched patients on the number of days from
the new drug's approval date that the patient's treatment (new or standard)
began. Second, we included a covariate in the model for the number of days
between the date of approval of the new drug and the treatment (new or
standard) start date. Neither approach completely mitigated the bias. Residual
bias we attribute to differences in patient disease severity or other
unmeasured patient characteristics. Had we not identified the secular trend
bias in our data, the causal inference model would have been interpreted
without consideration for this underlying bias. Being aware of, testing for,
and handling potential bias in the data is essential to diminish the
uncertainty in AI modeling.
A Recipe for Arabic-English Neural Machine Translation
Authors: Abdullah Alrajeh
Date: 2018-08-18 00:00:00
Tasks: Machine Translation, Translation

In this paper, we present a recipe for building a good Arabic-English neural
machine translation. We compare neural systems with traditional phrase-based
systems using various parallel corpora including UN, ISI and Ummah. We also
investigate the importance of special preprocessing of the Arabic script. The
presented results are based on test sets from NIST MT 2005 and 2012. The best
neural system produces a gain of +13 BLEU points compared to an equivalent
simple phrase-based system in NIST MT12 test set. Unexpectedly, we find that
tuning a model trained on the whole data using a small high quality corpus like
Ummah gives a substantial improvement (+3 BLEU points). We also find that
training a neural system with a small Arabic-English corpus is competitive to a
traditional phrase-based system.
Emoji Sentiment Scores of Writers using Odds Ratio and Fisher Exact Test
Authors: Jose Berengueres
Date: 2018-08-18 00:00:00
Tasks: Sentiment Analysis

The sentiment of a given emoji is traditionally calculated by averaging the
ratings {-1, 0 or +1} given by various users to a given context where the emoji
appears. However, using such formula complicates the statistical significance
analysis particularly for low sample sizes. Here, we provide sentiment scores
using odds and a sentiment mapping to a 4-icon scale. We show how odds ratio
statistics leads to simpler sentiment analysis. Finally, we provide a list of
sentiment scores with the often-missing exact p-values and CI for the most
common emoji.
Exact Passive-Aggressive Algorithms for Learning to Rank Using Interval Labels
Authors: Naresh Manwani, Mohit Chandra
Date: 2018-08-18 00:00:00
Tasks: Learning-To-Rank

In this paper, we propose exact passive-aggressive (PA) online algorithms for
learning to rank. The proposed algorithms can be used even when we have
interval labels instead of actual labels for examples. The proposed algorithms
solve a convex optimization problem at every trial. We find exact solution to
those optimization problems to determine the updated parameters. We propose
support class algorithm (SCA) which finds the active constraints using the KKT
conditions of the optimization problems. These active constrains form support
set which determines the set of thresholds that need to be updated. We derive
update rules for PA, PA-I and PA-II. We show that the proposed algorithms
maintain the ordering of the thresholds after every trial. We provide the
mistake bounds of the proposed algorithms in both ideal and general settings.
We also show experimentally that the proposed algorithms successfully learn
accurate classifiers using interval labels as well as exact labels. Proposed
algorithms also do well compared to other approaches.
Tangent-Normal Adversarial Regularization for Semi-supervised Learning
Authors: Bing Yu, Jingfeng Wu, Jinwen Ma, Zhanxing Zhu
Date: 2018-08-18 00:00:00
Proceeding: CVPR 2019 6
Tasks: TAR

Compared with standard supervised learning, the key difficulty in
semi-supervised learning is how to make full use of the unlabeled data. A
recently proposed method, virtual adversarial training (VAT), smartly performs
adversarial training without label information to impose a local smoothness on
the classifier, which is especially beneficial to semi-supervised learning. In
this work, we propose tangent-normal adversarial regularization (TNAR) as an
extension of VAT by taking the data manifold into consideration. The proposed
TNAR is composed by two complementary parts, the tangent adversarial
regularization (TAR) and the normal adversarial regularization (NAR). In TAR,
VAT is applied along the tangent space of the data manifold, aiming to enforce
local invariance of the classifier on the manifold, while in NAR, VAT is
performed on the normal space orthogonal to the tangent space, intending to
impose robustness on the classifier against the noise causing the observed data
deviating from the underlying data manifold. Demonstrated by experiments on
both artificial and practical datasets, our proposed TAR and NAR complement
with each other, and jointly outperforms other state-of-the-art methods for
semi-supervised learning.
Community detection in networks without observing edges
Authors: Till Hoffmann, Leto Peel, Renaud Lambiotte, Nick S. Jones
Date: 2018-08-18 00:00:00
Tasks: Community Detection

We develop a Bayesian hierarchical model to identify communities in networks for which we do not observe the edges directly, but instead observe a series of interdependent signals for each of the nodes. Fitting the model provides an end-to-end community detection algorithm that does not extract information as a sequence of point estimates but propagates uncertainties from the raw data to the community labels. Our approach naturally supports multiscale community detection as well as the selection of an optimal scale using model comparison. We study the properties of the algorithm using synthetic data and apply it to daily returns of constituents of the S&P100 index as well as climate data from US cities.
Learning to Compose over Tree Structures via POS Tags
Authors: Gehui Shen, Zhi-Hong Deng, Ting Huang, Xi Chen
Date: 2018-08-18 00:00:00
Tasks: POS, Semantic Composition, Sentence, Sentence Classification, TAG

Recursive Neural Network (RecNN), a type of models which compose words or
phrases recursively over syntactic tree structures, has been proven to have
superior ability to obtain sentence representation for a variety of NLP tasks.
However, RecNN is born with a thorny problem that a shared compositional
function for each node of trees can't capture the complex semantic
compositionality so that the expressive power of model is limited. In this
paper, in order to address this problem, we propose Tag-Guided
HyperRecNN/TreeLSTM (TG-HRecNN/TreeLSTM), which introduces hypernetwork into
RecNNs to take as inputs Part-of-Speech (POS) tags of word/phrase and generate
the semantic composition parameters dynamically. Experimental results on five
datasets for two typical NLP tasks show proposed models both obtain significant
improvement compared with RecNN and TreeLSTM consistently. Our TG-HTreeLSTM
outperforms all existing RecNN-based models and achieves or is competitive with
state-of-the-art on four sentence classification benchmarks. The effectiveness
of our models is also demonstrated by qualitative analysis.
SeVeN: Augmenting Word Embeddings with Unsupervised Relation Vectors
Authors: Luis Espinosa-Anke, Steven Schockaert
Date: 2018-08-18 00:00:00
Proceeding: COLING 2018 8
Tasks: Relation, Text Categorization, Word Embeddings, Word Similarity

We present SeVeN (Semantic Vector Networks), a hybrid resource that encodes
relationships between words in the form of a graph. Different from traditional
semantic networks, these relations are represented as vectors in a continuous
vector space. We propose a simple pipeline for learning such relation vectors,
which is based on word vector averaging in combination with an ad hoc
autoencoder. We show that by explicitly encoding relational information in a
dedicated vector space we can capture aspects of word meaning that are
complementary to what is captured by word embeddings. For example, by examining
clusters of relation vectors, we observe that relational similarities can be
identified at a more abstract level than with traditional word vector
differences. Finally, we test the effectiveness of semantic vector networks in
two tasks: measuring word similarity and neural text categorization. SeVeN is
available at bitbucket.org/luisespinosa/seven.
Distractor-aware Siamese Networks for Visual Object Tracking
Authors: Zheng Zhu, Qiang Wang, Bo Li, Wei Wu, Junjie Yan, Weiming Hu
Date: 2018-08-18 00:00:00
Proceeding: ECCV 2018 9
Tasks: Incremental Learning, Object, Object Tracking, Video Object Tracking, Visual Object Tracking, Visual Tracking

Recently, Siamese networks have drawn great attention in visual tracking
community because of their balanced accuracy and speed. However, features used
in most Siamese tracking approaches can only discriminate foreground from the
non-semantic backgrounds. The semantic backgrounds are always considered as
distractors, which hinders the robustness of Siamese trackers. In this paper,
we focus on learning distractor-aware Siamese networks for accurate and
long-term tracking. To this end, features used in traditional Siamese trackers
are analyzed at first. We observe that the imbalanced distribution of training
data makes the learned features less discriminative. During the off-line
training phase, an effective sampling strategy is introduced to control this
distribution and make the model focus on the semantic distractors. During
inference, a novel distractor-aware module is designed to perform incremental
learning, which can effectively transfer the general embedding to the current
video domain. In addition, we extend the proposed approach for long-term
tracking by introducing a simple yet effective local-to-global search region
strategy. Extensive experiments on benchmarks show that our approach
significantly outperforms the state-of-the-arts, yielding 9.6% relative gain in
VOT2016 dataset and 35.9% relative gain in UAV20L dataset. The proposed tracker
can perform at 160 FPS on short-term benchmarks and 110 FPS on long-term
benchmarks.
CellLineNet: End-to-End Learning and Transfer Learning For Multiclass Epithelial Breast cell Line Classification via a Convolutional Neural Network
Authors: Darlington Ahiale Akogo, Vincent Appiah, Xavier-Lewis Palmer
Date: 2018-08-18 00:00:00
Tasks: Classification, General Classification, Transfer Learning

Computer Vision for Analyzing and Classifying cells and tissues often require
rigorous lab procedures and so automated Computer Vision solutions have been
sought. Most work in such field usually requires Feature Extractions before the
analysis of such features via Machine Learning and Machine Vision algorithms.
We developed a Convolutional Neural Network that classifies 5 types of
epithelial breast cell lines comprised of two human cancer lines, 2 normal
immortalized lines, and 1 immortalized mouse line (MDA-MB-468, MCF7, 10A, 12A
and HC11) without requiring feature extraction. The Multiclass Cell Line
Classification Convolutional Neural Network extends our earlier work on a
Binary Breast Cancer Cell Line Classification model. CellLineNet is 31-layer
Convolutional Neural Network trained, validated and tested on a 3,252 image
dataset of 5 types of Epithelial Breast cell Lines (MDA-MB-468, MCF7, 10A, 12A
and HC11) in an end-to-end fashion. End-to-End Learning enables CellLineNet to
identify and learn on its own, visual features and regularities most important
to Breast Cancer Cell Line Classification from the dataset of images. Using
Transfer Learning, the 28-layer MobileNet Convolutional Neural Network
architecture with pre-trained ImageNet weights is extended and fine tuned to
the Multiclass Epithelial Breast cell Line Classification problem. CellLineNet
simply requires an imaged Cell Line as input and it outputs the type of breast
epithelial cell line (MDA-MB-468, MCF7, 10A, 12A or HC11) as predicted
probabilities for the 5 classes. CellLineNet scored a 96.67% Accuracy.
Concept Mask: Large-Scale Segmentation from Semantic Concepts
Authors: Yufei Wang, Zhe Lin, Xiaohui Shen, Jianming Zhang, Scott Cohen
Date: 2018-08-18 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Existing works on semantic segmentation typically consider a small number of
labels, ranging from tens to a few hundreds. With a large number of labels,
training and evaluation of such task become extremely challenging due to
correlation between labels and lack of datasets with complete annotations. We
formulate semantic segmentation as a problem of image segmentation given a
semantic concept, and propose a novel system which can potentially handle an
unlimited number of concepts, including objects, parts, stuff, and attributes.
We achieve this using a weakly and semi-supervised framework leveraging
multiple datasets with different levels of supervision. We first train a deep
neural network on a 6M stock image dataset with only image-level labels to
learn visual-semantic embedding on 18K concepts. Then, we refine and extend the
embedding network to predict an attention map, using a curated dataset with
bounding box annotations on 750 concepts. Finally, we train an attention-driven
class agnostic segmentation network using an 80-category fully annotated
dataset. We perform extensive experiments to validate that the proposed system
performs competitively to the state of the art on fully supervised concepts,
and is capable of producing accurate segmentations for weakly learned and
unseen concepts.