pwc-archive/papers-with-abstracts huggingface dataset:
PeRView: A Framework for Personalized Review Selection Using Micro-Reviews
Authors: Muhmmad Al-Khiza'ay, Noora Alallaq, Qusay Alanoz, Adil Al-Azzawi, N. Maheswari
Date: 2018-04-23 00:00:00
Tasks: Decision Making

In the contemporary era, social media has its influence on people in making
decisions. The proliferation of online reviews with diversified and verbose
content often causes problems inaccurate decision making. Since online reviews
have an impact on people of all walks of life while taking decisions, choosing
appropriate reviews based on the podsolization consisting is very important
since it relies on using such micro-reviews consistency to evaluate the review
set section. Micro-reviews are very concise and directly talk about product or
service instead of having unnecessary verbose content. Thus, micro-reviews can
help in choosing reviews based on their personalized consistency that is
related to directly or indirectly to the main profile of the reviews.
Personalized reviews selection that is highly relevant with high personalized
coverage in terms of matching with micro-reviews is the main problem that is
considered in this paper. Furthermore, personalization with user preferences
while making review selection is also considered based on the personalized
users' profile. Towards this end, we proposed a framework known as PeRView for
personalized review selection using micro-reviews based on the proposed
evaluation metric approach which considering two main factors (personalized
matching score and subset size). Personalized Review Selection Algorithm (PRSA)
is proposed which makes use of multiple similarity measures merged to have
highly efficient personalized reviews matching function for selection. The
experimental results based on using reviews dataset which is collected from
YELP.COM while micro-reviews dataset is obtained from Foursqure.COM. show that
the personalized reviews selection is a very empirical case of study.
Preserving Differential Privacy in Convolutional Deep Belief Networks
Authors: NhatHai Phan, Xintao Wu, Dejing Dou
Date: 2017-06-25 00:00:00

The remarkable development of deep learning in medicine and healthcare domain
presents obvious privacy issues, when deep neural networks are built on users'
personal and highly sensitive data, e.g., clinical records, user profiles,
biomedical images, etc. However, only a few scientific studies on preserving
privacy in deep learning have been conducted. In this paper, we focus on
developing a private convolutional deep belief network (pCDBN), which
essentially is a convolutional deep belief network (CDBN) under differential
privacy. Our main idea of enforcing epsilon-differential privacy is to leverage
the functional mechanism to perturb the energy-based objective functions of
traditional CDBNs, rather than their results. One key contribution of this work
is that we propose the use of Chebyshev expansion to derive the approximate
polynomial representation of objective functions. Our theoretical analysis
shows that we can further derive the sensitivity and error bounds of the
approximate polynomial representation. As a result, preserving differential
privacy in CDBNs is feasible. We applied our model in a health social network,
i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for
human behavior prediction, human behavior classification, and handwriting digit
recognition tasks. Theoretical analysis and rigorous experimental evaluations
show that the pCDBN is highly effective. It significantly outperforms existing
solutions.
Vision Meets Drones: A Challenge
Authors: Pengfei Zhu, Longyin Wen, Xiao Bian, Haibin Ling, QinGhua Hu
Date: 2018-04-20 00:00:00
Tasks: Multi-Object Tracking, Object, object-detection, Object Detection, Object Tracking

In this paper we present a large-scale visual object detection and tracking
benchmark, named VisDrone2018, aiming at advancing visual understanding tasks
on the drone platform. The images and video sequences in the benchmark were
captured over various urban/suburban areas of 14 different cities across China
from north to south. Specifically, VisDrone2018 consists of 263 video clips and
10,209 images (no overlap with video clips) with rich annotations, including
object bounding boxes, object categories, occlusion, truncation ratios, etc.
With intensive amount of effort, our benchmark has more than 2.5 million
annotated instances in 179,264 images/video frames. Being the largest such
dataset ever published, the benchmark enables extensive evaluation and
investigation of visual analysis algorithms on the drone platform. In
particular, we design four popular tasks with the benchmark, including object
detection in images, object detection in videos, single object tracking, and
multi-object tracking. All these tasks are extremely challenging in the
proposed dataset due to factors such as occlusion, large scale and pose
variation, and fast motion. We hope the benchmark largely boost the research
and development in visual analysis on drone platforms.
Task Planning in Robotics: an Empirical Comparison of PDDL-based and ASP-based Systems
Authors: Yuqian Jiang, Shiqi Zhang, Piyush Khandelwal, Peter Stone
Date: 2018-04-23 00:00:00
Tasks: Robot Task Planning, Task Planning

Robots need task planning algorithms to sequence actions toward accomplishing
goals that are impossible through individual actions. Off-the-shelf task
planners can be used by intelligent robotics practitioners to solve a variety
of planning problems. However, many different planners exist, each with
different strengths and weaknesses, and there are no general rules for which
planner would be best to apply to a given problem.
  In this article, we empirically compare the performance of state-of-the-art
planners that use either the Planning Domain Description Language (PDDL), or
Answer Set Programming (ASP) as the underlying action language. PDDL is
designed for task planning, and PDDL-based planners are widely used for a
variety of planning problems. ASP is designed for knowledge-intensive
reasoning, but can also be used for solving task planning problems. Given
domain encodings that are as similar as possible, we find that PDDL-based
planners perform better on problems with longer solutions, and ASP-based
planners are better on tasks with a large number of objects or in which complex
reasoning is required to reason about action preconditions and effects. The
resulting analysis can inform selection among general purpose planning systems
for particular robot task planning domains.
Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning
Authors: NhatHai Phan, Xintao Wu, Han Hu, Dejing Dou
Date: 2017-09-18 00:00:00
Tasks: Deep Learning

In this paper, we focus on developing a novel mechanism to preserve
differential privacy in deep neural networks, such that: (1) The privacy budget
consumption is totally independent of the number of training steps; (2) It has
the ability to adaptively inject noise into features based on the contribution
of each to the output; and (3) It could be applied in a variety of different
deep neural networks. To achieve this, we figure out a way to perturb affine
transformations of neurons, and loss functions used in deep neural networks. In
addition, our mechanism intentionally adds "more noise" into features which are
"less relevant" to the model output, and vice-versa. Our theoretical analysis
further derives the sensitivities and error bounds of our mechanism. Rigorous
experiments conducted on MNIST and CIFAR-10 datasets show that our mechanism is
highly effective and outperforms existing solutions.
Parsing Tweets into Universal Dependencies
Authors: Yijia Liu, Yi Zhu, Wanxiang Che, Bing Qin, Nathan Schneider, Noah A. Smith
Date: 2018-04-23 00:00:00
Proceeding: NAACL 2018 6
Tasks: Computational Efficiency, Dependency Parsing, Part-Of-Speech Tagging

We study the problem of analyzing tweets with Universal Dependencies. We
extend the UD guidelines to cover special constructions in tweets that affect
tokenization, part-of-speech tagging, and labeled dependencies. Using the
extended guidelines, we create a new tweet treebank for English (Tweebank v2)
that is four times larger than the (unlabeled) Tweebank v1 introduced by Kong
et al. (2014). We characterize the disagreements between our annotators and
show that it is challenging to deliver consistent annotation due to ambiguity
in understanding and explaining tweets. Nonetheless, using the new treebank, we
build a pipeline system to parse raw tweets into UD. To overcome annotation
noise without sacrificing computational efficiency, we propose a new method to
distill an ensemble of 20 transition-based parsers into a single one. Our
parser achieves an improvement of 2.2 in LAS over the un-ensembled baseline and
outperforms parsers that are state-of-the-art on other treebanks in both
accuracy and speed.
A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers
Authors: Tianyun Zhang, Shaokai Ye, Kaiqi Zhang, Jian Tang, Wujie Wen, Makan Fardad, Yanzhi Wang
Date: 2018-04-10 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Classification, Network Pruning

Weight pruning methods for deep neural networks (DNNs) have been investigated
recently, but prior work in this area is mainly heuristic, iterative pruning,
thereby lacking guarantees on the weight reduction ratio and convergence time.
To mitigate these limitations, we present a systematic weight pruning framework
of DNNs using the alternating direction method of multipliers (ADMM). We first
formulate the weight pruning problem of DNNs as a nonconvex optimization
problem with combinatorial constraints specifying the sparsity requirements,
and then adopt the ADMM framework for systematic weight pruning. By using ADMM,
the original nonconvex optimization problem is decomposed into two subproblems
that are solved iteratively. One of these subproblems can be solved using
stochastic gradient descent, the other can be solved analytically. Besides, our
method achieves a fast convergence rate.
  The weight pruning results are very promising and consistently outperform the
prior work. On the LeNet-5 model for the MNIST data set, we achieve 71.2 times
weight reduction without accuracy loss. On the AlexNet model for the ImageNet
data set, we achieve 21 times weight reduction without accuracy loss. When we
focus on the convolutional layer pruning for computation reductions, we can
reduce the total computation by five times compared with the prior work
(achieving a total of 13.4 times weight reduction in convolutional layers). Our
models and codes are released at https://github.com/KaiqiZhang/admm-pruning
Multi-scale prediction for robust hand detection and classification
Authors: Ding Lu, Yong Wang, Robert Laganiere, Xinbin Luo, Shan Fu
Date: 2018-04-23 00:00:00
Tasks: Classification, General Classification, Hand Detection

In this paper, we present a multi-scale Fully Convolutional Networks
(MSP-RFCN) to robustly detect and classify human hands under various
challenging conditions. In our approach, the input image is passed through the
proposed network to generate score maps, based on multi-scale predictions. The
network has been specifically designed to deal with small objects. It uses an
architecture based on region proposals generated at multiple scales. Our method
is evaluated on challenging hand datasets, namely the Vision for Intelligent
Vehicles and Applications (VIVA) Challenge and the Oxford hand dataset. It is
compared against recent hand detection algorithms. The experimental results
demonstrate that our proposed method achieves state-of-the-art detection for
hands of various sizes.
High Performance Visual Tracking with Circular and Structural Operators
Authors: Peng Gao, Yipeng Ma, Ke Song, Chao Li, Fei Wang, Liyi Xiao, Yan Zhang
Date: 2018-04-23 00:00:00
Tasks: Computational Efficiency, Visual Tracking, Vocal Bursts Intensity Prediction

In this paper, a novel circular and structural operator tracker (CSOT) is
proposed for high performance visual tracking, it not only possesses the
powerful discriminative capability of SOSVM but also efficiently inherits the
superior computational efficiency of DCF. Based on the proposed circular and
structural operators, a set of primal confidence score maps can be obtained by
circular correlating feature maps with their corresponding structural
correlation filters. Furthermore, an implicit interpolation is applied to
convert the multi-resolution feature maps to the continuous domain and make all
primal confidence score maps have the same spatial resolution. Then, we exploit
an efficient ensemble post-processor based on relative entropy, which can
coalesce primal confidence score maps and create an optimal confidence score
map for more accurate localization. The target is localized on the peak of the
optimal confidence score map. Besides, we introduce a collaborative
optimization strategy to update circular and structural operators by
iteratively training structural correlation filters, which significantly
reduces computational complexity and improves robustness. Experimental results
demonstrate that our approach achieves state-of-the-art performance in mean AUC
scores of 71.5% and 69.4% on the OTB-2013 and OTB-2015 benchmarks respectively,
and obtains a third-best expected average overlap (EAO) score of 29.8% on the
VOT-2017 benchmark.
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
Authors: Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, Benjamin Van Durme
Date: 2018-04-23 00:00:00
Proceeding: EMNLP (ACL) 2018 10
Tasks: Natural Language Inference, Sentence

We present a large-scale collection of diverse natural language inference
(NLI) datasets that help provide insight into how well a sentence
representation captures distinct types of reasoning. The collection results
from recasting 13 existing datasets from 7 semantic phenomena into a common NLI
structure, resulting in over half a million labeled context-hypothesis pairs in
total. We refer to our collection as the DNC: Diverse Natural Language
Inference Collection. The DNC is available online at https://www.decomp.net,
and will grow over time as additional resources are recast and added from novel
sources.
AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI
Authors: Chen Yu, Bojan Karlas, Jie Zhong, Ce Zhang, Ji Liu
Date: 2018-03-17 00:00:00
Tasks: AutoML, Model Selection

AutoML has become a popular service that is provided by most leading cloud
service providers today. In this paper, we focus on the AutoML problem from the
\emph{service provider's perspective}, motivated by the following practical
consideration: When an AutoML service needs to serve {\em multiple users} with
{\em multiple devices} at the same time, how can we allocate these devices to
users in an efficient way? We focus on GP-EI, one of the most popular
algorithms for automatic model selection and hyperparameter tuning, used by
systems such as Google Vizer. The technical contribution of this paper is the
first multi-device, multi-tenant algorithm for GP-EI that is aware of
\emph{multiple} computation devices and multiple users sharing the same set of
computation devices. Theoretically, given $N$ users and $M$ devices, we obtain
a regret bound of $O((\text{\bf {MIU}}(T,K) + M)\frac{N^2}{M})$, where
$\text{\bf {MIU}}(T,K)$ refers to the maximal incremental uncertainty up to
time $T$ for the covariance matrix $K$. Empirically, we evaluate our algorithm
on two applications of automatic model selection, and show that our algorithm
significantly outperforms the strategy of serving users independently.
Moreover, when multiple computation devices are available, we achieve
near-linear speedup when the number of users is much larger than the number of
devices.
Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language Model
Authors: Sabrina J. Mielke, Jason Eisner
Date: 2018-04-23 00:00:00
Tasks: Language Modeling, Language Modelling, Sentence, Vocal Bursts Valence Prediction

We show how the spellings of known words can help us deal with unknown words in open-vocabulary NLP tasks. The method we propose can be used to extend any closed-vocabulary generative model, but in this paper we specifically consider the case of neural language modeling. Our Bayesian generative story combines a standard RNN language model (generating the word tokens in each sentence) with an RNN-based spelling model (generating the letters in each word type). These two RNNs respectively capture sentence structure and word structure, and are kept separate as in linguistics. By invoking the second RNN to generate spellings for novel words in context, we obtain an open-vocabulary language model. For known words, embeddings are naturally inferred by combining evidence from type spelling and token context. Comparing to baselines (including a novel strong baseline), we beat previous work and establish state-of-the-art results on multiple datasets.
Knowledge-based end-to-end memory networks
Authors: Jatin Ganhotra, Lazaros Polymenakos
Date: 2018-04-23 00:00:00
Tasks: Goal-Oriented Dialog, Retrieval

End-to-end dialog systems have become very popular because they hold the
promise of learning directly from human to human dialog interaction. Retrieval
and Generative methods have been explored in this area with mixed results. A
key element that is missing so far, is the incorporation of a-priori knowledge
about the task at hand. This knowledge may exist in the form of structured or
unstructured information. As a first step towards this direction, we present a
novel approach, Knowledge based end-to-end memory networks (KB-memN2N), which
allows special handling of named entities for goal-oriented dialog tasks. We
present results on two datasets, DSTC6 challenge dataset and dialog bAbI tasks.
Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input
Authors: Youmna Farag, Helen Yannakoudakis, Ted Briscoe
Date: 2018-04-18 00:00:00
Proceeding: NAACL 2018 6
Tasks: Automated Essay Scoring

We demonstrate that current state-of-the-art approaches to Automated Essay Scoring (AES) are not well-suited to capturing adversarially crafted input of grammatical but incoherent sequences of sentences. We develop a neural model of local coherence that can effectively learn connectedness features between sentences, and propose a framework for integrating and jointly training the local coherence model with a state-of-the-art AES model. We evaluate our approach against a number of baselines and experimentally demonstrate its effectiveness on both the AES task and the task of flagging adversarial input, further contributing to the development of an approach that strengthens the validity of neural essay scoring models.
Linguistically-Informed Self-Attention for Semantic Role Labeling
Authors: Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, Andrew McCallum
Date: 2018-04-23 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Dependency Parsing, Multi-Task Learning, Part-Of-Speech Tagging, Predicate Detection, Semantic Role Labeling, Semantic Role Labeling (predicted predicates), Word Embeddings

Current state-of-the-art semantic role labeling (SRL) uses a deep neural
network with no explicit linguistic features. However, prior work has shown
that gold syntax trees can dramatically improve SRL decoding, suggesting the
possibility of increased accuracy from explicit modeling of syntax. In this
work, we present linguistically-informed self-attention (LISA): a neural
network model that combines multi-head self-attention with multi-task learning
across dependency parsing, part-of-speech tagging, predicate detection and SRL.
Unlike previous models which require significant pre-processing to prepare
linguistic features, LISA can incorporate syntax using merely raw tokens as
input, encoding the sequence only once to simultaneously perform parsing,
predicate detection and role labeling for all predicates. Syntax is
incorporated by training one attention head to attend to syntactic parents for
each token. Moreover, if a high-quality syntactic parse is already available,
it can be beneficially injected at test time without re-training our SRL model.
In experiments on CoNLL-2005 SRL, LISA achieves new state-of-the-art
performance for a model using predicted predicates and standard word
embeddings, attaining 2.5 F1 absolute higher than the previous state-of-the-art
on newswire and more than 3.5 F1 on out-of-domain data, nearly 10% reduction in
error. On ConLL-2012 English SRL we also show an improvement of more than 2.5
F1. LISA also out-performs the state-of-the-art with contextually-encoded
(ELMo) word representations, by nearly 1.0 F1 on news and more than 2.0 F1 on
out-of-domain text.
A neural interlingua for multilingual machine translation
Authors: Yichao Lu, Phillip Keung, Faisal Ladhak, Vikas Bhardwaj, Shaonan Zhang, Jason Sun
Date: 2018-04-23 00:00:00
Proceeding: WS 2018 10
Tasks: Decoder, Machine Translation, NMT, Sentence, Sentence Embeddings, Translation

We incorporate an explicit neural interlingua into a multilingual
encoder-decoder neural machine translation (NMT) architecture. We demonstrate
that our model learns a language-independent representation by performing
direct zero-shot translation (without using pivot translation), and by using
the source sentence embeddings to create an English Yelp review classifier
that, through the mediation of the neural interlingua, can also classify French
and German reviews. Furthermore, we show that, despite using a smaller number
of parameters than a pairwise collection of bilingual NMT models, our approach
produces comparable BLEU scores for each language pair in WMT15.
A new class of metrics for learning on real-valued and structured data
Authors: Ruiyu Yang, Yuxiang Jiang, Scott Mathews, Elizabeth A. Housworth, Matthew W. Hahn, Predrag Radivojac
Date: 2016-03-22 00:00:00

We propose a new class of metrics on sets, vectors, and functions that can be
used in various stages of data mining, including exploratory data analysis,
learning, and result interpretation. These new distance functions unify and
generalize some of the popular metrics, such as the Jaccard and bag distances
on sets, Manhattan distance on vector spaces, and Marczewski-Steinhaus distance
on integrable functions. We prove that the new metrics are complete and show
useful relationships with $f$-divergences for probability distributions. To
further extend our approach to structured objects such as concept hierarchies
and ontologies, we introduce information-theoretic metrics on directed acyclic
graphs drawn according to a fixed probability distribution. We conduct
empirical investigation to demonstrate intuitive interpretation of the new
metrics and their effectiveness on real-valued, high-dimensional, and
structured data. Extensive comparative evaluation demonstrates that the new
metrics outperformed multiple similarity and dissimilarity functions
traditionally used in data mining, including the Minkowski family, the
fractional $L^p$ family, two $f$-divergences, cosine distance, and two
correlation coefficients. Finally, we argue that the new class of metrics is
particularly appropriate for rapid processing of high-dimensional and
structured data in distance-based learning.