pwc-archive/papers-with-abstracts huggingface dataset:
Assertion-based QA with Question-Aware Open Information Extraction
Authors: Zhao Yan, Duyu Tang, Nan Duan, Shujie Liu, Wendi Wang, Daxin Jiang, Ming Zhou, Zhoujun Li
Date: 2018-01-23 00:00:00
Tasks: Learning-To-Rank, Open-Domain Question Answering, Open Information Extraction, Question Answering, Reading Comprehension

We present assertion based question answering (ABQA), an open domain question
answering task that takes a question and a passage as inputs, and outputs a
semi-structured assertion consisting of a subject, a predicate and a list of
arguments. An assertion conveys more evidences than a short answer span in
reading comprehension, and it is more concise than a tedious passage in
passage-based QA. These advantages make ABQA more suitable for human-computer
interaction scenarios such as voice-controlled speakers. Further progress
towards improving ABQA requires richer supervised dataset and powerful models
of text understanding. To remedy this, we introduce a new dataset called
WebAssertions, which includes hand-annotated QA labels for 358,427 assertions
in 55,960 web passages. To address ABQA, we develop both generative and
extractive approaches. The backbone of our generative approach is sequence to
sequence learning. In order to capture the structure of the output assertion,
we introduce a hierarchical decoder that first generates the structure of the
assertion and then generates the words of each field. The extractive approach
is based on learning to rank. Features at different levels of granularity are
designed to measure the semantic relevance between a question and an assertion.
Experimental results show that our approaches have the ability to infer
question-aware assertions from a passage. We further evaluate our approaches by
incorporating the ABQA results as additional features in passage-based QA.
Results on two datasets show that ABQA features significantly improve the
accuracy on passage-based~QA.
Comparison Training for Computer Chinese Chess
Authors: Wen-Jie Tseng, Jr-Chang Chen, I-Chen Wu, TingHan Wei
Date: 2018-01-23 00:00:00

This paper describes the application of comparison training (CT) for
automatic feature weight tuning, with the final objective of improving the
evaluation functions used in Chinese chess programs. First, we propose an
n-tuple network to extract features, since n-tuple networks require very little
expert knowledge through its large numbers of features, while simulta-neously
allowing easy access. Second, we propose a novel evalua-tion method that
incorporates tapered eval into CT. Experiments show that with the same features
and the same Chinese chess program, the automatically tuned comparison training
feature weights achieved a win rate of 86.58% against the weights that were
hand-tuned. The above trained version was then improved by adding additional
features, most importantly n-tuple features. This improved version achieved a
win rate of 81.65% against the trained version without additional features.
Building an Ellipsis-aware Chinese Dependency Treebank for Web Text
Authors: Xuancheng Ren, Xu sun, Ji Wen, Bingzhen Wei, Weidong Zhan, Zhiyuan Zhang
Date: 2018-01-20 00:00:00
Proceeding: LREC 2018 5
Tasks: Dependency Parsing, Knowledge Base Construction, Sentence

Web 2.0 has brought with it numerous user-produced data revealing one's
thoughts, experiences, and knowledge, which are a great source for many tasks,
such as information extraction, and knowledge base construction. However, the
colloquial nature of the texts poses new challenges for current natural
language processing techniques, which are more adapt to the formal form of the
language. Ellipsis is a common linguistic phenomenon that some words are left
out as they are understood from the context, especially in oral utterance,
hindering the improvement of dependency parsing, which is of great importance
for tasks relied on the meaning of the sentence. In order to promote research
in this area, we are releasing a Chinese dependency treebank of 319 weibos,
containing 572 sentences with omissions restored and contexts reserved.
Improving One-Shot Learning through Fusing Side Information
Authors: Yao-Hung Hubert Tsai, Ruslan Salakhutdinov
Date: 2017-10-23 00:00:00
Tasks: One-Shot Learning, regression, Representation Learning

Deep Neural Networks (DNNs) often struggle with one-shot learning where we
have only one or a few labeled training examples per category. In this paper,
we argue that by using side information, we may compensate the missing
information across classes. We introduce two statistical approaches for fusing
side information into data representation learning to improve one-shot
learning. First, we propose to enforce the statistical dependency between data
representations and multiple types of side information. Second, we introduce an
attention mechanism to efficiently treat examples belonging to the
'lots-of-examples' classes as quasi-samples (additional training samples) for
'one-example' classes. We empirically show that our learning architecture
improves over traditional softmax regression networks as well as
state-of-the-art attentional regression networks on one-shot recognition tasks.
Let's Dance: Learning From Online Dance Videos
Authors: Daniel Castro, Steven Hickson, Patsorn Sangkloy, Bhavishya Mittal, Sean Dai, James Hays, Irfan Essa
Date: 2018-01-23 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization

In recent years, deep neural network approaches have naturally extended to
the video domain, in their simplest case by aggregating per-frame
classifications as a baseline for action recognition. A majority of the work in
this area extends from the imaging domain, leading to visual-feature heavy
approaches on temporal data. To address this issue we introduce "Let's Dance",
a 1000 video dataset (and growing) comprised of 10 visually overlapping dance
categories that require motion for their classification. We stress the
important of human motion as a key distinguisher in our work given that, as we
show in this work, visual information is not sufficient to classify
motion-heavy categories. We compare our datasets' performance using imaging
techniques with UCF-101 and demonstrate this inherent difficulty. We present a
comparison of numerous state-of-the-art techniques on our dataset using three
different representations (video, optical flow and multi-person pose data) in
order to analyze these approaches. We discuss the motion parameterization of
each of them and their value in learning to categorize online dance videos.
Lastly, we release this dataset (and its three representations) for the
research community to use.
Learning Networks from Random Walk-Based Node Similarities
Authors: Jeremy G. Hoskins, Cameron Musco, Christopher Musco, Charalampos E. Tsourakakis
Date: 2018-01-23 00:00:00
Tasks: Anomaly Detection, Clustering, Graph Clustering, Graph Learning, Graph Similarity

Digital presence in the world of online social media entails significant
privacy risks. In this work we consider a privacy threat to a social network in
which an attacker has access to a subset of random walk-based node
similarities, such as effective resistances (i.e., commute times) or
personalized PageRank scores. Using these similarities, the attacker's goal is
to infer as much information as possible about the underlying network,
including any remaining unknown pairwise node similarities and edges.
  For the effective resistance metric, we show that with just a small subset of
measurements, the attacker can learn a large fraction of edges in a social
network, even when the measurements are noisy. We also show that it is possible
to learn a graph which accurately matches the underlying network on all other
effective resistances. This second observation is interesting from a data
mining perspective, since it can be expensive to accurately compute all
effective resistances. As an alternative, our graphs learned from just a subset
of approximate effective resistances can be used as surrogates in a wide range
of applications that use effective resistances to probe graph structure,
including for graph clustering, node centrality evaluation, and anomaly
detection.
  We obtain our results by formalizing the graph learning objective
mathematically, using two optimization problems. One formulation is convex and
can be solved provably in polynomial time. The other is not, but we solve it
efficiently with projected gradient and coordinate descent. We demonstrate the
effectiveness of these methods on a number of social networks obtained from
Facebook. We also discuss how our methods can be generalized to other random
walk-based similarities, such as personalized PageRank. Our code is available
at https://github.com/cnmusco/graph-similarity-learning.
Secure Mobile Crowdsensing with Deep Learning
Authors: Liang Xiao, Donghua Jiang, Dongjin Xu, Ning An
Date: 2018-01-23 00:00:00
Tasks: Deep Learning, Intrusion Detection

In order to stimulate secure sensing for Internet of Things (IoT)
applications such as healthcare and traffic monitoring, mobile crowdsensing
(MCS) systems have to address security threats, such as jamming, spoofing and
faked sensing attacks, during both the sensing and the information exchange
processes in large-scale dynamic and heterogenous networks. In this article, we
investigate secure mobile crowdsensing and present how to use deep learning
(DL) methods such as stacked autoencoder (SAE), deep neural network (DNN), and
convolutional neural network (CNN) to improve the MCS security approaches
including authentication, privacy protection, faked sensing countermeasures,
intrusion detection and anti-jamming transmissions in MCS. We discuss the
performance gain of these DL-based approaches compared with traditional
security schemes and identify the challenges that need to be addressed to
implement them in practical MCS systems.
Learning to Prune Filters in Convolutional Neural Networks
Authors: Qiangui Huang, Kevin Zhou, Suya You, Ulrich Neumann
Date: 2018-01-23 00:00:00
Tasks: Semantic Segmentation

Many state-of-the-art computer vision algorithms use large scale
convolutional neural networks (CNNs) as basic building blocks. These CNNs are
known for their huge number of parameters, high redundancy in weights, and
tremendous computing resource consumptions. This paper presents a learning
algorithm to simplify and speed up these CNNs. Specifically, we introduce a
"try-and-learn" algorithm to train pruning agents that remove unnecessary CNN
filters in a data-driven way. With the help of a novel reward function, our
agents removes a significant number of filters in CNNs while maintaining
performance at a desired level. Moreover, this method provides an easy control
of the tradeoff between network performance and its scale. Per- formance of our
algorithm is validated with comprehensive pruning experiments on several
popular CNNs for visual recognition and semantic segmentation tasks.
Dependent landmark drift: robust point set registration with a Gaussian mixture model and a statistical shape model
Authors: Osamu Hirose
Date: 2017-11-17 00:00:00
Tasks: model, Object

The goal of point set registration is to find point-by-point correspondences
between point sets, each of which characterizes the shape of an object. Because
local preservation of object geometry is assumed, prevalent algorithms in the
area can often elegantly solve the problems without using geometric information
specific to the objects. This means that registration performance can be
further improved by using prior knowledge of object geometry. In this paper, we
propose a novel point set registration method using the Gaussian mixture model
with prior shape information encoded as a statistical shape model. Our
transformation model is defined as a combination of the similar transformation,
motion coherence, and the statistical shape model. Therefore, the proposed
method works effectively if the target point set includes outliers and missing
regions, or if it is rotated. The computational cost can be reduced to linear,
and therefore the method is scalable to large point sets. The effectiveness of
the method will be verified through comparisons with existing algorithms using
datasets concerning human body shapes, hands, and faces.
CHALET: Cornell House Agent Learning Environment
Authors: Claudia Yan, Dipendra Misra, Andrew Bennnett, Aaron Walsman, Yonatan Bisk, Yoav Artzi
Date: 2018-01-23 00:00:00

We present CHALET, a 3D house simulator with support for navigation and manipulation. CHALET includes 58 rooms and 10 house configuration, and allows to easily create new house and room layouts. CHALET supports a range of common household activities, including moving objects, toggling appliances, and placing objects inside closeable containers. The environment and actions available are designed to create a challenging domain to train and evaluate autonomous agents, including for tasks that combine language, vision, and planning in a dynamic environment.
Flexible Deep Neural Network Processing
Authors: Hokchhay Tann, Soheil Hashemi, Sherief Reda
Date: 2018-01-23 00:00:00

The recent success of Deep Neural Networks (DNNs) has drastically improved
the state of the art for many application domains. While achieving high
accuracy performance, deploying state-of-the-art DNNs is a challenge since they
typically require billions of expensive arithmetic computations. In addition,
DNNs are typically deployed in ensemble to boost accuracy performance, which
further exacerbates the system requirements. This computational overhead is an
issue for many platforms, e.g. data centers and embedded systems, with tight
latency and energy budgets. In this article, we introduce flexible DNNs
ensemble processing technique, which achieves large reduction in average
inference latency while incurring small to negligible accuracy drop. Our
technique is flexible in that it allows for dynamic adaptation between quality
of results (QoR) and execution runtime. We demonstrate the effectiveness of the
technique on AlexNet and ResNet-50 using the ImageNet dataset. This technique
can also easily handle other types of networks.
Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data
Authors: Susan Athey, David Blei, Robert Donnelly, Francisco Ruiz, Tobias Schmidt
Date: 2018-01-22 00:00:00
Tasks: counterfactual, Variational Inference

This paper analyzes consumer choices over lunchtime restaurants using data
from a sample of several thousand anonymous mobile phone users in the San
Francisco Bay Area. The data is used to identify users' approximate typical
morning location, as well as their choices of lunchtime restaurants. We build a
model where restaurants have latent characteristics (whose distribution may
depend on restaurant observables, such as star ratings, food category, and
price range), each user has preferences for these latent characteristics, and
these preferences are heterogeneous across users. Similarly, each item has
latent characteristics that describe users' willingness to travel to the
restaurant, and each user has individual-specific preferences for those latent
characteristics. Thus, both users' willingness to travel and their base utility
for each restaurant vary across user-restaurant pairs. We use a Bayesian
approach to estimation. To make the estimation computationally feasible, we
rely on variational inference to approximate the posterior distribution, as
well as stochastic gradient descent as a computational approach. Our model
performs better than more standard competing models such as multinomial logit
and nested logit models, in part due to the personalization of the estimates.
We analyze how consumers re-allocate their demand after a restaurant closes to
nearby restaurants versus more distant restaurants with similar
characteristics, and we compare our predictions to actual outcomes. Finally, we
show how the model can be used to analyze counterfactual questions such as what
type of restaurant would attract the most consumers in a given location.
Weighted Orthogonal Components Regression Analysis
Authors: Xiaogang Su, Yaa Wonkye, Pei Wang, Xiangrong Yin
Date: 2017-09-13 00:00:00
Tasks: regression

In the multiple linear regression setting, we propose a general framework,
termed weighted orthogonal components regression (WOCR), which encompasses many
known methods as special cases, including ridge regression and principal
components regression. WOCR makes use of the monotonicity inherent in
orthogonal components to parameterize the weight function. The formulation
allows for efficient determination of tuning parameters and hence is
computationally advantageous. Moreover, WOCR offers insights for deriving new
better variants. Specifically, we advocate weighting components based on their
correlations with the response, which leads to enhanced predictive performance.
Both simulated studies and real data examples are provided to assess and
illustrate the advantages of the proposed methods.
Straggler Mitigation in Distributed Optimization Through Data Encoding
Authors: Can Karakus, Yifan Sun, Suhas Diggavi, Wotao Yin
Date: 2017-11-14 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Distributed Optimization

Slow running or straggler tasks can significantly reduce computation speed in
distributed computation. Recently, coding-theory-inspired approaches have been
applied to mitigate the effect of straggling, through embedding redundancy in
certain linear computational steps of the optimization algorithm, thus
completing the computation without waiting for the stragglers. In this paper,
we propose an alternate approach where we embed the redundancy directly in the
data itself, and allow the computation to proceed completely oblivious to
encoding. We propose several encoding schemes, and demonstrate that popular
batch algorithms, such as gradient descent and L-BFGS, applied in a
coding-oblivious manner, deterministically achieve sample path linear
convergence to an approximate solution of the original problem, using an
arbitrarily varying subset of the nodes at each iteration. Moreover, this
approximation can be controlled by the amount of redundancy and the number of
nodes used in each iteration. We provide experimental results demonstrating the
advantage of the approach over uncoded and data replication strategies.
Time-Dependent Representation for Neural Event Sequence Prediction
Authors: Yang Li, Nan Du, Samy Bengio
Date: 2017-07-31 00:00:00
Proceeding: ICLR 2018 1
Tasks: Prediction, Sentence

Existing sequence prediction methods are mostly concerned with
time-independent sequences, in which the actual time span between events is
irrelevant and the distance between events is simply the difference between
their order positions in the sequence. While this time-independent view of
sequences is applicable for data such as natural languages, e.g., dealing with
words in a sentence, it is inappropriate and inefficient for many real world
events that are observed and collected at unequally spaced points of time as
they naturally arise, e.g., when a person goes to a grocery store or makes a
phone call. The time span between events can carry important information about
the sequence dependence of human behaviors. In this work, we propose a set of
methods for using time in sequence prediction. Because neural sequence models
such as RNN are more amenable for handling token-like input, we propose two
methods for time-dependent event representation, based on the intuition on how
time is tokenized in everyday life and previous work on embedding
contextualization. We also introduce two methods for using next event duration
as regularization for training a sequence prediction model. We discuss these
methods based on recurrent neural nets. We evaluate these methods as well as
baseline models on five datasets that resemble a variety of sequence prediction
tasks. The experiments revealed that the proposed methods offer accuracy gain
over baseline models in a range of settings.
The Hybrid Bootstrap: A Drop-in Replacement for Dropout
Authors: Robert Kosar, David W. Scott
Date: 2018-01-22 00:00:00

Regularization is an important component of predictive model building. The
hybrid bootstrap is a regularization technique that functions similarly to
dropout except that features are resampled from other training points rather
than replaced with zeros. We show that the hybrid bootstrap offers superior
performance to dropout. We also present a sampling based technique to simplify
hyperparameter choice. Next, we provide an alternative sampling technique for
convolutional neural networks. Finally, we demonstrate the efficacy of the
hybrid bootstrap on non-image tasks using tree-based models.
Early Detection of Social Media Hoaxes at Scale
Authors: Arkaitz Zubiaga, Aiqi Jiang
Date: 2018-01-22 00:00:00
Tasks: Veracity Classification, Word Embeddings

The unmoderated nature of social media enables the diffusion of hoaxes, which in turn jeopardises the credibility of information gathered from social media platforms. Existing research on automated detection of hoaxes has the limitation of using relatively small datasets, owing to the difficulty of getting labelled data. This in turn has limited research exploring early detection of hoaxes as well as exploring other factors such as the effect of the size of the training data or the use of sliding windows. To mitigate this problem, we introduce a semi-automated method that leverages the Wikidata knowledge base to build large-scale datasets for veracity classification, focusing on celebrity death reports. This enables us to create a dataset with 4,007 reports including over 13 million tweets, 15% of which are fake. Experiments using class-specific representations of word embeddings show that we can achieve F1 scores nearing 72% within 10 minutes of the first tweet being posted when we expand the size of the training data following our semi-automated means. Our dataset represents a realistic scenario with a real distribution of true, commemorative and false stories, which we release for further use as a benchmark in future research.
Semi-Supervised Convolutional Neural Networks for Human Activity Recognition
Authors: Ming Zeng, Tong Yu, Xiao Wang, Le T. Nguyen, Ole J. Mengshoel, Ian Lane
Date: 2018-01-22 00:00:00
Tasks: Activity Recognition, Diversity, Feature Engineering, Human Activity Recognition

Labeled data used for training activity recognition classifiers are usually
limited in terms of size and diversity. Thus, the learned model may not
generalize well when used in real-world use cases. Semi-supervised learning
augments labeled examples with unlabeled examples, often resulting in improved
performance. However, the semi-supervised methods studied in the activity
recognition literatures assume that feature engineering is already done. In
this paper, we lift this assumption and present two semi-supervised methods
based on convolutional neural networks (CNNs) to learn discriminative hidden
features. Our semi-supervised CNNs learn from both labeled and unlabeled data
while also performing feature learning on raw sensor data. In experiments on
three real world datasets, we show that our CNNs outperform supervised methods
and traditional semi-supervised learning methods by up to 18% in mean F1-score
(Fm).