pwc-archive/papers-with-abstracts huggingface dataset:
Neural Variational Hybrid Collaborative Filtering
Authors: Teng Xiao, Shangsong Liang, Hong Shen, Zaiqiao Meng
Date: 2018-10-12 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Collaborative Filtering (CF) is one of the most used methods for Recommender
System. Because of the Bayesian nature and nonlinearity, deep generative
models, e.g. Variational Autoencoder (VAE), have been applied into CF task, and
have achieved great performance. However, most VAE-based methods suffer from
matrix sparsity and consider the prior of users' latent factors to be the same,
which leads to poor latent representations of users and items. Additionally,
most existing methods model latent factors of users only and but not items,
which makes them not be able to recommend items to a new user. To tackle these
problems, we propose a Neural Variational Hybrid Collaborative Filtering,
NVHCF. Specifically, we consider both the generative processes of users and
items, and the prior of latent factors of users and items to be side
informationspecific, which enables our model to alleviate matrix sparsity and
learn better latent representations of users and items. For inference purpose,
we derived a Stochastic Gradient Variational Bayes (SGVB) algorithm to
analytically approximate the intractable distributions of latent factors of
users and items. Experiments conducted on two large datasets have showed our
methods significantly outperform the state-of-the-art CF methods, including the
VAE-based methods.
Field-aware Neural Factorization Machine for Click-Through Rate Prediction
Authors: Li Zhang, Weichen Shen, Shijian Li, Gang Pan
Date: 2019-02-25 00:00:00
Tasks: Click-Through Rate Prediction, Feature Engineering, Prediction, Recommendation Systems

Recommendation systems and computing advertisements have gradually entered
the field of academic research from the field of commercial applications.
Click-through rate prediction is one of the core research issues because the
prediction accuracy affects the user experience and the revenue of merchants
and platforms. Feature engineering is very important to improve click-through
rate prediction. Traditional feature engineering heavily relies on people's
experience, and is difficult to construct a feature combination that can
describe the complex patterns implied in the data. This paper combines
traditional feature combination methods and deep neural networks to automate
feature combinations to improve the accuracy of click-through rate prediction.
We propose a mechannism named 'Field-aware Neural Factorization Machine'
(FNFM). This model can have strong second order feature interactive learning
ability like Field-aware Factorization Machine, on this basis, deep neural
network is used for higher-order feature combination learning. Experiments show
that the model has stronger expression ability than current deep learning
feature combination models like the DeepFM, DCN and NFM.
Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds
Authors: Igor Labutov, Bishan Yang, Anusha Prakash, Amos Azaria
Date: 2019-02-25 00:00:00
Proceeding: ACL 2018 7
Tasks: Question Answering, Reading Comprehension

Question Answering (QA), as a research field, has primarily focused on either
knowledge bases (KBs) or free text as a source of knowledge. These two sources
have historically shaped the kinds of questions that are asked over these
sources, and the methods developed to answer them. In this work, we look
towards a practical use-case of QA over user-instructed knowledge that uniquely
combines elements of both structured QA over knowledge bases, and unstructured
QA over narrative, introducing the task of multi-relational QA over personal
narrative. As a first step towards this goal, we make three key contributions:
(i) we generate and release TextWorldsQA, a set of five diverse datasets, where
each dataset contains dynamic narrative that describes entities and relations
in a simulated world, paired with variably compositional questions over that
knowledge, (ii) we perform a thorough evaluation and analysis of several
state-of-the-art QA models and their variants at this task, and (iii) we
release a lightweight Python-based framework we call TextWorlds for easily
generating arbitrary additional worlds and narrative, with the goal of allowing
the community to create and share a growing collection of diverse worlds as a
test-bed for this task.
Transfer Learning for Sequences via Learning to Collocate
Authors: Wanyun Cui, Guangyu Zheng, Zhiqiang Shen, Sihang Jiang, Wei Wang
Date: 2019-02-25 00:00:00
Proceeding: ICLR 2019 5
Tasks: NER, POS, POS Tagging, Sentence, Sentence Classification, Sentiment Analysis, Transfer Learning

Transfer learning aims to solve the data sparsity for a target domain by
applying information of the source domain. Given a sequence (e.g. a natural
language sentence), the transfer learning, usually enabled by recurrent neural
network (RNN), represents the sequential information transfer. RNN uses a chain
of repeating cells to model the sequence data. However, previous studies of
neural network based transfer learning simply represents the whole sentence by
a single vector, which is unfeasible for seq2seq and sequence labeling.
Meanwhile, such layer-wise transfer learning mechanisms lose the fine-grained
cell-level information from the source domain.
  In this paper, we proposed the aligned recurrent transfer, ART, to achieve
cell-level information transfer. ART is under the pre-training framework. Each
cell attentively accepts transferred information from a set of positions in the
source domain. Therefore, ART learns the cross-domain word collocations in a
more flexible way. We conducted extensive experiments on both sequence labeling
tasks (POS tagging, NER) and sentence classification (sentiment analysis). ART
outperforms the state-of-the-arts over all experiments.
Leveraging Knowledge Bases in LSTMs for Improving Machine Reading
Authors: Bishan Yang, Tom Mitchell
Date: 2019-02-25 00:00:00
Proceeding: ACL 2017 7
Tasks: Entity Extraction using GAN, Event Extraction, Feature Engineering, Reading Comprehension

This paper focuses on how to take advantage of external knowledge bases (KBs)
to improve recurrent neural networks for machine reading. Traditional methods
that exploit knowledge from KBs encode knowledge as discrete indicator
features. Not only do these features generalize poorly, but they require
task-specific feature engineering to achieve good performance. We propose
KBLSTM, a novel neural model that leverages continuous representations of KBs
to enhance the learning of recurrent neural networks for machine reading. To
effectively integrate background knowledge with information from the currently
processed text, our model employs an attention mechanism with a sentinel to
adaptively decide whether to attend to background knowledge and which
information from KBs is useful. Experimental results show that our model
achieves accuracies that surpass the previous state-of-the-art results for both
entity extraction and event extraction on the widely used ACE2005 dataset.
Lattice CNNs for Matching Based Chinese Question Answering
Authors: Yuxuan Lai, Yansong Feng, Xiaohan Yu, Zheng Wang, Kun Xu, Dongyan Zhao
Date: 2019-02-25 00:00:00
Tasks: Diversity, Question Answering, Text Matching

Short text matching often faces the challenges that there are great word
mismatch and expression diversity between the two texts, which would be further
aggravated in languages like Chinese where there is no natural space to segment
words explicitly. In this paper, we propose a novel lattice based CNN model
(LCNs) to utilize multi-granularity information inherent in the word lattice
while maintaining strong ability to deal with the introduced noisy information
for matching based question answering in Chinese. We conduct extensive
experiments on both document based question answering and knowledge based
question answering tasks, and experimental results show that the LCNs models
can significantly outperform the state-of-the-art matching models and strong
baselines by taking advantages of better ability to distill rich but
discriminative information from the word lattice input.
Privacy-Preserving Action Recognition using Coded Aperture Videos
Authors: Zihao W. Wang, Vibhav Vineet, Francesco Pittaluga, Sudipta Sinha, Oliver Cossairt, Sing Bing Kang
Date: 2019-02-25 00:00:00
Tasks: Action Recognition, Image Restoration, Privacy Preserving, Temporal Action Localization, Translation

The risk of unauthorized remote access of streaming video from networked
cameras underlines the need for stronger privacy safeguards. We propose a
lens-free coded aperture camera system for human action recognition that is
privacy-preserving. While coded aperture systems exist, we believe ours is the
first system designed for action recognition without the need for image
restoration as an intermediate step. Action recognition is done using a deep
network that takes in as input, non-invertible motion features between pairs of
frames computed using phase correlation and log-polar transformation. Phase
correlation encodes translation while the log polar transformation encodes
in-plane rotation and scaling. We show that the translation features are
independent of the coded aperture design, as long as its spectral response
within the bandwidth has no zeros. Stacking motion features computed on frames
at multiple different strides in the video can improve accuracy. Preliminary
results on simulated data based on a subset of the UCF and NTU datasets are
promising. We also describe our prototype lens-free coded aperture camera
system, and results for real captured videos are mixed.
SSA-CNN: Semantic Self-Attention CNN for Pedestrian Detection
Authors: Chengju Zhou, Meiqing Wu, Siew-Kei Lam
Date: 2019-02-25 00:00:00
Tasks: Autonomous Driving, Computational Efficiency, Pedestrian Detection, Segmentation, Semantic Segmentation

Pedestrian detection plays an important role in many applications such as autonomous driving. We propose a method that explores semantic segmentation results as self-attention cues to significantly improve the pedestrian detection performance. Specifically, a multi-task network is designed to jointly learn semantic segmentation and pedestrian detection from image datasets with weak box-wise annotations. The semantic segmentation feature maps are concatenated with corresponding convolution features maps to provide more discriminative features for pedestrian detection and pedestrian classification. By jointly learning segmentation and detection, our proposed pedestrian self-attention mechanism can effectively identify pedestrian regions and suppress backgrounds. In addition, we propose to incorporate semantic attention information from multi-scale layers into deep convolution neural network to boost pedestrian detection. Experiment results show that the proposed method achieves the best detection performance with MR of 6.27% on Caltech dataset and obtain competitive performance on CityPersons dataset while maintaining high computational efficiency.
Wasserstein GAN Can Perform PCA
Authors: Jaewoong Cho, Changho Suh
Date: 2019-02-25 00:00:00

Generative Adversarial Networks (GANs) have become a powerful framework to learn generative models that arise across a wide variety of domains. While there has been a recent surge in the development of numerous GAN architectures with distinct optimization metrics, we are still lacking in our understanding on how far away such GANs are from optimality. In this paper, we make progress on a theoretical understanding of the GANs under a simple linear-generator Gaussian-data setting where the optimal maximum-likelihood generator is known to perform Principal Component Analysis (PCA). We find that the original GAN by Goodfellow et. al. fails to recover the optimal PCA solution. On the other hand, we show that Wasserstein GAN can approach the PCA solution in the limit of sample size, and hence it may serve as a basis for an optimal GAN architecture that yields the optimal generator for a wide range of data settings.
Automatic Detection and Compression for Passive Acoustic Monitoring of the African Forest Elephant
Authors: Johan Bjorck, Brendan H. Rappazzo, Di Chen, Richard Bernstein, Peter H. Wrege, Carla P. Gomes
Date: 2019-02-25 00:00:00
Tasks: Audio Compression

In this work, we consider applying machine learning to the analysis and
compression of audio signals in the context of monitoring elephants in
sub-Saharan Africa. Earth's biodiversity is increasingly under threat by
sources of anthropogenic change (e.g. resource extraction, land use change, and
climate change) and surveying animal populations is critical for developing
conservation strategies. However, manually monitoring tropical forests or deep
oceans is intractable. For species that communicate acoustically, researchers
have argued for placing audio recorders in the habitats as a cost-effective and
non-invasive method, a strategy known as passive acoustic monitoring (PAM). In
collaboration with conservation efforts, we construct a large labeled dataset
of passive acoustic recordings of the African Forest Elephant via
crowdsourcing, compromising thousands of hours of recordings in the wild. Using
state-of-the-art techniques in artificial intelligence we improve upon
previously proposed methods for passive acoustic monitoring for classification
and segmentation. In real-time detection of elephant calls, network bandwidth
quickly becomes a bottleneck and efficient ways to compress the data are
needed. Most audio compression schemes are aimed at human listeners and are
unsuitable for low-frequency elephant calls. To remedy this, we provide a novel
end-to-end differentiable method for compression of audio signals that can be
adapted to acoustic monitoring of any species and dramatically improves over
naive coding strategies.
A Driving Intention Prediction Method Based on Hidden Markov Model for Autonomous Driving
Authors: Shiwen Liu, Kan Zheng, Long Zhao, Pingzhi Fan
Date: 2019-02-25 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Prediction

In a mixed-traffic scenario where both autonomous vehicles and human-driving
vehicles exist, a timely prediction of driving intentions of nearby
human-driving vehicles is essential for the safe and efficient driving of an
autonomous vehicle. In this paper, a driving intention prediction method based
on Hidden Markov Model (HMM) is proposed for autonomous vehicles. HMMs
representing different driving intentions are trained and tested with field
collected data from a flyover. When training the models, either discrete or
continuous characterization of the mobility features of vehicles is applied.
Experimental results show that the HMMs trained with the continuous
characterization of mobility features can give a higher prediction accuracy
when they are used for predicting driving intentions. Moreover, when the
surrounding traffic of the vehicle is taken into account, the performances of
the proposed prediction method are further improved.
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Authors: Amber L. Simpson, Michela Antonelli, Spyridon Bakas, Michel Bilello, Keyvan Farahani, Bram van Ginneken, Annette Kopp-Schneider, Bennett A. Landman, Geert Litjens, Bjoern Menze, Olaf Ronneberger, Ronald M. Summers, Patrick Bilic, Patrick F. Christ, Richard K. G. Do, Marc Gollub, Jennifer Golia-Pernicka, Stephan H. Heckers, William R. Jarnagin, Maureen K. McHugo, Sandy Napel, Eugene Vorontsov, Lena Maier-Hein, M. Jorge Cardoso
Date: 2019-02-25 00:00:00
Tasks: Benchmarking, Segmentation, Semantic Segmentation

Semantic segmentation of medical images aims to associate a pixel with a
label in a medical image without human initialization. The success of semantic
segmentation algorithms is contingent on the availability of high-quality
imaging data with corresponding labels provided by experts. We sought to create
a large collection of annotated medical image datasets of various clinically
relevant anatomies available under open source license to facilitate the
development of semantic segmentation algorithms. Such a resource would allow:
1) objective assessment of general-purpose segmentation methods through
comprehensive benchmarking and 2) open and free access to medical image data
for any researcher interested in the problem domain. Through a
multi-institutional effort, we generated a large, curated dataset
representative of several highly variable segmentation tasks that was used in a
crowd-sourced challenge - the Medical Segmentation Decathlon held during the
2018 Medical Image Computing and Computer Aided Interventions Conference in
Granada, Spain. Here, we describe these ten labeled image datasets so that
these data may be effectively reused by the research community.
Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence
Authors: Yi Han, David Hubczenko, Paul Montague, Olivier De Vel, Tamas Abraham, Benjamin I. P. Rubinstein, Christopher Leckie, Tansu Alpcan, Sarah Erfani
Date: 2019-02-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent studies have demonstrated that reinforcement learning (RL) agents are susceptible to adversarial manipulation, similar to vulnerabilities previously demonstrated in the supervised learning setting. While most existing work studies the problem in the context of computer vision or console games, this paper focuses on reinforcement learning in autonomous cyber defence under partial observability. We demonstrate that under the black-box setting, where the attacker has no direct access to the target RL model, causative attacks---attacks that target the training process---can poison RL agents even if the attacker only has partial observability of the environment. In addition, we propose an inversion defence method that aims to apply the opposite perturbation to that which an attacker might use to generate their adversarial samples. Our experimental results illustrate that the countermeasure can effectively reduce the impact of the causative attack, while not significantly affecting the training process in non-attack scenarios.
Vector operations for accelerating expensive Bayesian computations -- a tutorial guide
Authors: David J. Warne, Scott A. Sisson, Christopher Drovandi
Date: 2019-02-25 00:00:00
Tasks: Distributed Computing

Many applications in Bayesian statistics are extremely computationally intensive. However, they are often inherently parallel, making them prime targets for modern massively parallel processors. Multi-core and distributed computing is widely applied in the Bayesian community, however, very little attention has been given to fine-grain parallelisation using single instruction multiple data (SIMD) operations that are available on most modern commodity CPUs and is the basis of GPGPU computing. In this work, we practically demonstrate, using standard programming libraries, the utility of the SIMD approach for several topical Bayesian applications. We show that SIMD can improve the floating point arithmetic performance resulting in up to $6\times$ improvement in serial algorithm performance. Importantly, these improvements are multiplicative to any gains achieved through multi-core processing. We illustrate the potential of SIMD for accelerating Bayesian computations and provide the reader with techniques for exploiting modern massively parallel processing environments using standard tools.
Entity Personalized Talent Search Models with Tree Interaction Features
Authors: Cagri Ozcaglar, Sahin Geyik, Brian Schmitz, Prakhar Sharma, Alex Shelkovnykov, Yiming Ma, Erik Buchanan
Date: 2019-02-25 00:00:00
Tasks: Benchmarking

Talent Search systems aim to recommend potential candidates who are a good
match to the hiring needs of a recruiter expressed in terms of the recruiter's
search query or job posting. Past work in this domain has focused on linear and
nonlinear models which lack preference personalization in the user-level due to
being trained only with globally collected recruiter activity data. In this
paper, we propose an entity-personalized Talent Search model which utilizes a
combination of generalized linear mixed (GLMix) models and gradient boosted
decision tree (GBDT) models, and provides personalized talent recommendations
using nonlinear tree interaction features generated by the GBDT. We also
present the offline and online system architecture for the productionization of
this hybrid model approach in our Talent Search systems. Finally, we provide
offline and online experiment results benchmarking our entity-personalized
model with tree interaction features, which demonstrate significant
improvements in our precision metrics compared to globally trained
non-personalized models.
Adaptive Estimators Show Information Compression in Deep Neural Networks
Authors: Ivan Chelombiev, Conor Houghton, Cian O'Donnell
Date: 2019-02-24 00:00:00
Proceeding: ICLR 2019 5
Tasks: L2 Regularization, Mutual Information Estimation

To improve how neural networks function it is crucial to understand their learning process. The information bottleneck theory of deep learning proposes that neural networks achieve good generalization by compressing their representations to disregard information that is not relevant to the task. However, empirical evidence for this theory is conflicting, as compression was only observed when networks used saturating activation functions. In contrast, networks with non-saturating activation functions achieved comparable levels of task performance but did not show compression. In this paper we developed more robust mutual information estimation techniques, that adapt to hidden activity of neural networks and produce more sensitive measurements of activations from all functions, especially unbounded functions. Using these adaptive estimation techniques, we explored compression in networks with a range of different activation functions. With two improved methods of estimation, firstly, we show that saturation of the activation function is not required for compression, and the amount of compression varies between different activation functions. We also find that there is a large amount of variation in compression between different network initializations. Secondary, we see that L2 regularization leads to significantly increased compression, while preventing overfitting. Finally, we show that only compression of the last layer is positively correlated with generalization.
Rapidly Adapting Moment Estimation
Authors: Guo-Qiang Zhang, Kenta Niwa, W. Bastiaan Kleijn
Date: 2019-02-24 00:00:00

Adaptive gradient methods such as Adam have been shown to be very effective
for training deep neural networks (DNNs) by tracking the second moment of
gradients to compute the individual learning rates. Differently from existing
methods, we make use of the most recent first moment of gradients to compute
the individual learning rates per iteration. The motivation behind it is that
the dynamic variation of the first moment of gradients may provide useful
information to obtain the learning rates. We refer to the new method as the
rapidly adapting moment estimation (RAME). The theoretical convergence of
deterministic RAME is studied by using an analysis similar to the one used in
[1] for Adam. Experimental results for training a number of DNNs show promising
performance of RAME w.r.t. the convergence speed and generalization performance
compared to the stochastic heavy-ball (SHB) method, Adam, and RMSprop.