pwc-archive/papers-with-abstracts huggingface dataset:
contextual: Evaluating Contextual Multi-Armed Bandit Problems in R
Authors: Robin van Emden, Maurits Kaptein
Date: 2018-11-06 00:00:00
Tasks: Object

Over the past decade, contextual bandit algorithms have been gaining in popularity due to their effectiveness and flexibility in solving sequential decision problems---from online advertising and finance to clinical trial design and personalized medicine. At the same time, there are, as of yet, surprisingly few options that enable researchers and practitioners to simulate and compare the wealth of new and existing bandit algorithms in a standardized way. To help close this gap between analytical research and empirical evaluation the current paper introduces the object-oriented R package "contextual": a user-friendly and, through its object-oriented structure, easily extensible framework that facilitates parallelized comparison of contextual and context-free bandit policies through both simulation and offline analysis.
An Optimal Itinerary Generation in a Configuration Space of Large Intellectual Agent Groups with Linear Logic
Authors: Dmitry Maximov
Date: 2018-11-06 00:00:00

A group of intelligent agents which fulfill a set of tasks in parallel is
represented first by the tensor multiplication of corresponding processes in a
linear logic game category. An optimal itinerary in the configuration space of
the group states is defined as a play with maximal total reward in the
category. New moments also are: the reward is represented as a degree of
certainty (visibility) of an agent goal, and the system goals are chosen by the
greatest value corresponding to these processes in the system goal lattice.
Day-ahead time series forecasting: application to capacity planning
Authors: Colin Leverger, Vincent Lemaire, Simon Malinowski, Thomas Guyet, Laurence Rozé
Date: 2018-11-06 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis, Time Series Forecasting

In the context of capacity planning, forecasting the evolution of informatics
servers usage enables companies to better manage their computational resources.
We address this problem by collecting key indicator time series and propose to
forecast their evolution a day-ahead. Our method assumes that data is
structured by a daily seasonality, but also that there is typical evolution of
indicators within a day. Then, it uses the combination of a clustering
algorithm and Markov Models to produce day-ahead forecasts. Our experiments on
real datasets show that the data satisfies our assumption and that, in the case
study, our method outperforms classical approaches (AR, Holt-Winters).
Hybrid Approach to Automation, RPA and Machine Learning: a Method for the Human-centered Design of Software Robots
Authors: Wiesław Kopeć, Marcin Skibiński, Cezary Biele, Kinga Skorupska, Dominika Tkaczyk, Anna Jaskulska, Katarzyna Abramczuk, Piotr Gago, Krzysztof Marasek
Date: 2018-11-06 00:00:00

One of the more prominent trends within Industry 4.0 is the drive to employ
Robotic Process Automation (RPA), especially as one of the elements of the Lean
approach. The full implementation of RPA is riddled with challenges relating
both to the reality of everyday business operations, from SMEs to SSCs and
beyond, and the social effects of the changing job market. To successfully
address these points there is a need to develop a solution that would adjust to
the existing business operations and at the same time lower the negative social
impact of the automation process.
  To achieve these goals we propose a hybrid, human-centered approach to the
development of software robots. This design and implementation method combines
the Living Lab approach with empowerment through participatory design to
kick-start the co-development and co-maintenance of hybrid software robots
which, supported by variety of AI methods and tools, including interactive and
collaborative ML in the cloud, transform menial job posts into higher-skilled
positions, allowing former employees to stay on as robot co-designers and
maintainers, i.e. as co-programmers who supervise the machine learning
processes with the use of tailored high-level RPA Domain Specific Languages
(DSLs) to adjust the functioning of the robots and maintain operational
flexibility.
DSNet: Deep and Shallow Feature Learning for Efficient Visual Tracking
Authors: Qiangqiang Wu, Yan Yan, Yanjie Liang, Yi Liu, Hanzi Wang
Date: 2018-11-06 00:00:00
Tasks: image-classification, Image Classification, Visual Tracking

In recent years, Discriminative Correlation Filter (DCF) based tracking
methods have achieved great success in visual tracking. However, the
multi-resolution convolutional feature maps trained from other tasks like image
classification, cannot be naturally used in the conventional DCF formulation.
Furthermore, these high-dimensional feature maps significantly increase the
tracking complexity and thus limit the tracking speed. In this paper, we
present a deep and shallow feature learning network, namely DSNet, to learn the
multi-level same-resolution compressed (MSC) features for efficient online
tracking, in an end-to-end offline manner. Specifically, the proposed DSNet
compresses multi-level convolutional features to uniform spatial resolution
features. The learned MSC features effectively encode both appearance and
semantic information of objects in the same-resolution feature maps, thus
enabling an elegant combination of the MSC features with any DCF-based methods.
Additionally, a channel reliability measurement (CRM) method is presented to
further refine the learned MSC features. We demonstrate the effectiveness of
the MSC features learned from the proposed DSNet on two DCF tracking
frameworks: the basic DCF framework and the continuous convolution operator
framework. Extensive experiments show that the learned MSC features have the
appealing advantage of allowing the equipped DCF-based tracking methods to
perform favorably against the state-of-the-art methods while running at high
frame rates.
Collaborative Filtering with Stability
Authors: Dongsheng Li, Chao Chen, Qin Lv, Junchi Yan, Li Shang, Stephen M. Chu
Date: 2018-11-06 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Collaborative filtering (CF) is a popular technique in today's recommender
systems, and matrix approximation-based CF methods have achieved great success
in both rating prediction and top-N recommendation tasks. However, real-world
user-item rating matrices are typically sparse, incomplete and noisy, which
introduce challenges to the algorithm stability of matrix approximation, i.e.,
small changes in the training data may significantly change the models. As a
result, existing matrix approximation solutions yield low generalization
performance, exhibiting high error variance on the training data, and
minimizing the training error may not guarantee error reduction on the test
data. This paper investigates the algorithm stability problem of matrix
approximation methods and how to achieve stable collaborative filtering via
stable matrix approximation. We present a new algorithm design framework, which
(1) introduces new optimization objectives to guide stable matrix approximation
algorithm design, and (2) solves the optimization problem to obtain stable
approximation solutions with good generalization performance. Experimental
results on real-world datasets demonstrate that the proposed method can achieve
better accuracy compared with state-of-the-art matrix approximation methods and
ensemble methods in both rating prediction and top-N recommendation tasks.
Credit Card Fraud Detection in e-Commerce: An Outlier Detection Approach
Authors: Utkarsh Porwal, Smruthi Mukund
Date: 2018-11-06 00:00:00
Tasks: Clustering, Fraud Detection, Outlier Detection, Spam detection

Often the challenge associated with tasks like fraud and spam detection is the lack of all likely patterns needed to train suitable supervised learning models. This problem accentuates when the fraudulent patterns are not only scarce, they also change over time. Change in fraudulent pattern is because fraudsters continue to innovate novel ways to circumvent measures put in place to prevent fraud. Limited data and continuously changing patterns makes learning significantly difficult. We hypothesize that good behavior does not change with time and data points representing good behavior have consistent spatial signature under different groupings. Based on this hypothesis we are proposing an approach that detects outliers in large data sets by assigning a consistency score to each data point using an ensemble of clustering methods. Our main contribution is proposing a novel method that can detect outliers in large datasets and is robust to changing patterns. We also argue that area under the ROC curve, although a commonly used metric to evaluate outlier detection methods is not the right metric. Since outlier detection problems have a skewed distribution of classes, precision-recall curves are better suited because precision compares false positives to true positives (outliers) rather than true negatives (inliers) and therefore is not affected by the problem of class imbalance. We show empirically that area under the precision-recall curve is a better than ROC as an evaluation metric. The proposed approach is tested on the modified version of the Landsat satellite dataset, the modified version of the ann-thyroid dataset and a large real world credit card fraud detection dataset available through Kaggle where we show significant improvement over the baseline methods.
In-the-wild Facial Expression Recognition in Extreme Poses
Authors: Fei Yang, Qian Zhang, Chi Zheng, Guoping Qiu
Date: 2018-11-06 00:00:00
Tasks: Deep Learning, Facial Expression Recognition, Facial Expression Recognition (FER)

In the computer research area, facial expression recognition is a hot
research problem. Recent years, the research has moved from the lab environment
to in-the-wild circumstances. It is challenging, especially under extreme
poses. But current expression detection systems are trying to avoid the pose
effects and gain the general applicable ability. In this work, we solve the
problem in the opposite approach. We consider the head poses and detect the
expressions within special head poses. Our work includes two parts: detect the
head pose and group it into one pre-defined head pose class; do facial
expression recognize within each pose class. Our experiments show that the
recognition results with pose class grouping are much better than that of
direct recognition without considering poses. We combine the hand-crafted
features, SIFT, LBP and geometric feature, with deep learning feature as the
representation of the expressions. The handcrafted features are added into the
deep learning framework along with the high level deep learning features. As a
comparison, we implement SVM and random forest to as the prediction models. To
train and test our methodology, we labeled the face dataset with 6 basic
expressions.
3DCapsule: Extending the Capsule Architecture to Classify 3D Point Clouds
Authors: Ali Cheraghian, Lars Petersson
Date: 2018-11-06 00:00:00
Tasks: Classification, Classify 3D Point Clouds, General Classification

This paper introduces the 3DCapsule, which is a 3D extension of the recently
introduced Capsule concept that makes it applicable to unordered point sets.
The original Capsule relies on the existence of a spatial relationship between
the elements in the feature map it is presented with, whereas in point
permutation invariant formulations of 3D point set classification methods, such
relationships are typically lost. Here, a new layer called ComposeCaps is
introduced that, in lieu of a spatially relevant feature mapping, learns a new
mapping that can be exploited by the 3DCapsule. Previous works in the 3D point
set classification domain have focused on other parts of the architecture,
whereas instead, the 3DCapsule is a drop-in replacement of the commonly used
fully connected classifier. It is demonstrated via an ablation study, that when
the 3DCapsule is applied to recent 3D point set classification architectures,
it consistently shows an improvement, in particular when subjected to noisy
data. Similarly, the ComposeCaps layer is evaluated and demonstrates an
improvement over the baseline. In an apples-to-apples comparison against
state-of-the-art methods, again, better performance is demonstrated by the
3DCapsule.
BLP -- Boundary Likelihood Pinpointing Networks for Accurate Temporal Action Localization
Authors: Weijie Kong, Nannan Li, Shan Liu, Thomas Li, Ge Li
Date: 2018-11-06 00:00:00
Tasks: Action Detection, Action Localization, regression, Temporal Action Localization

Despite tremendous progress achieved in temporal action detection, state-of-the-art methods still suffer from the sharp performance deterioration when localizing the starting and ending temporal action boundaries. Although most methods apply boundary regression paradigm to tackle this problem, we argue that the direct regression lacks detailed enough information to yield accurate temporal boundaries. In this paper, we propose a novel Boundary Likelihood Pinpointing (BLP) network to alleviate this deficiency of boundary regression and improve the localization accuracy. Given a loosely localized search interval that contains an action instance, BLP casts the problem of localizing temporal boundaries as that of assigning probabilities on each equally divided unit of this interval. These generated probabilities provide useful information regarding the boundary location of the action inside this search interval. Based on these probabilities, we introduce a boundary pinpointing paradigm to pinpoint the accurate boundaries under a simple probabilistic framework. Compared with other C3D feature based detectors, extensive experiments demonstrate that BLP significantly improves the localization performance of recent state-of-the-art detectors, and achieves competitive detection mAP on both THUMOS' 14 and ActivityNet datasets, particularly when the evaluation tIoU is high.
Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning
Authors: Ritchie Lee, Ole J. Mengshoel, Anshu Saksena, Ryan Gardner, Daniel Genin, Joshua Silbermann, Michael Owen, Mykel J. Kochenderfer
Date: 2018-11-06 00:00:00
Tasks: Autonomous Driving, Collision Avoidance, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collision avoidance systems and autonomous cars. In many applications such as autonomous driving, failures cannot be completely eliminated due to the complex stochastic environment in which the system operates. As a result, safety validation is not only concerned about whether a failure can occur, but also discovering which failures are most likely to occur. This article presents adaptive stress testing (AST), a framework for finding the most likely path to a failure event in simulation. We consider a general black box setting for partially observable and continuous-valued systems operating in an environment with stochastic disturbances. We formulate the problem as a Markov decision process and use reinforcement learning to optimize it. The approach is simulation-based and does not require internal knowledge of the system, making it suitable for black-box testing of large systems. We present formulations for fully observable and partially observable systems. In the latter case, we present a modified Monte Carlo tree search algorithm that only requires access to the pseudorandom number generator of the simulator to overcome partial observability. We also present an extension of the framework, called differential adaptive stress testing (DAST), that can find failures that occur in one system but not in another. This type of differential analysis is useful in applications such as regression testing, where we are concerned with finding areas of relative weakness compared to a baseline. We demonstrate the effectiveness of the approach on an aircraft collision avoidance application, where a prototype aircraft collision avoidance system is stress tested to find the most likely scenarios of near mid-air collision.
Neural Network-Hardware Co-design for Scalable RRAM-based BNN Accelerators
Authors: Yulhwa Kim, HyungJun Kim, Jae-Joon Kim
Date: 2018-11-06 00:00:00
Tasks: Neural Network simulation

Recently, RRAM-based Binary Neural Network (BNN) hardware has been gaining
interests as it requires 1-bit sense-amp only and eliminates the need for
high-resolution ADC and DAC. However, RRAM-based BNN hardware still requires
high-resolution ADC for partial sum calculation to implement large-scale neural
network using multiple memory arrays. We propose a neural network-hardware
co-design approach to split input to fit each split network on a RRAM array so
that the reconstructed BNNs calculate 1-bit output neuron in each array. As a
result, ADC can be completely eliminated from the design even for large-scale
neural network. Simulation results show that the proposed network
reconstruction and retraining recovers the inference accuracy of the original
BNN. The accuracy loss of the proposed scheme in the CIFAR-10 testcase was less
than 1.1% compared to the original network. The code for training and running
proposed BNN models is available at:
https://github.com/YulhwaKim/RRAMScalable_BNN.
Dynamic Regret Convergence Analysis and an Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning
Authors: Jonathan N. Lee, Michael Laskey, Ajay Kumar Tanwani, Anil Aswani, Ken Goldberg
Date: 2018-11-06 00:00:00
Tasks: Imitation Learning

On-policy imitation learning algorithms such as DAgger evolve a robot control policy by executing it, measuring performance (loss), obtaining corrective feedback from a supervisor, and generating the next policy. As the loss between iterations can vary unpredictably, a fundamental question is under what conditions this process will eventually achieve a converged policy. If one assumes the underlying trajectory distribution is static (stationary), it is possible to prove convergence for DAgger. However, in more realistic models for robotics, the underlying trajectory distribution is dynamic because it is a function of the policy. Recent results show it is possible to prove convergence of DAgger when a regularity condition on the rate of change of the trajectory distributions is satisfied. In this article, we reframe this result using dynamic regret theory from the field of online optimization and show that dynamic regret can be applied to any on-policy algorithm to analyze its convergence and optimality. These results inspire a new algorithm, Adaptive On-Policy Regularization (AOR), that ensures the conditions for convergence. We present simulation results with cart-pole balancing and locomotion benchmarks that suggest AOR can significantly decrease dynamic regret and chattering as the robot learns. To our knowledge, this the first application of dynamic regret theory to imitation learning.
Unpaired Speech Enhancement by Acoustic and Adversarial Supervision for Speech Recognition
Authors: Geonmin Kim, Hwaran Lee, Bo-Kyeong Kim, Sang-Hoon Oh, Soo-Young Lee
Date: 2018-11-06 00:00:00
Tasks: Generative Adversarial Network, Speech Enhancement, speech-recognition, Speech Recognition

Many speech enhancement methods try to learn the relationship between noisy
and clean speech, obtained using an acoustic room simulator. We point out
several limitations of enhancement methods relying on clean speech targets; the
goal of this work is proposing an alternative learning algorithm, called
acoustic and adversarial supervision (AAS). AAS makes the enhanced output both
maximizing the likelihood of transcription on the pre-trained acoustic model
and having general characteristics of clean speech, which improve
generalization on unseen noisy speeches. We employ the connectionist temporal
classification and the unpaired conditional boundary equilibrium generative
adversarial network as the loss function of AAS. AAS is tested on two datasets
including additive noise without and with reverberation, Librispeech + DEMAND
and CHiME-4. By visualizing the enhanced speech with different loss
combinations, we demonstrate the role of each supervision. AAS achieves a lower
word error rate than other state-of-the-art methods using the clean speech
target in both datasets.
Fast OBDD Reordering using Neural Message Passing on Hypergraph
Authors: Feifan Xu, Fei He, Enze Xie, Liang Li
Date: 2018-11-06 00:00:00

Ordered binary decision diagrams (OBDDs) are an efficient data structure for
representing and manipulating Boolean formulas. With respect to different
variable orders, the OBDDs' sizes may vary from linear to exponential in the
number of the Boolean variables. Finding the optimal variable order has been
proved a NP-complete problem. Many heuristics have been proposed to find a
near-optimal solution of this problem. In this paper, we propose a neural
network-based method to predict near-optimal variable orders for unknown
formulas. Viewing these formulas as hypergraphs, and lifting the message
passing neural network into 3-hypergraph (MPNN3), we are able to learn the
patterns of Boolean formula. Compared to the traditional methods, our method
can find a near-the-best solution with an extremely shorter time, even for some
hard examples.To the best of our knowledge, this is the first work on applying
neural network to OBDD reordering.
Neural Phrase-to-Phrase Machine Translation
Authors: Jiangtao Feng, Lingpeng Kong, Po-Sen Huang, Chong Wang, Da Huang, Jiayuan Mao, Kan Qiao, Dengyong Zhou
Date: 2018-11-06 00:00:00
Tasks: Decoder, Machine Translation, Translation

In this paper, we propose Neural Phrase-to-Phrase Machine Translation
(NP$^2$MT). Our model uses a phrase attention mechanism to discover relevant
input (source) segments that are used by a decoder to generate output (target)
phrases. We also design an efficient dynamic programming algorithm to decode
segments that allows the model to be trained faster than the existing neural
phrase-based machine translation method by Huang et al. (2018). Furthermore,
our method can naturally integrate with external phrase dictionaries during
decoding. Empirical experiments show that our method achieves comparable
performance with the state-of-the art methods on benchmark datasets. However,
when the training and testing data are from different distributions or domains,
our method performs better.
DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction
Authors: Shun Zheng, Xu Han, Yankai Lin, Peilin Yu, Lu Chen, Ling Huang, Zhiyuan Liu, Wei Xu
Date: 2018-11-06 00:00:00
Proceeding: ACL 2019 7
Tasks: Relation, Relation Extraction

Pattern-based labeling methods have achieved promising results in alleviating the inevitable labeling noises of distantly supervised neural relation extraction. However, these methods require significant expert labor to write relation-specific patterns, which makes them too sophisticated to generalize quickly.To ease the labor-intensive workload of pattern writing and enable the quick generalization to new relation types, we propose a neural pattern diagnosis framework, DIAG-NRE, that can automatically summarize and refine high-quality relational patterns from noise data with human experts in the loop. To demonstrate the effectiveness of DIAG-NRE, we apply it to two real-world datasets and present both significant and interpretable improvements over state-of-the-art methods.