pwc-archive/papers-with-abstracts huggingface dataset:
Machine-Translation History and Evolution: Survey for Arabic-English Translations
Authors: Nabeel T. Alsohybe, Neama Abdulaziz Dahan, Fadl Mutaher Ba-Alwi
Date: 2017-09-14 00:00:00
Tasks: Machine Translation, Survey, Translation

As a result of the rapid changes in information and communication technology
(ICT), the world has become a small village where people from all over the
world connect with each other in dialogue and communication via the Internet.
Also, communications have become a daily routine activity due to the new
globalization where companies and even universities become global residing
cross countries borders. As a result, translation becomes a needed activity in
this connected world. ICT made it possible to have a student in one country
take a course or even a degree from a different country anytime anywhere
easily. The resulted communication still needs a language as a means that helps
the receiver understands the contents of the sent message. People need an
automated translation application because human translators are hard to find
all the times, and the human translations are very expensive comparing to the
translations automated process. Several types of research describe the
electronic process of the Machine-Translation. In this paper, the authors are
going to study some of these previous researches, and they will explore some of
the needed tools for the Machine-Translation. This research is going to
contribute to the Machine-Translation area by helping future researchers to
have a summary for the Machine-Translation groups of research and to let lights
on the importance of the translation mechanism.
Towards an Arabic-English Machine-Translation Based on Semantic Web
Authors: Neama Abdulaziz Dahan, Fadl Mutaher Ba-Alwi, Ibrahim Ahmed Al-Baltah, Ghaleb H. Al-gapheri
Date: 2017-09-14 00:00:00
Tasks: Machine Translation, Translation

Communication tools make the world like a small village and as a consequence
people can contact with others who are from different societies or who speak
different languages. This communication cannot happen effectively without
Machine Translation because they can be found anytime and everywhere. There are
a number of studies that have developed Machine Translation for the English
language with so many other languages except the Arabic it has not been
considered yet. Therefore we aim to highlight a roadmap for our proposed
translation machine to provide an enhanced Arabic English translation based on
Semantic.
Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification
Authors: Bikash Joshi, Massih-Reza Amini, Ioannis Partalas, Franck Iutzeler, Yury Maximov
Date: 2017-01-23 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Binary Classification, Classification, General Classification, Multi-class Classification, text-classification, Text Classification

We address the problem of multi-class classification in the case where the
number of classes is very large. We propose a double sampling strategy on top
of a multi-class to binary reduction strategy, which transforms the original
multi-class problem into a binary classification problem over pairs of
examples. The aim of the sampling strategy is to overcome the curse of
long-tailed class distributions exhibited in majority of large-scale
multi-class classification problems and to reduce the number of pairs of
examples in the expanded data. We show that this strategy does not alter the
consistency of the empirical risk minimization principle defined over the
double sample reduction. Experiments are carried out on DMOZ and Wikipedia
collections with 10,000 to 100,000 classes where we show the efficiency of the
proposed approach in terms of training and prediction time, memory consumption,
and predictive performance with respect to state-of-the-art approaches.
Skeleton-aided Articulated Motion Generation
Authors: Yichao Yan, Jingwei Xu, Bingbing Ni, Xiaokang Yang
Date: 2017-07-04 00:00:00
Tasks: Gesture-to-Gesture Translation, Motion Generation, Triplet, Video Generation

This work make the first attempt to generate articulated human motion
sequence from a single image. On the one hand, we utilize paired inputs
including human skeleton information as motion embedding and a single human
image as appearance reference, to generate novel motion frames, based on the
conditional GAN infrastructure. On the other hand, a triplet loss is employed
to pursue appearance-smoothness between consecutive frames. As the proposed
framework is capable of jointly exploiting the image appearance space and
articulated/kinematic motion space, it generates realistic articulated motion
sequence, in contrast to most previous video generation methods which yield
blurred motion effects. We test our model on two human action datasets
including KTH and Human3.6M, and the proposed framework generates very
promising results on both datasets.
Detection of Unauthorized IoT Devices Using Machine Learning Techniques
Authors: Yair Meidan, Michael Bohadana, Asaf Shabtai, Martin Ochoa, Nils Ole Tippenhauer, Juan Davis Guarnizo, Yuval Elovici
Date: 2017-09-14 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Security experts have demonstrated numerous risks imposed by Internet of
Things (IoT) devices on organizations. Due to the widespread adoption of such
devices, their diversity, standardization obstacles, and inherent mobility,
organizations require an intelligent mechanism capable of automatically
detecting suspicious IoT devices connected to their networks. In particular,
devices not included in a white list of trustworthy IoT device types (allowed
to be used within the organizational premises) should be detected. In this
research, Random Forest, a supervised machine learning algorithm, was applied
to features extracted from network traffic data with the aim of accurately
identifying IoT device types from the white list. To train and evaluate
multi-class classifiers, we collected and manually labeled network traffic data
from 17 distinct IoT devices, representing nine types of IoT devices. Based on
the classification of 20 consecutive sessions and the use of majority rule, IoT
device types that are not on the white list were correctly detected as unknown
in 96% of test cases (on average), and white listed device types were correctly
classified by their actual types in 99% of cases. Some IoT device types were
identified quicker than others (e.g., sockets and thermostats were successfully
detected within five TCP sessions of connecting to the network). Perfect
detection of unauthorized IoT device types was achieved upon analyzing 110
consecutive sessions; perfect classification of white listed types required 346
consecutive sessions, 110 of which resulted in 99.49% accuracy. Further
experiments demonstrated the successful applicability of classifiers trained in
one location and tested on another. In addition, a discussion is provided
regarding the resilience of our machine learning-based IoT white listing method
to adversarial attacks.
From Node Embedding To Community Embedding
Authors: Vincent W. Zheng, Sandro Cavallari, Hongyun Cai, Kevin Chen-Chuan Chang, Erik Cambria
Date: 2016-10-31 00:00:00
Tasks: Graph Embedding, Node Classification

Most of the existing graph embedding methods focus on nodes, which aim to
output a vector representation for each node in the graph such that two nodes
being "close" on the graph are close too in the low-dimensional space. Despite
the success of embedding individual nodes for graph analytics, we notice that
an important concept of embedding communities (i.e., groups of nodes) is
missing. Embedding communities is useful, not only for supporting various
community-level applications, but also to help preserve community structure in
graph embedding. In fact, we see community embedding as providing a
higher-order proximity to define the node closeness, whereas most of the
popular graph embedding methods focus on first-order and/or second-order
proximities. To learn the community embedding, we hinge upon the insight that
community embedding and node embedding reinforce with each other. As a result,
we propose ComEmbed, the first community embedding method, which jointly
optimizes the community embedding and node embedding together. We evaluate
ComEmbed on real-world data sets. We show it outperforms the state-of-the-art
baselines in both tasks of node classification and community prediction.
Learning to Segment Instances in Videos with Spatial Propagation Network
Authors: Jingchun Cheng, Sifei Liu, Yi-Hsuan Tsai, Wei-Chih Hung, Shalini De Mello, Jinwei Gu, Jan Kautz, Shengjin Wang, Ming-Hsuan Yang
Date: 2017-09-14 00:00:00
Tasks: Object, Segmentation, Semantic Segmentation

We propose a deep learning-based framework for instance-level object
segmentation. Our method mainly consists of three steps. First, We train a
generic model based on ResNet-101 for foreground/background segmentations.
Second, based on this generic model, we fine-tune it to learn instance-level
models and segment individual objects by using augmented object annotations in
first frames of test videos. To distinguish different instances in the same
video, we compute a pixel-level score map for each object from these
instance-level models. Each score map indicates the objectness likelihood and
is only computed within the foreground mask obtained in the first step. To
further refine this per frame score map, we learn a spatial propagation
network. This network aims to learn how to propagate a coarse segmentation mask
spatially based on the pairwise similarities in each frame. In addition, we
apply a filter on the refined score map that aims to recognize the best
connected region using spatial and temporal consistencies in the video.
Finally, we decide the instance-level object segmentation in each video by
comparing score maps of different instances.
SAM: Semantic Attribute Modulation for Language Modeling and Style Variation
Authors: Wenbo Hu, Lifeng Hua, Lei LI, Hang Su, Tian Wang, Ning Chen, Bo Zhang
Date: 2017-07-01 00:00:00
Tasks: Attribute, Language Modeling, Language Modelling

This paper presents a Semantic Attribute Modulation (SAM) for language
modeling and style variation. The semantic attribute modulation includes
various document attributes, such as titles, authors, and document categories.
We consider two types of attributes, (title attributes and category
attributes), and a flexible attribute selection scheme by automatically scoring
them via an attribute attention mechanism. The semantic attributes are embedded
into the hidden semantic space as the generation inputs. With the attributes
properly harnessed, our proposed SAM can generate interpretable texts with
regard to the input attributes. Qualitative analysis, including word semantic
analysis and attention values, shows the interpretability of SAM. On several
typical text datasets, we empirically demonstrate the superiority of the
Semantic Attribute Modulated language model with different combinations of
document attributes. Moreover, we present a style variation for the lyric
generation using SAM, which shows a strong connection between the style
variation and the semantic attributes.
A Framework for Generalizing Graph-based Representation Learning Methods
Authors: Nesreen K. Ahmed, Ryan A. Rossi, Rong Zhou, John Boaz Lee, Xiangnan Kong, Theodore L. Willke, Hoda Eldardiry
Date: 2017-09-14 00:00:00
Tasks: Inductive Learning, Representation Learning

Random walks are at the heart of many existing deep learning algorithms for
graph data. However, such algorithms have many limitations that arise from the
use of random walks, e.g., the features resulting from these methods are unable
to transfer to new nodes and graphs as they are tied to node identity. In this
work, we introduce the notion of attributed random walks which serves as a
basis for generalizing existing methods such as DeepWalk, node2vec, and many
others that leverage random walks. Our proposed framework enables these methods
to be more widely applicable for both transductive and inductive learning as
well as for use on graphs with attributes (if available). This is achieved by
learning functions that generalize to new nodes and graphs. We show that our
proposed framework is effective with an average AUC improvement of 16.1% while
requiring on average 853 times less space than existing methods on a variety of
graphs from several domains.
Guiding Reinforcement Learning Exploration Using Natural Language
Authors: Brent Harrison, Upol Ehsan, Mark O. Riedl
Date: 2017-07-26 00:00:00
Tasks: Decoder, Machine Translation, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Translation

In this work we present a technique to use natural language to help
reinforcement learning generalize to unseen environments. This technique uses
neural machine translation, specifically the use of encoder-decoder networks,
to learn associations between natural language behavior descriptions and
state-action information. We then use this learned model to guide agent
exploration using a modified version of policy shaping to make it more
effective at learning in unseen environments. We evaluate this technique using
the popular arcade game, Frogger, under ideal and non-ideal conditions. This
evaluation shows that our modified policy shaping algorithm improves over a
Q-learning agent as well as a baseline version of policy shaping.
Catalyst design using actively learned machine with non-ab initio input features towards CO2 reduction reactions
Authors: Juhwan Noh, JaeHoon Kim, Seoin Back, Yousung Jung
Date: 2017-09-14 00:00:00
Tasks: Active Learning

In conventional chemisorption model, the d-band center theory (augmented
sometimes with the upper edge of d-band for imporved accuarcy) plays a central
role in predicting adsorption energies and catalytic activity as a function of
d-band center of the solid surfaces, but it requires density functional
calculations that can be quite costly for large scale screening purposes of
materials. In this work, we propose to use the d-band width of the muffin-tin
orbital theory (to account for local coordination environment) plus
electronegativity (to account for adsorbate renormalization) as a simple set of
alternative descriptors for chemisorption, which do not demand the ab initio
calculations. This pair of descriptors are then combined with machine learning
methods, namely, artificial neural network (ANN) and kernel ridge regression
(KRR), to allow large scale materials screenings. We show, for a toy set of 263
alloy systems, that the CO adsorption energy can be predicted with a remarkably
small mean absolute deviation error of 0.05 eV, a significantly improved result
as compared to 0.13 eV obtained with descriptors including costly d-band center
calculations in literature. We achieved this high accuracy by utilizing an
active learning algorithm, without which the accuracy was 0.18 eV otherwise. As
a practical application of this machine, we identified Cu3Y@Cu as a highly
active and cost-effective electrochemical CO2 reduction catalyst to produce CO
with the overpotential 0.37 V lower than Au catalyst.
Towards personalized human AI interaction - adapting the behavior of AI agents using neural signatures of subjective interest
Authors: Victor Shih, David C Jangraw, Paul Sajda, Sameer Saproo
Date: 2017-09-14 00:00:00
Tasks: AI Agent, Brain Computer Interface, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement Learning AI commonly uses reward/penalty signals that are
objective and explicit in an environment -- e.g. game score, completion time,
etc. -- in order to learn the optimal strategy for task performance. However,
Human-AI interaction for such AI agents should include additional reinforcement
that is implicit and subjective -- e.g. human preferences for certain AI
behavior -- in order to adapt the AI behavior to idiosyncratic human
preferences. Such adaptations would mirror naturally occurring processes that
increase trust and comfort during social interactions. Here, we show how a
hybrid brain-computer-interface (hBCI), which detects an individual's level of
interest in objects/events in a virtual environment, can be used to adapt the
behavior of a Deep Reinforcement Learning AI agent that is controlling a
virtual autonomous vehicle. Specifically, we show that the AI learns a driving
strategy that maintains a safe distance from a lead vehicle, and most novelly,
preferentially slows the vehicle when the human passengers of the vehicle
encounter objects of interest. This adaptation affords an additional 20\%
viewing time for subjectively interesting objects. This is the first
demonstration of how an hBCI can be used to provide implicit reinforcement to
an AI agent in a way that incorporates user preferences into the control
system.
When Waiting is not an Option : Learning Options with a Deliberation Cost
Authors: Jean Harb, Pierre-Luc Bacon, Martin Klissarov, Doina Precup
Date: 2017-09-14 00:00:00
Tasks: Atari Games

Recent work has shown that temporally extended actions (options) can be
learned fully end-to-end as opposed to being specified in advance. While the
problem of "how" to learn options is increasingly well understood, the question
of "what" good options should be has remained elusive. We formulate our answer
to what "good" options should be in the bounded rationality framework (Simon,
1957) through the notion of deliberation cost. We then derive practical
gradient-based learning algorithms to implement this objective. Our results in
the Arcade Learning Environment (ALE) show increased performance and
interpretability.
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach
Authors: Yi Ouyang, Mukul Gagrani, Ashutosh Nayyar, Rahul Jain
Date: 2017-09-14 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Reinforcement Learning, Thompson Sampling

We consider the problem of learning an unknown Markov Decision Process (MDP)
that is weakly communicating in the infinite horizon setting. We propose a
Thompson Sampling-based reinforcement learning algorithm with dynamic episodes
(TSDE). At the beginning of each episode, the algorithm generates a sample from
the posterior distribution over the unknown model parameters. It then follows
the optimal stationary policy for the sampled model for the rest of the
episode. The duration of each episode is dynamically determined by two stopping
criteria. The first stopping criterion controls the growth rate of episode
length. The second stopping criterion happens when the number of visits to any
state-action pair is doubled. We establish $\tilde O(HS\sqrt{AT})$ bounds on
expected regret under a Bayesian setting, where $S$ and $A$ are the sizes of
the state and action spaces, $T$ is time, and $H$ is the bound of the span.
This regret bound matches the best available bound for weakly communicating
MDPs. Numerical results show it to perform better than existing algorithms for
infinite horizon MDPs.
Variance-Reduced and Projection-Free Stochastic Optimization
Authors: Elad Hazan, Haipeng Luo
Date: 2016-02-05 00:00:00
Tasks: Stochastic Optimization

The Frank-Wolfe optimization algorithm has recently regained popularity for
machine learning applications due to its projection-free property and its
ability to handle structured constraints. However, in the stochastic learning
setting, it is still relatively understudied compared to the gradient descent
counterpart. In this work, leveraging a recent variance reduction technique, we
propose two stochastic Frank-Wolfe variants which substantially improve
previous results in terms of the number of stochastic gradient evaluations
needed to achieve $1-\epsilon$ accuracy. For example, we improve from
$O(\frac{1}{\epsilon})$ to $O(\ln\frac{1}{\epsilon})$ if the objective function
is smooth and strongly convex, and from $O(\frac{1}{\epsilon^2})$ to
$O(\frac{1}{\epsilon^{1.5}})$ if the objective function is smooth and
Lipschitz. The theoretical improvement is also observed in experiments on
real-world datasets for a multiclass classification application.
Large Batch Training of Convolutional Networks
Authors: Yang You, Igor Gitman, Boris Ginsburg
Date: 2017-08-13 00:00:00
Tasks: 8k

A common way to speed up training of large convolutional networks is to add
computational units. Training is then performed using data-parallel synchronous
Stochastic Gradient Descent (SGD) with mini-batch divided between computational
units. With an increase in the number of nodes, the batch size grows. But
training with large batch size often results in the lower model accuracy. We
argue that the current recipe for large batch training (linear learning rate
scaling with warm-up) is not general enough and training may diverge. To
overcome this optimization difficulties we propose a new training algorithm
based on Layer-wise Adaptive Rate Scaling (LARS). Using LARS, we scaled Alexnet
up to a batch size of 8K, and Resnet-50 to a batch size of 32K without loss in
accuracy.
MOLTE: a Modular Optimal Learning Testing Environment
Authors: Yingfei Wang, Warren Powell
Date: 2017-09-13 00:00:00
Tasks: Experimental Design

We address the relative paucity of empirical testing of learning algorithms
(of any type) by introducing a new public-domain, Modular, Optimal Learning
Testing Environment (MOLTE) for Bayesian ranking and selection problem,
stochastic bandits or sequential experimental design problems. The Matlab-based
simulator allows the comparison of a number of learning policies (represented
as a series of .m modules) in the context of a wide range of problems (each
represented in its own .m module) which makes it easy to add new algorithms and
new test problems. State-of-the-art policies and various problem classes are
provided in the package. The choice of problems and policies is guided through
a spreadsheet-based interface. Different graphical metrics are included. MOLTE
is designed to be compatible with parallel computing to scale up from local
desktop to clusters and clouds. We offer MOLTE as an easy-to-use tool for the
research community that will make it possible to perform much more
comprehensive testing, spanning a broader selection of algorithms and test
problems. We demonstrate the capabilities of MOLTE through a series of
comparisons of policies on a starter library of test problems. We also address
the problem of tuning and constructing priors that have been largely overlooked
in optimal learning literature. We envision MOLTE as a modest spur to provide
researchers an easy environment to study interesting questions involved in
optimal learning.
Optimal Learning for Sequential Decision Making for Expensive Cost Functions with Stochastic Binary Feedbacks
Authors: Yingfei Wang, Chu Wang, Warren Powell
Date: 2017-09-13 00:00:00
Tasks: Decision Making, Multi-Armed Bandits, Sequential Decision Making

We consider the problem of sequentially making decisions that are rewarded by
"successes" and "failures" which can be predicted through an unknown
relationship that depends on a partially controllable vector of attributes for
each instance. The learner takes an active role in selecting samples from the
instance pool. The goal is to maximize the probability of success in either
offline (training) or online (testing) phases. Our problem is motivated by
real-world applications where observations are time-consuming and/or expensive.
We develop a knowledge gradient policy using an online Bayesian linear
classifier to guide the experiment by maximizing the expected value of
information of labeling each alternative. We provide a finite-time analysis of
the estimated error and show that the maximum likelihood estimator based
produced by the KG policy is consistent and asymptotically normal. We also show
that the knowledge gradient policy is asymptotically optimal in an offline
setting. This work further extends the knowledge gradient to the setting of
contextual bandits. We report the results of a series of experiments that
demonstrate its efficiency.