pwc-archive/papers-with-abstracts huggingface dataset:
Faster Deep Q-learning using Neural Episodic Control
Authors: Daichi Nishio, Satoshi Yamane
Date: 2018-01-06 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The research on deep reinforcement learning which estimates Q-value by deep
learning has been attracted the interest of researchers recently. In deep
reinforcement learning, it is important to efficiently learn the experiences
that an agent has collected by exploring environment. We propose NEC2DQN that
improves learning speed of a poor sample efficiency algorithm such as DQN by
using good one such as NEC at the beginning of learning. We show it is able to
learn faster than Double DQN or N-step DQN in the experiments of Pong.
Contextualize, Show and Tell: A Neural Visual Storyteller
Authors: Diana Gonzalez-Rico, Gibran Fuentes-Pineda
Date: 2018-06-03 00:00:00
Tasks: Decoder, Image Description, Visual Storytelling

We present a neural model for generating short stories from image sequences,
which extends the image description model by Vinyals et al. (Vinyals et al.,
2015). This extension relies on an encoder LSTM to compute a context vector of
each story from the image sequence. This context vector is used as the first
state of multiple independent decoder LSTMs, each of which generates the
portion of the story corresponding to each image in the sequence by taking the
image embedding as the first input. Our model showed competitive results with
the METEOR metric and human ratings in the internal track of the Visual
Storytelling Challenge 2018.
Content-based Video Relevance Prediction Challenge: Data, Protocol, and Baseline
Authors: Mengyi Liu, Xiaohui Xie, Hanning Zhou
Date: 2018-06-03 00:00:00
Tasks: Prediction

Video relevance prediction is one of the most important tasks for online
streaming service. Given the relevance of videos and viewer feedbacks, the
system can provide personalized recommendations, which will help the user
discover more content of interest. In most online service, the computation of
video relevance table is based on users' implicit feedback, e.g. watch and
search history. However, this kind of method performs poorly for "cold-start"
problems - when a new video is added to the library, the recommendation system
needs to bootstrap the video relevance score with very little user behavior
known. One promising approach to solve it is analyzing video content itself,
i.e. predicting video relevance by video frame, audio, subtitle and metadata.
In this paper, we describe a challenge on Content-based Video Relevance
Prediction (CBVRP) that is hosted by Hulu in the ACM Multimedia Conference
2018. In this challenge, Hulu drives the study on an open problem of exploiting
content characteristics directly from original video for video relevance
prediction. We provide massive video assets and ground truth relevance derived
from our really system, to build up a common platform for algorithm development
and performance evaluation.
Minnorm training: an algorithm for training over-parameterized deep neural networks
Authors: Yamini Bansal, Madhu Advani, David D. Cox, Andrew M. Saxe
Date: 2018-06-03 00:00:00
Tasks: Generalization Bounds

In this work, we propose a new training method for finding minimum weight
norm solutions in over-parameterized neural networks (NNs). This method seeks
to improve training speed and generalization performance by framing NN training
as a constrained optimization problem wherein the sum of the norm of the
weights in each layer of the network is minimized, under the constraint of
exactly fitting training data. It draws inspiration from support vector
machines (SVMs), which are able to generalize well, despite often having an
infinite number of free parameters in their primal form, and from recent
theoretical generalization bounds on NNs which suggest that lower norm
solutions generalize better. To solve this constrained optimization problem,
our method employs Lagrange multipliers that act as integrators of error over
training and identify `support vector'-like examples. The method can be
implemented as a wrapper around gradient based methods and uses standard
back-propagation of gradients from the NN for both regression and
classification versions of the algorithm. We provide theoretical justifications
for the effectiveness of this algorithm in comparison to early stopping and
$L_2$-regularization using simple, analytically tractable settings. In
particular, we show faster convergence to the max-margin hyperplane in a
shallow network (compared to vanilla gradient descent); faster convergence to
the minimum-norm solution in a linear chain (compared to $L_2$-regularization);
and initialization-independent generalization performance in a deep linear
network. Finally, using the MNIST dataset, we demonstrate that this algorithm
can boost test accuracy and identify difficult examples in real-world datasets.
Data-Free/Data-Sparse Softmax Parameter Estimation with Structured Class Geometries
Authors: Nisar Ahmed
Date: 2018-06-03 00:00:00
Tasks: parameter estimation

This note considers softmax parameter estimation when little/no labeled
training data is available, but a priori information about the relative
geometry of class label log-odds boundaries is available. It is shown that
`data-free' softmax model synthesis corresponds to solving a linear system of
parameter equations, wherein desired dominant class log-odds boundaries are
encoded via convex polytopes that decompose the input feature space. When
solvable, the linear equations yield closed-form softmax parameter solution
families using class boundary polytope specifications only. This allows softmax
parameter learning to be implemented without expensive brute force data
sampling and numerical optimization. The linear equations can also be adapted
to constrained maximum likelihood estimation in data-sparse settings. Since
solutions may also fail to exist for the linear parameter equations derived
from certain polytope specifications, it is thus also shown that there exist
probabilistic classification problems over m convexly separable classes for
which the log-odds boundaries cannot be learned using an m-class softmax model.
Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search
Authors: Luke Burks, Ian Loefgren, Luke Barbier, Jeremy Muesing, Jamison McGinley, Sousheel Vunnam, Nisar Ahmed
Date: 2018-06-03 00:00:00

In search applications, autonomous unmanned vehicles must be able to
efficiently reacquire and localize mobile targets that can remain out of view
for long periods of time in large spaces. As such, all available information
sources must be actively leveraged -- including imprecise but readily available
semantic observations provided by humans. To achieve this, this work develops
and validates a novel collaborative human-machine sensing solution for dynamic
target search. Our approach uses continuous partially observable Markov
decision process (CPOMDP) planning to generate vehicle trajectories that
optimally exploit imperfect detection data from onboard sensors, as well as
semantic natural language observations that can be specifically requested from
human sensors. The key innovation is a scalable hierarchical Gaussian mixture
model formulation for efficiently solving CPOMDPs with semantic observations in
continuous dynamic state spaces. The approach is demonstrated and validated
with a real human-robot team engaged in dynamic indoor target search and
capture scenarios on a custom testbed.
Dense Information Flow for Neural Machine Translation
Authors: Yanyao Shen, Xu Tan, Di He, Tao Qin, Tie-Yan Liu
Date: 2018-06-03 00:00:00
Proceeding: NAACL 2018 6
Tasks: Decoder, Machine Translation, NMT, Translation

Recently, neural machine translation has achieved remarkable progress by
introducing well-designed deep neural networks into its encoder-decoder
framework. From the optimization perspective, residual connections are adopted
to improve learning performance for both encoder and decoder in most of these
deep architectures, and advanced attention connections are applied as well.
Inspired by the success of the DenseNet model in computer vision problems, in
this paper, we propose a densely connected NMT architecture (DenseNMT) that is
able to train more efficiently for NMT. The proposed DenseNMT not only allows
dense connection in creating new features for both encoder and decoder, but
also uses the dense attention structure to improve attention quality. Our
experiments on multiple datasets show that DenseNMT structure is more
competitive and efficient.
Seeking Open-Ended Evolution in Swarm Chemistry II: Analyzing Long-Term Dynamics via Automated Object Harvesting
Authors: Hiroki Sayama
Date: 2018-04-10 00:00:00
Tasks: Object

We studied the long-term dynamics of evolutionary Swarm Chemistry by
extending the simulation length ten-fold compared to earlier work and by
developing and using a new automated object harvesting method. Both macroscopic
dynamics and microscopic object features were characterized and tracked using
several measures. Results showed that the evolutionary dynamics tended to
settle down into a stable state after the initial transient period, and that
the extent of environmental perturbations also affected the evolutionary trends
substantially. In the meantime, the automated harvesting method successfully
produced a huge collection of spontaneously evolved objects, revealing the
system's autonomous creativity at an unprecedented scale.
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
Authors: Haitao Liu, Jianfei Cai, Yi Wang, Yew-Soon Ong
Date: 2018-06-03 00:00:00
Proceeding: ICML 2018 7
Tasks: Distributed Computing, regression

In order to scale standard Gaussian process (GP) regression to large-scale
datasets, aggregation models employ factorized training process and then
combine predictions from distributed experts. The state-of-the-art aggregation
models, however, either provide inconsistent predictions or require
time-consuming aggregation process. We first prove the inconsistency of typical
aggregations using disjoint or random data partition, and then present a
consistent yet efficient aggregation model for large-scale GP. The proposed
model inherits the advantages of aggregations, e.g., closed-form inference and
aggregation, parallelization and distributed computing. Furthermore,
theoretical and empirical analyses reveal that the new aggregation model
performs better due to the consistent predictions that converge to the true
underlying function when the training size approaches infinity.
An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification
Authors: Shiwen Shen, Simon X. Han, Denise R. Aberle, Alex A. T. Bui, Willliam Hsu
Date: 2018-06-02 00:00:00
Tasks: Computed Tomography (CT), Diagnostic, General Classification

While deep learning methods are increasingly being applied to tasks such as
computer-aided diagnosis, these models are difficult to interpret, do not
incorporate prior domain knowledge, and are often considered as a "black-box."
The lack of model interpretability hinders them from being fully understood by
target users such as radiologists. In this paper, we present a novel
interpretable deep hierarchical semantic convolutional neural network (HSCNN)
to predict whether a given pulmonary nodule observed on a computed tomography
(CT) scan is malignant. Our network provides two levels of output: 1) low-level
radiologist semantic features, and 2) a high-level malignancy prediction score.
The low-level semantic outputs quantify the diagnostic features used by
radiologists and serve to explain how the model interprets the images in an
expert-driven manner. The information from these low-level tasks, along with
the representations learned by the convolutional layers, are then combined and
used to infer the high-level task of predicting nodule malignancy. This unified
architecture is trained by optimizing a global loss function including both
low- and high-level tasks, thereby learning all the parameters within a joint
framework. Our experimental results using the Lung Image Database Consortium
(LIDC) show that the proposed method not only produces interpretable lung
cancer predictions but also achieves significantly better results compared to
common 3D CNN approaches.
Learning and Generalizing Motion Primitives from Driving Data for Path-Tracking Applications
Authors: Boyang Wang, Zirui Li, Jianwei Gong, Yidi Liu, Huiyan Chen, Chao Lu
Date: 2018-06-02 00:00:00
Tasks: Clustering

Considering the driving habits which are learned from the naturalistic
driving data in the path-tracking system can significantly improve the
acceptance of intelligent vehicles. Therefore, the goal of this paper is to
generate the prediction results of lateral commands with confidence regions
according to the reference based on the learned motion primitives. We present a
two-level structure for learning and generalizing motion primitives through
demonstrations. The lower-level motion primitives are generated under the path
segmentation and clustering layer in the upper-level. The Gaussian Mixture
Model(GMM) is utilized to represent the primitives and Gaussian Mixture
Regression (GMR) is selected to generalize the motion primitives. We show how
the upper-level can help to improve the prediction accuracy and evaluate the
influence of different time scales and the number of Gaussian components. The
model is trained and validated by using the driving data collected from the
Beijing Institute of Technology (BIT) intelligent vehicle platform. Experiment
results show that the proposed method can extract the motion primitives from
the driving data and predict the future lateral control commands with high
accuracy.
Bandit Learning with Positive Externalities
Authors: Virag Shah, Jose Blanchet, Ramesh Johari
Date: 2018-02-15 00:00:00
Proceeding: NeurIPS 2018 12

In many platforms, user arrivals exhibit a self-reinforcing behavior: future
user arrivals are likely to have preferences similar to users who were
satisfied in the past. In other words, arrivals exhibit positive externalities.
We study multiarmed bandit (MAB) problems with positive externalities. We show
that the self-reinforcing preferences may lead standard benchmark algorithms
such as UCB to exhibit linear regret. We develop a new algorithm, Balanced
Exploration (BE), which explores arms carefully to avoid suboptimal convergence
of arrivals before sufficient evidence is gathered. We also introduce an
adaptive variant of BE which successively eliminates suboptimal arms. We
analyze their asymptotic regret, and establish optimality by showing that no
algorithm can perform better.
Long Short-Term Memory for Japanese Word Segmentation
Authors: Yoshiaki Kitagawa, Mamoru Komachi
Date: 2017-09-23 00:00:00
Proceeding: PACLIC 2018 12
Tasks: Chinese Word Segmentation, Japanese Word Segmentation, Segmentation

This study presents a Long Short-Term Memory (LSTM) neural network approach
to Japanese word segmentation (JWS). Previous studies on Chinese word
segmentation (CWS) succeeded in using recurrent neural networks such as LSTM
and gated recurrent units (GRU). However, in contrast to Chinese, Japanese
includes several character types, such as hiragana, katakana, and kanji, that
produce orthographic variations and increase the difficulty of word
segmentation. Additionally, it is important for JWS tasks to consider a global
context, and yet traditional JWS approaches rely on local features. In order to
address this problem, this study proposes employing an LSTM-based approach to
JWS. The experimental results indicate that the proposed model achieves
state-of-the-art accuracy with respect to various Japanese corpora.
A note on belief structures and S-approximation spaces
Authors: Ali Shakiba, Amir Kafshdar Goharshady, MohammadReza Hooshmandasl, Mohsen Alambardar Meybodi
Date: 2018-05-27 00:00:00

We study relations between evidence theory and S-approximation spaces. Both theories have their roots in the analysis of Dempster's multivalued mappings and lower and upper probabilities and have close relations to rough sets. We show that an S-approximation space, satisfying a monotonicity condition, can induce a natural belief structure which is a fundamental block in evidence theory. We also demonstrate that one can induce a natural belief structure on one set, given a belief structure on another set if those sets are related by a partial monotone S-approximation space.
NLP-assisted software testing: A systematic mapping of the literature
Authors: Vahid Garousi, Sara Bauer, Michael Felderer
Date: 2018-06-02 00:00:00
Tasks: software testing

Context: To reduce manual effort of extracting test cases from natural-language requirements, many approaches based on Natural Language Processing (NLP) have been proposed in the literature. Given the large amount of approaches in this area, and since many practitioners are eager to utilize such techniques, it is important to synthesize and provide an overview of the state-of-the-art in this area. Objective: Our objective is to summarize the state-of-the-art in NLP-assisted software testing which could benefit practitioners to potentially utilize those NLP-based techniques. Moreover, this can benefit researchers in providing an overview of the research landscape. Method: To address the above need, we conducted a survey in the form of a systematic literature mapping (classification). After compiling an initial pool of 95 papers, we conducted a systematic voting, and our final pool included 67 technical papers. Results: This review paper provides an overview of the contribution types presented in the papers, types of NLP approaches used to assist software testing, types of required input requirements, and a review of tool support in this area. Some key results we have detected are: (1) only four of the 38 tools (11%) presented in the papers are available for download; (2) a larger ratio of the papers (30 of 67) provided a shallow exposure to the NLP aspects (almost no details). Conclusion: This paper would benefit both practitioners and researchers by serving as an "index" to the body of knowledge in this area. The results could help practitioners utilizing the existing NLP-based techniques; this in turn reduces the cost of test-case design and decreases the amount of human resources spent on test activities. After sharing this review with some of our industrial collaborators, initial insights show that this review can indeed be useful and beneficial to practitioners.
Deep Feature Aggregation and Image Re-ranking with Heat Diffusion for Image Retrieval
Authors: Shanmin Pang, Jin Ma, Jianru Xue, Jihua Zhu, Vicente Ordonez
Date: 2018-05-22 00:00:00
Tasks: Image Retrieval, Re-Ranking, Retrieval

Image retrieval based on deep convolutional features has demonstrated
state-of-the-art performance in popular benchmarks. In this paper, we present a
unified solution to address deep convolutional feature aggregation and image
re-ranking by simulating the dynamics of heat diffusion. A distinctive problem
in image retrieval is that repetitive or \emph{bursty} features tend to
dominate final image representations, resulting in representations less
distinguishable. We show that by considering each deep feature as a heat
source, our unsupervised aggregation method is able to avoid
over-representation of \emph{bursty} features. We additionally provide a
practical solution for the proposed aggregation method and further show the
efficiency of our method in experimental evaluation. Inspired by the
aforementioned deep feature aggregation method, we also propose a method to
re-rank a number of top ranked images for a given query image by considering
the query as the heat source. Finally, we extensively evaluate the proposed
approach with pre-trained and fine-tuned deep networks on common public
benchmarks and show superior performance compared to previous work.
Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction
Authors: Yunzhe Tao, Lin Ma, Weizhong Zhang, Jian Liu, Wei Liu, Qiang Du
Date: 2018-06-02 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis, Time Series Prediction

Time series prediction has been studied in a variety of domains. However, it
is still challenging to predict future series given historical observations and
past exogenous data. Existing methods either fail to consider the interactions
among different components of exogenous variables which may affect the
prediction accuracy, or cannot model the correlations between exogenous data
and target data. Besides, the inherent temporal dynamics of exogenous data are
also related to the target series prediction, and thus should be considered as
well. To address these issues, we propose an end-to-end deep learning model,
i.e., Hierarchical attention-based Recurrent Highway Network (HRHN), which
incorporates spatio-temporal feature extraction of exogenous variables and
temporal dynamics modeling of target variables into a single framework.
Moreover, by introducing the hierarchical attention mechanism, HRHN can
adaptively select the relevant exogenous features in different semantic levels.
We carry out comprehensive empirical evaluations with various methods over
several datasets, and show that HRHN outperforms the state of the arts in time
series prediction, especially in capturing sudden changes and sudden
oscillations of time series.
Deep Pepper: Expert Iteration based Chess agent in the Reinforcement Learning Setting
Authors: Sai Krishna G. V., Kyle Goyette, Ahmad Chamseddine, Breandan Considine
Date: 2018-06-02 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

An almost-perfect chess playing agent has been a long standing challenge in
the field of Artificial Intelligence. Some of the recent advances demonstrate
we are approaching that goal. In this project, we provide methods for faster
training of self-play style algorithms, mathematical details of the algorithm
used, various potential future directions, and discuss most of the relevant
work in the area of computer chess. Deep Pepper uses embedded knowledge to
accelerate the training of the chess engine over a "tabula rasa" system such as
Alpha Zero. We also release our code to promote further research.
A Hierarchical Fuzzy System for an Advanced Driving Assistance System
Authors: Mejdi Ben Dkhil, Ali Wali, Adel M. ALIMI
Date: 2018-06-02 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

In this study, we present a hierarchical fuzzy system by evaluating the risk
state for a Driver Assistance System in order to contribute in reducing the
road accident's number. A key component of this system is its ability to
continually detect and test the inside and outside risks in real time: The
outside car risks by detecting various road moving objects; this proposed
system stands on computer vision approaches. The inside risks by presenting an
automatic system for drowsy driving identification or detection by evaluating
EEG signals of the driver; this developed system is based on computer vision
techniques and biometrics factors (electroencephalogram EEG). This proposed
system is then composed of three main modules. The first module is responsible
for identifying the driver drowsiness state through his eye movements (physical
drowsiness). The second one is responsible for detecting and analysing his
physiological signals to also identify his drowsiness state (moral drowsiness).
The third module is responsible to evaluate the road driving risks by detecting
of the road different moving objects in a real time. The final decision will be
obtained by merging of the three detection systems through the use of fuzzy
decision rules. Finally, the proposed approach has been improved on ten samples
from a proposed dataset.