60,018 research outputs found
A Comparative Exploration of ML Techniques for Tuning Query Degree of Parallelism
There is a large body of recent work applying machine learning (ML)
techniques to query optimization and query performance prediction in relational
database management systems (RDBMSs). However, these works typically ignore the
effect of \textit{intra-parallelism} -- a key component used to boost the
performance of OLAP queries in practice -- on query performance prediction. In
this paper, we take a first step towards filling this gap by studying the
problem of \textit{tuning the degree of parallelism (DOP) via ML techniques} in
Microsoft SQL Server, a popular commercial RDBMS that allows an individual
query to execute using multiple cores.
In our study, we cast the problem of DOP tuning as a {\em regression} task,
and examine how several popular ML models can help with query performance
prediction in a multi-core setting. We explore the design space and perform an
extensive experimental study comparing different models against a list of
performance metrics, testing how well they generalize in different settings:
to queries from the same template, to queries from a new template,
to instances of different scale, and to different instances and
queries. Our experimental results show that a simple featurization of the input
query plan that ignores cost model estimations can accurately predict query
performance, capture the speedup trend with respect to the available
parallelism, as well as help with automatically choosing an optimal per-query
DOP
Does computer vision matter for action?
Computer vision produces representations of scene content. Much computer
vision research is predicated on the assumption that these intermediate
representations are useful for action. Recent work at the intersection of
machine learning and robotics calls this assumption into question by training
sensorimotor systems directly for the task at hand, from pixels to actions,
with no explicit intermediate representations. Thus the central question of our
work: Does computer vision matter for action? We probe this question and its
offshoots via immersive simulation, which allows us to conduct controlled
reproducible experiments at scale. We instrument immersive three-dimensional
environments to simulate challenges such as urban driving, off-road trail
traversal, and battle. Our main finding is that computer vision does matter.
Models equipped with intermediate representations train faster, achieve higher
task performance, and generalize better to previously unseen environments. A
video that summarizes the work and illustrates the results can be found at
https://youtu.be/4MfWa2yZ0JcComment: Published in Science Robotics, 4(30), May 201
Natural Language Processing (almost) from Scratch
We propose a unified neural network architecture and learning algorithm that
can be applied to various natural language processing tasks including:
part-of-speech tagging, chunking, named entity recognition, and semantic role
labeling. This versatility is achieved by trying to avoid task-specific
engineering and therefore disregarding a lot of prior knowledge. Instead of
exploiting man-made input features carefully optimized for each task, our
system learns internal representations on the basis of vast amounts of mostly
unlabeled training data. This work is then used as a basis for building a
freely available tagging system with good performance and minimal computational
requirements
Machine learning \& artificial intelligence in the quantum domain
Quantum information technologies, and intelligent learning systems, are both
emergent technologies that will likely have a transforming impact on our
society. The respective underlying fields of research -- quantum information
(QI) versus machine learning (ML) and artificial intelligence (AI) -- have
their own specific challenges, which have hitherto been investigated largely
independently. However, in a growing body of recent work, researchers have been
probing the question to what extent these fields can learn and benefit from
each other. QML explores the interaction between quantum computing and ML,
investigating how results and techniques from one field can be used to solve
the problems of the other. Recently, we have witnessed breakthroughs in both
directions of influence. For instance, quantum computing is finding a vital
application in providing speed-ups in ML, critical in our "big data" world.
Conversely, ML already permeates cutting-edge technologies, and may become
instrumental in advanced quantum technologies. Aside from quantum speed-up in
data analysis, or classical ML optimization used in quantum experiments,
quantum enhancements have also been demonstrated for interactive learning,
highlighting the potential of quantum-enhanced learning agents. Finally, works
exploring the use of AI for the very design of quantum experiments, and for
performing parts of genuine research autonomously, have reported their first
successes. Beyond the topics of mutual enhancement, researchers have also
broached the fundamental issue of quantum generalizations of ML/AI concepts.
This deals with questions of the very meaning of learning and intelligence in a
world that is described by quantum mechanics. In this review, we describe the
main ideas, recent developments, and progress in a broad spectrum of research
investigating machine learning and artificial intelligence in the quantum
domain.Comment: Review paper. 106 pages. 16 figure
RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans
Automatic extraction of liver and tumor from CT volumes is a challenging task
due to their heterogeneous and diffusive shapes. Recently, 2D and 3D deep
convolutional neural networks have become popular in medical image segmentation
tasks because of the utilization of large labeled datasets to learn
hierarchical features. However, 3D networks have some drawbacks due to their
high cost on computational resources. In this paper, we propose a 3D hybrid
residual attention-aware segmentation method, named RA-UNet, to precisely
extract the liver volume of interests (VOI) and segment tumors from the liver
VOI. The proposed network has a basic architecture as a 3D U-Net which extracts
contextual information combining low-level feature maps with high-level ones.
Attention modules are stacked so that the attention-aware features change
adaptively as the network goes "very deep" and this is made possible by
residual learning. This is the first work that an attention residual mechanism
is used to process medical volumetric images. We evaluated our framework on the
public MICCAI 2017 Liver Tumor Segmentation dataset and the 3DIRCADb dataset.
The results show that our architecture outperforms other state-of-the-art
methods. We also extend our RA-UNet to brain tumor segmentation on the
BraTS2018 and BraTS2017 datasets, and the results indicate that RA-UNet
achieves good performance on a brain tumor segmentation task as well
Taking Human out of Learning Applications: A Survey on Automated Machine Learning
Machine learning techniques have deeply rooted in our everyday life. However,
since it is knowledge- and labor-intensive to pursue good learning performance,
human experts are heavily involved in every aspect of machine learning. In
order to make machine learning techniques easier to apply and reduce the demand
for experienced human experts, automated machine learning (AutoML) has emerged
as a hot topic with both industrial and academic interest. In this paper, we
provide an up to date survey on AutoML. First, we introduce and define the
AutoML problem, with inspiration from both realms of automation and machine
learning. Then, we propose a general AutoML framework that not only covers most
existing approaches to date but also can guide the design for new methods.
Subsequently, we categorize and review the existing works from two aspects,
i.e., the problem setup and the employed techniques. Finally, we provide a
detailed analysis of AutoML approaches and explain the reasons underneath their
successful applications. We hope this survey can serve as not only an
insightful guideline for AutoML beginners but also an inspiration for future
research.Comment: This is a preliminary and will be kept update
Evolutionary aspects of Reservoir Computing
Reservoir Computing (RC) is a powerful computational paradigm that allows
high versatility with cheap learning. While other artificial intelligence
approaches need exhaustive resources to specify their inner workings, RC is
based on a reservoir with highly non-linear dynamics that does not require a
fine tuning of its parts. These dynamics project input signals into
high-dimensional spaces, where training linear readouts to extract input
features is vastly simplified. Thus, inexpensive learning provides very
powerful tools for decision making, controlling dynamical systems,
classification, etc. RC also facilitates solving multiple tasks in parallel,
resulting in a high throughput. Existing literature focuses on applications in
artificial intelligence and neuroscience. We review this literature from an
evolutionary perspective. RC's versatility make it a great candidate to solve
outstanding problems in biology, which raises relevant questions: Is RC as
abundant in Nature as its advantages should imply? Has it evolved? Once
evolved, can it be easily sustained? Under what circumstances? (In other words,
is RC an evolutionarily stable computing paradigm?) To tackle these issues we
introduce a conceptual morphospace that would map computational selective
pressures that could select for or against RC and other computing paradigms.
This guides a speculative discussion about the questions above and allows us to
propose a solid research line that brings together computation and evolution
with RC as a working bench.Comment: 17 pages, 4 figures, review pape
Personalized Web Services for Web Information Extraction
The field of information extraction from the Web emerged with the growth of
the Web and the multiplication of online data sources. This paper is an
analysis of information extraction methods. It presents a service oriented
approach for web information extraction considering both web data management
and extraction services. Then we propose an SOA based architecture to enhance
flexibility and on-the-fly modification of web extraction services. An
implementation of the proposed architecture is proposed on the middleware level
of Java Enterprise Edition (JEE) servers
Personalized Saliency and its Prediction
Nearly all existing visual saliency models by far have focused on predicting
a universal saliency map across all observers. Yet psychology studies suggest
that visual attention of different observers can vary significantly under
specific circumstances, especially a scene is composed of multiple salient
objects. To study such heterogenous visual attention pattern across observers,
we first construct a personalized saliency dataset and explore correlations
between visual attention, personal preferences, and image contents.
Specifically, we propose to decompose a personalized saliency map (referred to
as PSM) into a universal saliency map (referred to as USM) predictable by
existing saliency detection models and a new discrepancy map across users that
characterizes personalized saliency. We then present two solutions towards
predicting such discrepancy maps, i.e., a multi-task convolutional neural
network (CNN) framework and an extended CNN with Person-specific Information
Encoded Filters (CNN-PIEF). Extensive experimental results demonstrate the
effectiveness of our models for PSM prediction as well their generalization
capability for unseen observers.Comment: 15 pages, 10 figures, journa
Driver Gaze Zone Estimation using Convolutional Neural Networks: A General Framework and Ablative Analysis
Driver gaze has been shown to be an excellent surrogate for driver attention
in intelligent vehicles. With the recent surge of highly autonomous vehicles,
driver gaze can be useful for determining the handoff time to a human driver.
While there has been significant improvement in personalized driver gaze zone
estimation systems, a generalized system which is invariant to different
subjects, perspectives and scales is still lacking. We take a step towards this
generalized system using Convolutional Neural Networks (CNNs). We finetune 4
popular CNN architectures for this task, and provide extensive comparisons of
their outputs. We additionally experiment with different input image patches,
and also examine how image size affects performance. For training and testing
the networks, we collect a large naturalistic driving dataset comprising of 11
long drives, driven by 10 subjects in two different cars. Our best performing
model achieves an accuracy of 95.18% during cross-subject testing,
outperforming current state of the art techniques for this task. Finally, we
evaluate our best performing model on the publicly available Columbia Gaze
Dataset comprising of images from 56 subjects with varying head pose and gaze
directions. Without any training, our model successfully encodes the different
gaze directions on this diverse dataset, demonstrating good generalization
capabilities
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