16,410 research outputs found
Extractive Summarization using Deep Learning
This paper proposes a text summarization approach for factual reports using a
deep learning model. This approach consists of three phases: feature
extraction, feature enhancement, and summary generation, which work together to
assimilate core information and generate a coherent, understandable summary. We
are exploring various features to improve the set of sentences selected for the
summary, and are using a Restricted Boltzmann Machine to enhance and abstract
those features to improve resultant accuracy without losing any important
information. The sentences are scored based on those enhanced features and an
extractive summary is constructed. Experimentation carried out on several
articles demonstrates the effectiveness of the proposed approach. Source code
available at: https://github.com/vagisha-nidhi/TextSummarizerComment: Accepted to 18th International Conference on Computational
Linguistics and Intelligent Text Processin
Machine Learning for Condensed Matter Physics
Condensed Matter Physics (CMP) seeks to understand the microscopic
interactions of matter at the quantum and atomistic levels, and describes how
these interactions result in both mesoscopic and macroscopic properties. CMP
overlaps with many other important branches of science, such as Chemistry,
Materials Science, Statistical Physics, and High-Performance Computing. With
the advancements in modern Machine Learning (ML) technology, a keen interest in
applying these algorithms to further CMP research has created a compelling new
area of research at the intersection of both fields. In this review, we aim to
explore the main areas within CMP, which have successfully applied ML
techniques to further research, such as the description and use of ML schemes
for potential energy surfaces, the characterization of topological phases of
matter in lattice systems, the prediction of phase transitions in off-lattice
and atomistic simulations, the interpretation of ML theories with
physics-inspired frameworks and the enhancement of simulation methods with ML
algorithms. We also discuss in detail the main challenges and drawbacks of
using ML methods on CMP problems, as well as some perspectives for future
developments.Comment: 48 pages, 2 figures, 300 references. Review paper. Major Revisio
Boltzmann Machines and Denoising Autoencoders for Image Denoising
Image denoising based on a probabilistic model of local image patches has
been employed by various researchers, and recently a deep (denoising)
autoencoder has been proposed by Burger et al. [2012] and Xie et al. [2012] as
a good model for this. In this paper, we propose that another popular family of
models in the field of deep learning, called Boltzmann machines, can perform
image denoising as well as, or in certain cases of high level of noise, better
than denoising autoencoders. We empirically evaluate the two models on three
different sets of images with different types and levels of noise. Throughout
the experiments we also examine the effect of the depth of the models. The
experiments confirmed our claim and revealed that the performance can be
improved by adding more hidden layers, especially when the level of noise is
high
Energy-based Models for Video Anomaly Detection
Automated detection of abnormalities in data has been studied in research
area in recent years because of its diverse applications in practice including
video surveillance, industrial damage detection and network intrusion
detection. However, building an effective anomaly detection system is a
non-trivial task since it requires to tackle challenging issues of the shortage
of annotated data, inability of defining anomaly objects explicitly and the
expensive cost of feature engineering procedure. Unlike existing appoaches
which only partially solve these problems, we develop a unique framework to
cope the problems above simultaneously. Instead of hanlding with ambiguous
definition of anomaly objects, we propose to work with regular patterns whose
unlabeled data is abundant and usually easy to collect in practice. This allows
our system to be trained completely in an unsupervised procedure and liberate
us from the need for costly data annotation. By learning generative model that
capture the normality distribution in data, we can isolate abnormal data points
that result in low normality scores (high abnormality scores). Moreover, by
leverage on the power of generative networks, i.e. energy-based models, we are
also able to learn the feature representation automatically rather than
replying on hand-crafted features that have been dominating anomaly detection
research over many decades. We demonstrate our proposal on the specific
application of video anomaly detection and the experimental results indicate
that our method performs better than baselines and are comparable with
state-of-the-art methods in many benchmark video anomaly detection datasets
Radiological images and machine learning: trends, perspectives, and prospects
The application of machine learning to radiological images is an increasingly
active research area that is expected to grow in the next five to ten years.
Recent advances in machine learning have the potential to recognize and
classify complex patterns from different radiological imaging modalities such
as x-rays, computed tomography, magnetic resonance imaging and positron
emission tomography imaging. In many applications, machine learning based
systems have shown comparable performance to human decision-making. The
applications of machine learning are the key ingredients of future clinical
decision making and monitoring systems. This review covers the fundamental
concepts behind various machine learning techniques and their applications in
several radiological imaging areas, such as medical image segmentation, brain
function studies and neurological disease diagnosis, as well as computer-aided
systems, image registration, and content-based image retrieval systems.
Synchronistically, we will briefly discuss current challenges and future
directions regarding the application of machine learning in radiological
imaging. By giving insight on how take advantage of machine learning powered
applications, we expect that clinicians can prevent and diagnose diseases more
accurately and efficiently.Comment: 13 figure
Multimodal Emotion Recognition Using Multimodal Deep Learning
To enhance the performance of affective models and reduce the cost of
acquiring physiological signals for real-world applications, we adopt
multimodal deep learning approach to construct affective models from multiple
physiological signals. For unimodal enhancement task, we indicate that the best
recognition accuracy of 82.11% on SEED dataset is achieved with shared
representations generated by Deep AutoEncoder (DAE) model. For multimodal
facilitation tasks, we demonstrate that the Bimodal Deep AutoEncoder (BDAE)
achieves the mean accuracies of 91.01% and 83.25% on SEED and DEAP datasets,
respectively, which are much superior to the state-of-the-art approaches. For
cross-modal learning task, our experimental results demonstrate that the mean
accuracy of 66.34% is achieved on SEED dataset through shared representations
generated by EEG-based DAE as training samples and shared representations
generated by eye-based DAE as testing sample, and vice versa
Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
With quantum computing technologies nearing the era of commercialization and
quantum supremacy, machine learning (ML) appears as one of the promising
"killer" applications. Despite significant effort, there has been a disconnect
between most quantum ML proposals, the needs of ML practitioners, and the
capabilities of near-term quantum devices to demonstrate quantum enhancement in
the near future. In this contribution to the focus collection on "What would
you do with 1000 qubits?", we provide concrete examples of intractable ML tasks
that could be enhanced with near-term devices. We argue that to reach this
target, the focus should be on areas where ML researchers are struggling, such
as generative models in unsupervised and semi-supervised learning, instead of
the popular and more tractable supervised learning techniques. We also
highlight the case of classical datasets with potential quantum-like
statistical correlations where quantum models could be more suitable. We focus
on hybrid quantum-classical approaches and illustrate some of the key
challenges we foresee for near-term implementations. Finally, we introduce the
quantum-assisted Helmholtz machine (QAHM), an attempt to use near-term quantum
devices to tackle high-dimensional datasets of continuous variables. Instead of
using quantum computers to assist deep learning, as previous approaches do, the
QAHM uses deep learning to extract a low-dimensional binary representation of
data, suitable for relatively small quantum processors which can assist the
training of an unsupervised generative model. Although we illustrate this
concept on a quantum annealer, other quantum platforms could benefit as well
from this hybrid quantum-classical framework.Comment: Contribution to the special issue of Quantum Science & Technology
(QST) on "What would you do with 1000 qubits
A theoretical basis for efficient computations with noisy spiking neurons
Network of neurons in the brain apply - unlike processors in our current
generation of computer hardware - an event-based processing strategy, where
short pulses (spikes) are emitted sparsely by neurons to signal the occurrence
of an event at a particular point in time. Such spike-based computations
promise to be substantially more power-efficient than traditional clocked
processing schemes. However it turned out to be surprisingly difficult to
design networks of spiking neurons that are able to carry out demanding
computations. We present here a new theoretical framework for organizing
computations of networks of spiking neurons. In particular, we show that a
suitable design enables them to solve hard constraint satisfaction problems
from the domains of planning - optimization and verification - logical
inference. The underlying design principles employ noise as a computational
resource. Nevertheless the timing of spikes (rather than just spike rates)
plays an essential role in the resulting computations. Furthermore, one can
demonstrate for the Traveling Salesman Problem a surprising computational
advantage of networks of spiking neurons compared with traditional artificial
neural networks and Gibbs sampling. The identification of such advantage has
been a well-known open problem.Comment: main paper: 21 pages, 5 figures supplemental paper: 11 pages, no
figure
Physics-Driven Regularization of Deep Neural Networks for Enhanced Engineering Design and Analysis
In this paper, we introduce a physics-driven regularization method for
training of deep neural networks (DNNs) for use in engineering design and
analysis problems. In particular, we focus on prediction of a physical system,
for which in addition to training data, partial or complete information on a
set of governing laws is also available. These laws often appear in the form of
differential equations, derived from first principles, empirically-validated
laws, or domain expertise, and are usually neglected in data-driven prediction
of engineering systems. We propose a training approach that utilizes the known
governing laws and regularizes data-driven DNN models by penalizing divergence
from those laws. The first two numerical examples are synthetic examples, where
we show that in constructing a DNN model that best fits the measurements from a
physical system, the use of our proposed regularization results in DNNs that
are more interpretable with smaller generalization errors, compared to other
common regularization methods. The last two examples concern metamodeling for a
random Burgers' system and for aerodynamic analysis of passenger vehicles,
where we demonstrate that the proposed regularization provides superior
generalization accuracy compared to other common alternatives
Representation Learning on Large and Small Data
Deep learning owes its success to three key factors: scale of data, enhanced
models to learn representations from data, and scale of computation. This book
chapter presented the importance of the data-driven approach to learn good
representations from both big data and small data. In terms of big data, it has
been widely accepted in the research community that the more data the better
for both representation and classification improvement. The question is then
how to learn representations from big data, and how to perform representation
learning when data is scarce. We addressed the first question by presenting CNN
model enhancements in the aspects of representation, optimization, and
generalization. To address the small data challenge, we showed transfer
representation learning to be effective. Transfer representation learning
transfers the learned representation from a source domain where abundant
training data is available to a target domain where training data is scarce.
Transfer representation learning gave the OM and melanoma diagnosis modules of
our XPRIZE Tricorder device (which finished out of competing
teams) a significant boost in diagnosis accuracy.Comment: Book chapte
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