6,779 research outputs found
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks
Experiments in particle physics produce enormous quantities of data that must
be analyzed and interpreted by teams of physicists. This analysis is often
exploratory, where scientists are unable to enumerate the possible types of
signal prior to performing the experiment. Thus, tools for summarizing,
clustering, visualizing and classifying high-dimensional data are essential. In
this work, we show that meaningful physical content can be revealed by
transforming the raw data into a learned high-level representation using deep
neural networks, with measurements taken at the Daya Bay Neutrino Experiment as
a case study. We further show how convolutional deep neural networks can
provide an effective classification filter with greater than 97% accuracy
across different classes of physics events, significantly better than other
machine learning approaches
A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)
Neuromorphic computing systems comprise networks of neurons that use
asynchronous events for both computation and communication. This type of
representation offers several advantages in terms of bandwidth and power
consumption in neuromorphic electronic systems. However, managing the traffic
of asynchronous events in large scale systems is a daunting task, both in terms
of circuit complexity and memory requirements. Here we present a novel routing
methodology that employs both hierarchical and mesh routing strategies and
combines heterogeneous memory structures for minimizing both memory
requirements and latency, while maximizing programming flexibility to support a
wide range of event-based neural network architectures, through parameter
configuration. We validated the proposed scheme in a prototype multi-core
neuromorphic processor chip that employs hybrid analog/digital circuits for
emulating synapse and neuron dynamics together with asynchronous digital
circuits for managing the address-event traffic. We present a theoretical
analysis of the proposed connectivity scheme, describe the methods and circuits
used to implement such scheme, and characterize the prototype chip. Finally, we
demonstrate the use of the neuromorphic processor with a convolutional neural
network for the real-time classification of visual symbols being flashed to a
dynamic vision sensor (DVS) at high speed.Comment: 17 pages, 14 figure
Sequence Mining and Pattern Analysis in Drilling Reports with Deep Natural Language Processing
Drilling activities in the oil and gas industry have been reported over
decades for thousands of wells on a daily basis, yet the analysis of this text
at large-scale for information retrieval, sequence mining, and pattern analysis
is very challenging. Drilling reports contain interpretations written by
drillers from noting measurements in downhole sensors and surface equipment,
and can be used for operation optimization and accident mitigation. In this
initial work, a methodology is proposed for automatic classification of
sentences written in drilling reports into three relevant labels (EVENT,
SYMPTOM and ACTION) for hundreds of wells in an actual field. Some of the main
challenges in the text corpus were overcome, which include the high frequency
of technical symbols, mistyping/abbreviation of technical terms, and the
presence of incomplete sentences in the drilling reports. We obtain
state-of-the-art classification accuracy within this technical language and
illustrate advanced queries enabled by the tool.Comment: 7 pages, 14 figures, technical repor
Deep learning architectures for Computer Vision
Deep learning has become part of many state-of-the-art systems in multiple disciplines (specially in computer vision and speech processing). In this thesis Convolutional Neural Networks are used to solve the problem of recognizing people in images, both for verification and identification. Two different architectures, AlexNet and VGG19, both winners of the ILSVRC, have been fine-tuned and tested with four datasets: Labeled Faces in the Wild, FaceScrub, YouTubeFaces and Google UPC, a dataset generated at the UPC. Finally, with the features extracted from these fine-tuned networks, some verifications algorithms have been tested including Support Vector Machines, Joint Bayesian and Advanced Joint Bayesian formulation. The results of this work show that an Area Under the Receiver Operating Characteristic curve of 99.6% can be obtained, close to the state-of-the-art performance.El aprendizaje profundo se ha convertido en parte de muchos sistemas en el estado del arte de múltiples ámbitos (especialmente en visión por computador y procesamiento de voz). En esta tesis se utilizan las Redes Neuronales Convolucionales para resolver el problema de reconocer a personas en imágenes, tanto para verificación como para identificación. Dos arquitecturas diferentes, AlexNet y VGG19, ambas ganadores del ILSVRC, han sido afinadas y probadas con cuatro conjuntos de datos: Labeled Faces in the Wild, FaceScrub, YouTubeFaces y Google UPC, un conjunto generado en la UPC. Finalmente con las características extraídas de las redes afinadas, se han probado diferentes algoritmos de verificación, incluyendo Maquinas de Soporte Vectorial, Joint Bayesian y Advanced Joint Bayesian. Los resultados de este trabajo muestran que el Área Bajo la Curva de la Característica Operativa del Receptor puede llegar a ser del 99.6%, cercana al valor del estado del arte.L’aprenentatge profund s’ha convertit en una part importat de molts sistemes a l’estat de
l’art de múltiples àmbits (especialment de la visió per computador i el processament de
veu). A aquesta tesi s’utilitzen les Xarxes Neuronals Convolucionals per a resoldre el
problema de reconèixer persones a imatges, tant per verificació com per identificatió.
Dos arquitectures diferents, AlexNet i VGG19, les dues guanyadores del ILSVRC, han
sigut afinades i provades amb quatre bases de dades: Labeled Faces in the Wild,
FaceScrub, YouTubeFaces i Google UPC, un conjunt generat a la UPC.
Finalment, amb les característiques extretes de les xarxes afinades, s’han provat diferents
algoritmes de verificació, incloent Màquines de Suport Vectorial, Joint Bayesian i Advanced
Joint Bayesian. Els resultats d’aquest treball mostres que un Àrea Baix la Curva de la
Característica Operativa del Receptor por arribar a ser del 99.6%, propera al valor de l’estat
de l’art
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