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Artificial intelligence

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Displaying 226 - 250 of 273

Workshop on Machine Learning for Optical Communication Systems: a summary

March 8, 2020
Author(s)
Joshua A. Gordon, Abdella Battou, Dan Kilper
A summary and overview of a public workshop on machine learning for optical Communication systems held on August 2nd 2019, by the Communications Technology Laboratory at the National Institute of Standards and Technology in Boulder, CO.

Energy-efficient stochastic computing with superparamagnetic tunnel junctions

March 5, 2020
Author(s)
Matthew W. Daniels, Advait Madhavan, Philippe Talatchian, Alice Mizrahi, Mark D. Stiles
Stochastic computing has been limited by the inaccuracies introduced by correlations between the pseudorandom bitstreams used in the calculation. We hybridize a stochastic version of magnetic tunnel junctions with basic CMOS logic gates to create a

Ray-based classification framework for high-dimensional data

February 3, 2020
Author(s)
Justyna Zwolak, Jacob Taylor, Sandesh Kalantre, Thomas McJunkin, Brian Weber
While classification of arbitrary structures in high dimensions may require complete quantitative information, for simple geometrical structures, low-dimensional qualitative information about the boundaries defining the structures can suffice. Rather than

Role of non-linear data processing on speech recognition task in the framework of reservoir computing

January 15, 2020
Author(s)
Flavio Abreu Araujo, Mathieu Riou, Jacob Torrejon, Sumito Tsunegi, Damien Querlioz, K. Yakushiji, Akio Fukushima, Hitoshi Kubota, Shinji Yuasa, Mark D. Stiles, Julie Grollier
The reservoir computing neural network architecture is widely used to test hardware systems for neuromorphic computing. One of the preferred tasks for bench-marking such devices is automatic speech recognition (ASR). However, this task requires acoustic

BowTie - a deep learning feedforward neural network for sentiment analysis

January 3, 2020
Author(s)
Apostol T. Vassilev
How to model and encode the semantics of human-written text and select the type of neural network to process it are not settled issues in sentiment analysis. Accuracy and transferability are critical issues in machine learning in general. These properties

Towards Edge-Based Deep Learning in Industrial Internet of Things

January 1, 2020
Author(s)
Fan Liang, Wei Yu, Xing Lu, David W. Griffith, Nada T. Golmie
As a typical application of the Internet of Things (IoT), the Industrial Internet of Things (I- IoT) connects all the related IoT sensing and actuating devices ubiquitously so that the monitoring and control of numerous industrial systems can be realized

Cognitive Information Measurements: A New Perspective

December 1, 2019
Author(s)
Hamid Gharavi
From a traditional point of view, the value of information does not change during transmission. The Shannon information theory considers information transmission as a statistical phenomenon for measuring the communication channel capacity. However, in

Active Learning Yields Better Training Data for Scientific Named Entity Recognition

November 1, 2019
Author(s)
Roselyne B. Tchoua, Aswathy Ajith, Zhi Hong, Logan T. Ward, Kyle Chard, Debra Audus, Shrayesh N. Patel, Juan J. de Pablo
Despite significant progress in natural language processing, machine learning models require substantial expert-annotated training data to perform well in tasks such as named entity recognition (NER) and entity relations extraction. Furthermore, NER is

The 2018 NIST Speaker Recognition Evaluation

September 15, 2019
Author(s)
Omid Sadjadi, Craig Greenberg, Elliot Singer, Douglas A. Reynolds, Lisa Mason, Jaime Hernandez-Cordero
In 2018, the U.S. national institute of standards and technology (NIST) conducted the most recent in an ongoing series of speaker recognition evaluations (SRE). SRE18 was organized in a similar manner to SRE16, focusing on speaker detection over

Temporal pattern recognition with delayed feedback spin-torque nano-oscillators

August 23, 2019
Author(s)
Mathieu Riou, Jacob Torrejon, B. Garitaine, Flavio Abreu Araujo, Paolo Bortolotti, Vincent Cros, Sumito Tsunegi, K. Yakushiji, Akio Fukushima, Hitoshi Kubota, Shinji Yuasa, Damien Querlioz, Mark D. Stiles, Julie Grollier
The recent demonstration of neuromorphic computing with spin-torque nano-oscillators has opened a path to energy efficient data processing. The success of this demonstration hinged on the intrinsic short-term memory of the oscillators. In this study, we

A Standardized Representation of Convolutional Neural Networks for Reliable Deployment of Machine Learning Models in the Manufacturing Industry

August 18, 2019
Author(s)
Max K. Ferguson, Seongwoon Jeong, Kincho H. Law, Anantha Narayanan Narayanan, Svetlana Levitan, Jena Tridivesh, Yung-Tsun Lee
The use of deep convolutional neural networks is becoming increasingly popular in the engineering and manufacturing sectors. However, managing the distribution of trained models is still a difficult task, partially due to the limitations of standardized

Comparison of Artificial Intelligence based approaches to cell function prediction

July 11, 2019
Author(s)
Sarala Padi, Petru S. Manescu, Nicholas Schaub, Nathan Hotaling, Carl G. Simon Jr., Peter Bajcsy
Predicting Retinal Pigment Epithelium (RPE) cell functions in stem cell implants using non-invasive bright field microscopy imaging is a critical task for clinical deployment of stem cell therapies. Such cell function predictions can be carried out either

Unreliable evidence in binary classification problems

May 7, 2019
Author(s)
David W. Flater
Binary classification problems include such things as classifying email messages as spam or non-spam and screening for the presence of disease (which can be seen as classifying a subject as disease-positive or disease- negative). Both Bayesian and

BowTie - A deep learning feedforward neural network for sentiment analysis

April 22, 2019
Author(s)
Apostol T. Vassilev
How to model and encode the semantics of human-written text and select the type of neural network to process it with are not settled issues in sentiment analysis. Accuracy and transferability are critical issues in machine learning in general. These
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