Results 41 to 50 of about 264,274 (268)

What does fault tolerant Deep Learning need from MPI?

, 2017
Deep Learning (DL) algorithms have become the de facto Machine Learning (ML) algorithm for large scale data analysis. DL algorithms are computationally expensive - even distributed DL implementations which use MPI require days of training (model learning)
Amatya, Vinay, Daily, Jeff, Siegel, Charles, Vishnu, Abhinav   +3 more
core   +1 more source

Hospital Readmission After Traumatic Brain Injury Hospitalization in Community‐Dwelling Older Adults

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To examine the risk of hospital readmission after an index hospitalization for TBI in older adults. Methods Using data from the Atherosclerosis Risk in Communities (ARIC) study, we used propensity score matching of individuals with an index TBI‐related hospitalization to individuals with (1) non‐TBI hospitalizations (primary analysis)
Rachel Thomas, Connor A. Law, Joan A. Casey, Thomas Mosley, Rebecca F. Gottesman, Ramon Diaz‐Arrastia, Holly Elser, Andrea L. C. Schneider   +7 more
wiley   +1 more source

Deep Learning Cluster Structures for Management Decisions: The Digital CEO

Sensors, 2018
This paper presents a Deep Learning (DL) Cluster Structure for Management Decisions that emulates the way the brain learns and makes choices by combining different learning algorithms.
Will Serrano
doaj   +1 more source

DeepSecure: Scalable Provably-Secure Deep Learning [PDF]

, 2017
This paper proposes DeepSecure, a novel framework that enables scalable execution of the state-of-the-art Deep Learning (DL) models in a privacy-preserving setting. DeepSecure targets scenarios in which neither of the involved parties including the cloud
Koushanfar, Farinaz, Riazi, M. Sadegh, Rouhani, Bita Darvish   +2 more
core   +1 more source

To Learn or Not to Learn Features for Deformable Registration?

, 2018
Feature-based registration has been popular with a variety of features ranging from voxel intensity to Self-Similarity Context (SSC). In this paper, we examine the question on how features learnt using various Deep Learning (DL) frameworks can be used ...
A Klein, D Nigris De, D Shen, G Wu, GP Penney, J Masci, JE Iglesias, K Popuri, LG Nyýul, M Shakeri, M-M Rohé, MP Heinrich, MP Heinrich, MP Heinrich, O Ronneberger, S Joshi, X Yang, Y Ou   +17 more
core   +1 more source

Advancing Research on Biomaterials and Biological Materials with Scanning Electron Microscopy under Environmental and Low Vacuum Conditions

Advanced Engineering Materials, EarlyView.
Herein, environmental scanning electron microscopy (ESEM) is discussed as a powerful extension of conventional SEM for life sciences. By combining high‐resolution imaging with variable pressure and humidity, ESEM allows the analysis of untreated biological materials, supports in situ monitoring of hydration‐driven changes, and advances the functional ...
Jendrian Riedel, Lea Dierker, Antoine Eyram Kwame, Kim Nora Wagner, Jonas J. Unterholzner, Jan‐Henning Dirks, Dorothea Brüggemann   +6 more
wiley   +1 more source

Study of Deep Learning in Medical Education: Opportunities, Achievements and Future Challenges [PDF]

Journal of Advances in Medical Education and Professionalism
Introduction: In this era of progress, interest has developed regarding advancing deep learning (DL) in medicine. However, there has been reluctance to use deep learning, particularly among medical educators.
HOSSEIN MORADIMOKHLES, GWO-JEN HWANG, HOSSEIN ZANGENEH, MARYAM POURJAMSHIDI, AMIR HOSSEIN AMOOEIRAZANI   +4 more
doaj   +1 more source

Artificial Intelligence in Optical Communications: From Machine Learning to Deep Learning

Frontiers in Communications and Networks, 2021
Techniques from artificial intelligence have been widely applied in optical communication and networks, evolving from early machine learning (ML) to the recent deep learning (DL). This paper focuses on state-of-the-art DL algorithms and aims to highlight
Danshi Wang, Min Zhang
doaj   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

A survey of malware detection using deep learning

Machine Learning with Applications
The problem of malicious software (malware) detection and classification is a complex task, and there is no perfect approach. There is still a lot of work to be done. Unlike most other research areas, standard benchmarks are difficult to find for malware
Ahmed Bensaoud, Jugal Kalita, Mahmoud Bensaoud   +2 more
doaj   +1 more source