Machine learning for quantum and classical photonic devices (Conference Presentation)

2019 ◽  
Author(s):  
Claudio Conti
Keyword(s):  
Machine Learning ◽  
Photonic Devices

scholarly journals Ultra-compact integrated photonic devices enabled by machine learning and digital metamaterials

OSA Continuum ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 602
Author(s):  
Sourangsu Banerji ◽  
Apratim Majumder ◽  
Alex Hamrick ◽  
Rajesh Menon ◽  
Berardi Sensale-Rodriguez
Keyword(s):  
Machine Learning ◽  
Photonic Devices

Design of multi-parameter photonic devices using machine learning pattern recognition

2020 ◽  
Author(s):  
Daniele Melati ◽  
Yuri Grinberg ◽  
Mohsen Kamandar Dezfouli ◽  
Jens H. Schmid ◽  
Pavel Cheben ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Photonic Devices ◽  
Learning Pattern

Machine learning design of subwavelengh integrated photonic devices

2019 ◽  
Author(s):  
Daniele Melati ◽  
Mohsen Kamandar Dezfouli ◽  
Yuri Grinberg ◽  
Siegfried Janz ◽  
Jens H. Schmid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Photonic Devices ◽  
Learning Design

scholarly journals Machine learning–assisted global optimization of photonic devices

2021 ◽  
pp. 381-394
Author(s):  
Zhaxylyk A. Kudyshev ◽  
Alexander V. Kildishev ◽  
Vladimir M. Shalaev ◽  
Alexandra Boltasseva
Keyword(s):  
Machine Learning ◽  
Global Optimization ◽  
Photonic Devices

Machine-learning dimensionality reduction for multi-objective design of photonic devices

2021 ◽  
Author(s):  
Daniele Melati ◽  
Mohsen Kamandar Dezfouli ◽  
Yuri Grinberg ◽  
Muhammad Al-Digeil ◽  
Siegfried Janz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dimensionality Reduction ◽  
Photonic Devices ◽  
Multi Objective

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions
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