Results 71 to 80 of about 136,861 (303)

Unsupervised Learning of Visual Structure [PDF]

Journal of Vision, 2002
To learn a visual code in an unsupervised manner, one may attempt to capture those features of the stimulus set that would contribute significantly to a statistically efficient representation (as dictated, e.g., by the Minimum Description Length principle).
Shimon Edelman, Nathan Intrator, Judah S. Jacobson   +2 more
openaire   +1 more source

LEAD: Literature Enhanced Ab Initio Discovery of Nitride Dusting Layers for Enhanced Tunnel Magnetoresistance and Lower Resistance Magnetic Tunnel Junctions

Advanced Materials, EarlyView.
Magnetic tunnel junctions (MTJs) using MgO tunnel barriers face challenges of high resistance‐area product and low tunnel magnetoresistance (TMR). To discover alternative materials, Literature Enhanced Ab initio Discovery (LEAD) is developed. The LEAD‐predicted materials are theoretically evaluated, showing that MTJs with dusting of ScN or TiN on ...
Sabiq Islam, William S. Rogers, Chen‐Yu Hu, Ming‐Yuan Song, Xinyu Bao, Shehrin Sayed, Jean Anne C. Incorvia   +6 more
wiley   +1 more source

Explainable Unsupervised Machine Learning for Cyber-Physical Systems

IEEE Access, 2021
Cyber-Physical Systems (CPSs) play a critical role in our modern infrastructure due to their capability to connect computing resources with physical systems.
Chathurika S Wickramasinghe, Kasun Amarasinghe, Daniel L. Marino, Craig Rieger, Milos Manic   +4 more
doaj   +1 more source

Continual Unsupervised Representation Learning

CoRR, 2019
NeurIPS ...
Rao, D, Visin, F, Rusu, AA, Teh, YW, Pascanu, R, Hadsell, R   +5 more
openaire   +4 more sources

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

Unsupervised Relation Extraction for E-Learning Applications [PDF]

, 2011
A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of PhilosophyIn this modern era many educational institutes and business organisations are adopting the e-Learning approach as it ...
Naveed Afzal, Afzal, Naveed
core  

A summary of the 2012 JHU CLSP Workshop on Zero Resource Speech Technologies and Models of Early Language Acquisition [PDF]

, 2013
We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding zero resource (unsupervised) speech technologies and related models of early language acquisition.
Johnson, Mark, Balakrishnan Varadarajan, Ian McGraw, Sanjeev Khudanpur, Church, Kenneth, Florian Metze, Fourtassi, Abdallah, Naomi Feldman, Bennett, Erin, Pascal Clark, Hermansky, Hynek, Clark, Pascal, Thomas Schatz, Harwath, David, Norouzian, Atta, Peddinti, Vijay, Sharon Goldwater, Varadarajan, Balakrishnan, Emmanuel Dupoux, Kenneth Church, Benjamin Borschinger, Samuel Thomas, Chiu, Justin, Metze, Florian, David Harwath, Erin Bennett, Rachael Richardson, Aren Jansen, Abdellah Fourtassi, Mark Johnson, Ewan Dunbar, Khudanpur, Sanjeev, Dunbar, Ewan, Hynek Hermansky, Richardson, Rachel, Atta Norouzian, Schatz, Thomas, McGraw, Ian, Vijayaditya Peddinti, Richard Rose, Feldman, Naomi, Mike Seltzer, Dupoux, Emmanuel, Chia-ying Lee, Borschinger, Benjamin, Levin, Keith, Keith Levin, Thomas, Samuel, Justin Chiu, Rose, Richard, Jansen, Aren, Seltzer, Mike, Goldwater, Sharon; id_orcid, Lee, Chia-ying   +53 more
core   +1 more source

Unsupervised Learning of Temporal Abstractions with Slot-Based Transformers

, 2022
The discovery of reusable subroutines simplifies decision making and planning in complex reinforcement learning problems. Previous approaches propose to learn such temporal abstractions in an unsupervised fashion through observing state-action ...
van Steenkiste, Sjoerd, Gopalakrishnan, Anand, Schmidhuber, Juergen, Irie, Kazuki   +3 more
core   +1 more source

Weaving Intelligence: Thermally Drawn Multimaterial Fibers Toward AI‐Enabled Smart Textiles

Advanced Materials, EarlyView.
Thermally drawn multimaterial fibers are rapidly advancing as intelligent structural units for next‐generation smart textiles. Integrating multimaterial architectures with neuromorphic and spiking‐neural‐network principles enables fabrics that can sense, compute, and adapt autonomously.
Vuong Dinh Trung, Jiaxiang Yi, Hieu Nguyen‐Duc, Tien Nu Hoang Lo, Weili Zhao, Chaoqun Dong, Fabien Sorin, Baris Caglar, Lei Wei, Tung Nguyen‐Dang   +9 more
wiley   +1 more source

Unsupervised colour image segmentation using dual-tree complex wavelet transform [PDF]

, 2010
In this paper we present an effective unsupervised colour image segmentation algorithm which uses multiscale edge information and spatial colour content. The multiscale edge information is extracted using the dual-tree complex wavelet transform.
Tjahjadi, Tardi, Çelik, Turgay
core   +1 more source