What is recorded here?
Provided a computational-network abstraction and CPU/GPU toolkit for expressing neural model graphs, with forward/gradient computation and reusable model/network descriptions.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The family combines the 2014 computational-network introduction with a separately authored 2016 toolkit account. Later details are not backdated. Computational graphs, automatic differentiation and existing model types are inherited; arbitrary-topology descriptions do not guarantee that cyclic programs converge or execute unchanged.
Read the original work
- An introduction to computational networks and the computational network toolkit (invited talk)
- An Introduction to Computational Networks and the Computational Network Toolkit —17February2016draft
- Microsoft Research: CNTK report index and linked revision
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Dong Yu; Adam Eversole; Michael L. Seltzer; Kaisheng Yao; Brian Guenter; Oleksii Kuchaiev; Frank Seide; Huaming Wang; Jasha Droppo; Zhiheng Huang; Geoff Zweig; Chris Rossbach; Jon Currey; Amit Agarwal; Eldar Akchurin; Chris Basoglu; Guoguo Chen; Scott Cyphers; Mark Hillebrand; Xuedong Huang; Vladimir Ivanov; Alexey Kamenev; Philipp Kranen; Wolfgang Manousek; Avner May; Bhaskar Mitra; Olivier Nano; Gaizka Navarro; Alexey Orlov; Marko Padmilac; Hari Parthasarathi; Baolin Peng; Alexey Reznichenko; Malcolm Slaney; Andreas Stolcke; Yu Zhang; Geoffrey Zweig
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Research locations and affiliations
Location not yet established
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English catalogue narrative
CNTK: computational-network graph specification and execution
Provided a computational-network abstraction and CPU/GPU toolkit for expressing neural model graphs, with forward/gradient computation and reusable model/network descriptions.
Scientific scope
The family combines the 2014 computational-network introduction with a separately authored 2016 toolkit account. Later details are not backdated. Computational graphs, automatic differentiation and existing model types are inherited; arbitrary-topology descriptions do not guarantee that cyclic programs converge or execute unchanged.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Dong Yu; Adam Eversole; Michael L. Seltzer; Kaisheng Yao; Brian Guenter; Oleksii Kuchaiev; Frank Seide; Huaming Wang; Jasha Droppo; Zhiheng Huang; Geoff Zweig; Chris Rossbach; Jon Currey; Amit Agarwal; Eldar Akchurin; Chris Basoglu; Guoguo Chen; Scott Cyphers; Mark Hillebrand; Xuedong Huang; Vladimir Ivanov; Alexey Kamenev; Philipp Kranen; Wolfgang Manousek; Avner May; Bhaskar Mitra; Olivier Nano; Gaizka Navarro; Alexey Orlov; Marko Padmilac; Hari Parthasarathi; Baolin Peng; Alexey Reznichenko; Malcolm Slaney; Andreas Stolcke; Yu Zhang; Geoffrey Zweig
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.