Microsoft technology licensing, llc (20250005339). MACHINE LEARNING MODEL WITH DEPTH PROCESSING UNITS: Difference between revisions
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==Inventor(s)== | ==Inventor(s)== | ||
[[:Category:Jinyu Li of Redmond WA | [[:Category:Jinyu Li of Redmond WA US|Jinyu Li of Redmond WA US]][[Category:Jinyu Li of Redmond WA US]] | ||
[[:Category:Liang Lu of Redmond WA | [[:Category:Liang Lu of Redmond WA US|Liang Lu of Redmond WA US]][[Category:Liang Lu of Redmond WA US]] | ||
[[:Category:Changliang Liu of Bothell WA | [[:Category:Changliang Liu of Bothell WA US|Changliang Liu of Bothell WA US]][[Category:Changliang Liu of Bothell WA US]] | ||
[[:Category:Yifan Gong of Sammamish WA | [[:Category:Yifan Gong of Sammamish WA US|Yifan Gong of Sammamish WA US]][[Category:Yifan Gong of Sammamish WA US]] | ||
==MACHINE LEARNING MODEL WITH DEPTH PROCESSING UNITS== | ==MACHINE LEARNING MODEL WITH DEPTH PROCESSING UNITS== | ||
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This abstract first appeared for US patent application 20250005339 titled 'MACHINE LEARNING MODEL WITH DEPTH PROCESSING UNITS | This abstract first appeared for US patent application 20250005339 titled 'MACHINE LEARNING MODEL WITH DEPTH PROCESSING UNITS | ||
==Original Abstract Submitted== | ==Original Abstract Submitted== |
Latest revision as of 03:56, 25 March 2025
MACHINE LEARNING MODEL WITH DEPTH PROCESSING UNITS
Organization Name
microsoft technology licensing, llc
Inventor(s)
Changliang Liu of Bothell WA US
MACHINE LEARNING MODEL WITH DEPTH PROCESSING UNITS
This abstract first appeared for US patent application 20250005339 titled 'MACHINE LEARNING MODEL WITH DEPTH PROCESSING UNITS
Original Abstract Submitted
representative embodiments disclose machine learning classifiers used in scenarios such as speech recognition, image captioning, machine translation, or other sequence-to-sequence embodiments. the machine learning classifiers have a plurality of time layers, each layer having a time processing block and a depth processing block. the time processing block is a recurrent neural network such as a long short term memory (lstm) network. the depth processing blocks can be an lstm network, a gated deep neural network (dnn) or a maxout dnn. the depth processing blocks account for the hidden states of each time layer and uses summarized layer information for final input signal feature classification. an attention layer can also be used between the top depth processing block and the output layer.
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