公开(公告)号：US20230334313A1

公开(公告)日：2023-10-19

申请号：US18212289

申请日：2023-06-21

Applicant: Microsoft Technology Licensing, LLC

Inventor： Fethiye Asli CELIKYILMAZ ,  Li Deng ,  Lihong Li ,  Chong Wang

IPC: G06N3/08 ,  G06F16/332

CPC classification number: G06N3/08 ,  G06F16/3329 ,  G06N3/006

Abstract: Systems and methods are disclosed for inquiry-based deep learning. In one implementation, a first content segment is selected from a body of content. The content segment includes a first content element. The first content segment is compared to a second content segment to identify a content element present in the first content segment that is not present in the second content segment. Based on an identification of the content element present in the first content segment that is not present in the second content segment, the content element is stored in a session memory. A first question is generated based on the first content segment. The session memory is processed to compute an answer to the first question. An action is initiated based on the answer. Using deep learning, content segments can be encoded into memory. Incremental questioning can serve to focus various deep learning operations on certain content segments.

公开(公告)号：US20190266246A1

公开(公告)日：2019-08-29

申请号：US15903942

申请日：2018-02-23

Applicant: Microsoft Technology Licensing, LLC

Inventor： Chong Wang ,  Yining Wang ,  Po-Sen Huang ,  Abdelrahman Samir Abdelrahman Mohamed ,  Dengyong Zhou ,  Li Deng ,  Sitao Huang

IPC: G06F17/28 ,  G10L15/28 ,  G06N7/00 ,  G06N3/04 ,  G06F15/18

Abstract: In neural-network-based approaches to sequence modeling, an output sequence may be modeled via segmentations, the probability of the output sequence being constructed as a sum of products of output-segment probabilities, taken over all valid output-sequence segmentations. A set of artificial neural networks may model the distribution of the output-sequence probability with a recurrent neural network modeling the distributions of the individual output-segment probabilities, optionally in conjunction with a second recurrent neural network modeling concatenations of output segments. In various embodiments, this approach is applied to neural phrase-based machine translation.

公开(公告)号：US11715000B2

公开(公告)日：2023-08-01

申请号：US15639304

申请日：2017-06-30

Applicant: Microsoft Technology Licensing, LLC

Inventor： Fethiye Asli Celikyilmaz ,  Li Deng ,  Lihong Li ,  Chong Wang

IPC: G06N3/08 ,  G06F16/332 ,  G06N3/006

CPC classification number: G06N3/08 ,  G06F16/3329 ,  G06N3/006

Abstract: Systems and methods are disclosed for inquiry-based deep learning. In one implementation, a first content segment is selected from a body of content. The content segment includes a first content element. The first content segment is compared to a second content segment to identify a content element present in the first content segment that is not present in the second content segment. Based on an identification of the content element present in the first content segment that is not present in the second content segment, the content element is stored in a session memory. A first question is generated based on the first content segment. The session memory is processed to compute an answer to the first question. An action is initiated based on the answer. Using deep learning, content segments can be encoded into memory. Incremental questioning can serve to focus various deep learning operations on certain content segments.

公开(公告)号：US20190005385A1

公开(公告)日：2019-01-03

申请号：US15639304

申请日：2017-06-30

Applicant: Microsoft Technology Licensing, LLC

Inventor： Fethiye Asli Celikyilmaz ,  Li Deng ,  Lihong Li ,  Chong Wang

IPC: G06N3/08 ,  G06N3/00

Abstract: Systems and methods are disclosed for inquiry-based deep learning. In one implementation, a first content segment is selected from a body of content. The content segment includes a first content element. The first content segment is compared to a second content segment to identify a content element present in the first content segment that is not present in the second content segment. Based on an identification of the content element present in the first content segment that is not present in the second content segment, the content element is stored in a session memory. A first question is generated based on the first content segment. The session memory is processed to compute an answer to the first question. An action is initiated based on the answer. Using deep learning, content segments can be encoded into memory. Incremental questioning can serve to focus various deep learning operations on certain content segments.