公开(公告)号：US20180114069A1

公开(公告)日：2018-04-26

申请号：US15848891

申请日：2017-12-20

Applicant: Facebook, Inc.

Inventor： Du Le Hong Tran ,  Balamanohar Paluri ,  Lubomir Bourdev ,  Robert D. Fergus ,  Sumit Chopra

IPC: G06K9/00

CPC classification number: G06K9/00744 ,  G06N3/0454 ,  G06N3/084

Abstract: Systems, methods, and non-transitory computer-readable media can acquire video content for which video feature descriptors are to be determined. The video content can be processed based at least in part on a convolutional neural network including a set of two-dimensional convolutional layers and a set of three-dimensional convolutional layers. One or more outputs can be generated from the convolutional neural network. A plurality of video feature descriptors for the video content can be determined based at least in part on the one or more outputs from the convolutional neural network.

公开(公告)号：US09858484B2

公开(公告)日：2018-01-02

申请号：US14585826

申请日：2014-12-30

Applicant: Facebook, Inc.

Inventor： Du Le Hong Tran ,  Balamanohar Paluri ,  Lubomir Bourdev ,  Robert D. Fergus ,  Sumit Chopra

IPC: G06K9/62 ,  G06K9/00

CPC classification number: G06K9/00744 ,  G06N3/0454 ,  G06N3/084

Abstract: Systems, methods, and non-transitory computer-readable media can acquire video content for which video feature descriptors are to be determined. The video content can be processed based at least in part on a convolutional neural network including a set of two-dimensional convolutional layers and a set of three-dimensional convolutional layers. One or more outputs can be generated from the convolutional neural network. A plurality of video feature descriptors for the video content can be determined based at least in part on the one or more outputs from the convolutional neural network.

公开(公告)号：US20170193390A1

公开(公告)日：2017-07-06

申请号：US14984956

申请日：2015-12-30

Applicant: Facebook, Inc.

Inventor： Jason E. Weston ,  Keith Adams ,  Sumit Chopra

IPC: G06N99/00 ,  G06Q50/00

CPC classification number: G06N20/00 ,  G06Q50/01

Abstract: In one embodiment, a method includes accessing a first set of entities, with which a user has interacted, and a second set of entities in a social-networking system. A first set of vector representations of the first set of entities are determined using a deep-learning model. A target entity is selected from the first set of entities, and the vector representation of the target entity is removed from the first set. The remaining vector representations in the first set are combined to determine a vector representation of the user. A second set of vector representations of the second set of entities are determined using the deep-learning model. Similarity scores are computed between the user and each of the target entity and the entities in the second set of entities. Vector representations of entities in the second set of entities are updated based on the similarity scores using the deep-learning model.

公开(公告)号：US20170103324A1

公开(公告)日：2017-04-13

申请号：US14881352

申请日：2015-10-13

Applicant: Facebook, Inc.

Inventor： Jason E. Weston ,  Sumit Chopra ,  Antoine Bordes

IPC: G06N5/02 ,  G06F17/27 ,  G06F17/30

CPC classification number: G06N5/02 ,  G06F16/24578 ,  G06F16/285 ,  G06F16/90332 ,  G06N3/0445 ,  G06N3/08 ,  G06N5/04

Abstract: Embodiments are disclosed for providing a machine-generated response (e.g., answer) to an input (e.g., question) based on long-term memory information. A method according to some embodiments include receiving an input; converting the input into an input feature vector in an internal feature representation space; updating a memory data structure by incorporating the input feature vector into the memory data structure; generating an output feature vector in the internal feature representation space, based on the updated memory data structure and the input feature vector; converting the output feature vector into an output object; and providing an output based on the output object as a response to the input.