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1. 发明授权

US10402750B2 Identifying entities using a deep-learning model 有权
公开(公告)号：US10402750B2
公开(公告)日：2019-09-03
申请号：US14984956
申请日：2015-12-30
申请人： Facebook, Inc.
发明人： Jason E. Weston , Keith Adams , Sumit Chopra
IPC分类号： G06F15/18 , G06N20/00 , G06Q50/00
摘要： 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.

2. 发明授权

US10387464B2 Predicting labels using a deep-learning model 有权
公开(公告)号：US10387464B2
公开(公告)日：2019-08-20
申请号：US14949436
申请日：2015-11-23
申请人： Facebook, Inc.
发明人： Jason E. Weston , Keith Adams , Sumit Chopra
IPC分类号： G06N3/04 , G06F16/33
摘要： In one embodiment, a method includes receiving text query that includes n-grams. A vector representation of each n-gram is determined using a deep-learning model. A nonlinear combination of the vector representations of the n-grams is determined, and an embedding of the text query is determined based on the nonlinear combination. The embedding of the text query corresponds to a point in an embedding space, and the embedding space includes a plurality of points corresponding to a plurality of label embeddings. Each label embedding is based on a vector representation of a respective label determined using the deep-learning model. Label embeddings are identified as being relevant to the text query by applying a search algorithm to the embedding space. Points corresponding to the identified label embeddings are within a threshold distance of the point corresponding to the embedding of the text query in the embedding space.

3. 发明授权

US09311348B2 Method and system for implementing an array using different data structures 有权
公开(公告)号：US09311348B2
公开(公告)日：2016-04-12
申请号：US14713561
申请日：2015-05-15
申请人： Facebook, Inc.
发明人： Edwin Thur Gideon Smith , Keith Adams , Jason Owen Evans
IPC分类号： G06F17/30 , G06F9/34
CPC分类号： G06F17/30312 , G06F9/34 , G06F17/30126 , G06F17/3033
摘要： Disclosed are a method and system for implementing an array data type of a programming language using various data structures. The disclosed method includes a plurality of implementations in which the array data type may be implemented. The implementations provide an efficient way to retrieve elements from the array, especially in the order they are inserted into the array. The data structures also minimize the computing resources required to manage and access the array. The disclosed technique also selects one of the many implementations based on criteria such as access pattern or size of the array.

4. 发明申请

US20140156708A1 METHOD AND SYSTEM FOR IMPLEMENTING AN ARRAY USING DIFFERENT DATA STRUCTURES 有权
标题翻译：使用不同数据结构实现阵列的方法和系统
公开(公告)号：US20140156708A1
公开(公告)日：2014-06-05
申请号：US13691622
申请日：2012-11-30
申请人： Facebook, Inc.
发明人： Keith Adams , Andrew John Paroski , Jason Owen Evans
IPC分类号： G06F17/30
CPC分类号： G06F17/30312 , G06F9/34 , G06F17/30126 , G06F17/3033
摘要： Disclosed are a method and system for implementing an array data type of a programming language using various data structures. The disclosed method includes a plurality of implementations in which the array data type may be implemented. The implementations provide an efficient way to retrieve elements from the array, especially in the order they are inserted into the array. The data structures also minimize the computing resources required to manage and access the array. The disclosed technique also selects one of the many implementations based on criteria such as access pattern or size of the array.
摘要翻译：公开了一种使用各种数据结构实现编程语言的阵列数据类型的方法和系统。所公开的方法包括其中可以实现阵列数据类型的多个实现。这些实现提供了一种从数组中检索元素的有效方法，特别是按照它们插入数组的顺序。数据结构还将管理和访问阵列所需的计算资源最小化。所公开的技术还基于诸如访问模式或阵列的大小的标准来选择许多实现之一。

5. 发明申请

US20170109279A1 PARTITIONING SHARED CACHES 审中-公开
公开(公告)号：US20170109279A1
公开(公告)日：2017-04-20
申请号：US15390403
申请日：2016-12-23
申请人： Facebook, Inc.
发明人： Narsing Vijayrao , Keith Adams
IPC分类号： G06F12/0811 , G06F12/121 , G06F12/0846 , G06F12/084 , G06F12/0842
CPC分类号： G06F12/0811 , G06F12/084 , G06F12/0842 , G06F12/0848 , G06F12/12 , G06F12/121 , G06F2212/1024 , G06F2212/282 , G06F2212/604 , G06F2212/6042
摘要： Technology is provided for partitioning a shared unified cache in a multi-processor computer system. The technology can receive a request to allocate a portion of a shared unified cache memory for storing only executable instructions, partition the cache memory into multiple partitions, and allocate one of the partitions for storing only executable instructions. The technology can further determine the size of the portion of the cache memory to be allocated for storing only executable instructions as a function of the size of the multi-processor's L1 instruction cache and the number of cores in the multi-processor.

6. 发明申请

US20170061294A1 Predicting Labels Using a Deep-Learning Model 审中-公开
标题翻译：使用深度学习模型预测标签
公开(公告)号：US20170061294A1
公开(公告)日：2017-03-02
申请号：US14949436
申请日：2015-11-23
申请人： Facebook, Inc.
发明人： Jason E. Weston , Keith Adams , Sumit Chopra
IPC分类号： G06N5/04 , G06N99/00 , G06F17/30
CPC分类号： G06F16/334 , G06F16/3331 , G06N3/0454
摘要： In one embodiment, a method includes receiving text query that includes n-grams. A vector representation of each n-gram is determined using a deep-learning model. A nonlinear combination of the vector representations of the n-grams is determined, and an embedding of the text query is determined based on the nonlinear combination. The embedding of the text query corresponds to a point in an embedding space, and the embedding space includes a plurality of points corresponding to a plurality of label embeddings. Each label embedding is based on a vector representation of a respective label determined using the deep-learning model. Label embeddings are identified as being relevant to the text query by applying a search algorithm to the embedding space. Points corresponding to the identified label embeddings are within a threshold distance of the point corresponding to the embedding of the text query in the embedding space.
摘要翻译：在一个实施例中，一种方法包括接收包括n-gram的文本查询。使用深度学习模型确定每个n-gram的向量表示。确定n-gram的矢量表示的非线性组合，并且基于非线性组合来确定文本查询的嵌入。文本查询的嵌入对应于嵌入空间中的一个点，并且嵌入空间包括与多个标签嵌入相对应的多个点。每个标签嵌入基于使用深度学习模型确定的相应标签的向量表示。通过将搜索算法应用于嵌入空间，标签嵌入被标识为与文本查询相关。与识别的标签嵌入相对应的点在与嵌入空间中的文本查询的嵌入相对应的点的阈值距离内。

7. 发明申请

US20150095577A1 PARTITIONING SHARED CACHES 有权
标题翻译：分区共享快照
公开(公告)号：US20150095577A1
公开(公告)日：2015-04-02
申请号：US14040330
申请日：2013-09-27
申请人： Facebook, Inc.
发明人： Narsing Vijayrao , Keith Adams
IPC分类号： G06F12/08
CPC分类号： G06F12/0811 , G06F12/084 , G06F12/0842 , G06F12/0848 , G06F12/12 , G06F12/121 , G06F2212/1024 , G06F2212/282 , G06F2212/604 , G06F2212/6042
摘要： Technology is provided for partitioning a shared unified cache in a multi-processor computer system. The technology can receive a request to allocate a portion of a shared unified cache memory for storing only executable instructions, partition the cache memory into multiple partitions, and allocate one of the partitions for storing only executable instructions. The technology can further determine the size of the portion of the cache memory to be allocated for storing only executable instructions as a function of the size of the multi-processor's L1 instruction cache and the number of cores in the multi-processor.
摘要翻译：提供技术用于在多处理器计算机系统中分区共享统一缓存。该技术可以接收分配用于仅存储可执行指令的共享统一高速缓冲存储器的一部分的请求，将高速缓存存储器分割成多个分区，并且分配用于仅存储可执行指令的分区之一。该技术可以进一步确定要分配用于仅存储可执行指令的高速缓冲存储器的部分的大小，作为多处理器的L1指令高速缓存的大小和多处理器中的核心数量的函数。

8. 发明授权

US09880943B2 Cache management in a multi-threaded environment 有权
公开(公告)号：US09880943B2
公开(公告)日：2018-01-30
申请号：US15179729
申请日：2016-06-10
申请人： Facebook, Inc.
发明人： Keith Adams , Jason Owen Evans
IPC分类号： G06F9/46 , G06F12/12 , G06F9/50 , G06F9/48 , G06F9/455 , G06F9/44 , G06F12/0813 , G06F12/0891 , H04L29/08 , G06F12/0875
CPC分类号： G06F12/12 , G06F8/71 , G06F9/45516 , G06F9/4881 , G06F9/5016 , G06F9/5038 , G06F12/0813 , G06F12/0875 , G06F12/0891 , G06F2212/154 , G06F2212/452 , G06F2212/60 , G06F2212/62 , H04L67/1097
摘要： Disclosed here are methods, systems, paradigms and structures for deleting shared resources from a cache in a multi-threaded system. The shared resources can be used by a plurality of requests belonging to multiple threads executing in the system. When requests, such as requests for executing script code, and work items, such as work items for deleting a shared resource, are created, a global sequence number is assigned to each of them. The sequence number indicates the order in which the requests and work items are created. A particular work item can be executed to delete the shared resource if there are no requests having a sequence number lesser than that of the particular work item executing in the system. However, if there is at least one request with a sequence number lesser than that of the particular work item executing, the work item is ignored until the request completes executing.

9. 发明申请

US20170193390A1 Identifying Entities Using a Deep-Learning Model 审中-公开
公开(公告)号：US20170193390A1
公开(公告)日：2017-07-06
申请号：US14984956
申请日：2015-12-30
申请人： Facebook, Inc.
发明人： Jason E. Weston , Keith Adams , Sumit Chopra
IPC分类号： G06N99/00 , G06Q50/00
CPC分类号： G06N20/00 , G06Q50/01
摘要： 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.

10. 发明授权

US09569360B2 Partitioning shared caches 有权
标题翻译：分区共享缓存
公开(公告)号：US09569360B2
公开(公告)日：2017-02-14
申请号：US14040330
申请日：2013-09-27
申请人： Facebook, Inc.
发明人： Narsing Vijayrao , Keith Adams
IPC分类号： G06F12/08
CPC分类号： G06F12/0811 , G06F12/084 , G06F12/0842 , G06F12/0848 , G06F12/12 , G06F12/121 , G06F2212/1024 , G06F2212/282 , G06F2212/604 , G06F2212/6042
摘要： Technology is provided for partitioning a shared unified cache in a multi-processor computer system. The technology can receive a request to allocate a portion of a shared unified cache memory for storing only executable instructions, partition the cache memory into multiple partitions, and allocate one of the partitions for storing only executable instructions. The technology can further determine the size of the portion of the cache memory to be allocated for storing only executable instructions as a function of the size of the multi-processor's L1 instruction cache and the number of cores in the multi-processor.
摘要翻译：提供技术用于在多处理器计算机系统中分区共享统一缓存。该技术可以接收分配用于仅存储可执行指令的共享统一高速缓冲存储器的一部分的请求，将高速缓存存储器分割成多个分区，并且分配用于仅存储可执行指令的分区之一。该技术可以进一步确定要分配用于仅存储可执行指令的高速缓冲存储器的部分的大小，作为多处理器的L1指令高速缓存的大小和多处理器中的核心数量的函数。

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