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公开(公告)号：US10013637B2
公开(公告)日：2018-07-03
申请号：US14602494
申请日：2015-01-22
Applicant: Microsoft Technology Licensing, LLC.
Inventor： Ishan Misra , Jin Li , Xian-Sheng Hua
IPC: G06K9/62
CPC classification number: G06K9/6227 , G06K9/6218 , G06K9/623 , G06K9/6262
Abstract: Optimizing multi-class image classification by leveraging patch-based features extracted from weakly supervised images to train classifiers is described. A corpus of images associated with a set of labels may be received. One or more patches may be extracted from individual images in the corpus. Patch-based features may be extracted from the one or more patches and patch representations may be extracted from individual patches of the one or more patches. The patches may be arranged into clusters based at least in part on the patch-based features. At least some of the individual patches may be removed from individual clusters based at least in part on determined similarity values that are representative of similarity between the individual patches. The system may train classifiers based in part on patch-based features extracted from patches in the refined clusters. The classifiers may be used to accurately and efficiently classify new images.
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公开(公告)号：US09875301B2
公开(公告)日：2018-01-23
申请号：US14266228
申请日：2014-04-30
Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC
Inventor： Xian-Sheng Hua , Jin Li , Yoshitaka Ushiku
CPC classification number: G06F17/30705 , G06F17/30675 , G06F17/30864 , G06N99/005
Abstract: Systems and methods for learning topic models from unstructured data and applying the learned topic models to recognize semantics for new data items are described herein. In at least one embodiment, a corpus of multimedia data items associated with a set of labels may be processed to generate a refined corpus of multimedia data items associated with the set of labels. Such processing may include arranging the multimedia data items in clusters based on similarities of extracted multimedia features and generating intra-cluster and inter-cluster features. The intra-cluster and the inter-cluster features may be used for removing multimedia data items from the corpus to generate the refined corpus. The refined corpus may be used for training topic models for identifying labels. The resulting models may be stored and subsequently used for identifying semantics of a multimedia data item input by a user.
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公开(公告)号：US11036799B2
公开(公告)日：2021-06-15
申请号：US16785587
申请日：2020-02-08
Applicant: Microsoft Technology Licensing, LLC
Inventor： Sudipta Sengupta , Biplob Kumar Debnath , Jin Li
IPC: G06F16/22 , G06F16/901 , G06F12/0864
Abstract: Described is using flash memory (or other secondary storage), RAM-based data structures and mechanisms to access key-value pairs stored in the flash memory using only a low RAM space footprint. A mapping (e.g. hash) function maps key-value pairs to a slot in a RAM-based index. The slot includes a pointer that points to a bucket of records on flash memory that each had keys that mapped to the slot. The bucket of records is arranged as a linear-chained linked list, e.g., with pointers from the most-recently written record to the earliest written record. Also described are compacting non-contiguous records of a bucket onto a single flash page, and garbage collection. Still further described is load balancing to reduce variation in bucket sizes, using a bloom filter per slot to avoid unnecessary searching, and splitting a slot into sub-slots.
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公开(公告)号：US09646227B2
公开(公告)日：2017-05-09
申请号：US14445463
申请日：2014-07-29
Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC
Inventor： Nitin Suri , Xian-Sheng Hua , Tzong-Jhy Wang , William D. Sproule , Andrew S. Ivory , Jin Li
CPC classification number: G06K9/6256 , G06K9/00744 , G06T7/20 , G06T2207/10016
Abstract: This disclosure describes techniques for training models from video data and applying the learned models to identify desirable video data. Video data may be labeled to indicate a semantic category and/or a score indicative of desirability. The video data may be processed to extract low and high level features. A classifier and a scoring model may be trained based on the extracted features. The classifier may estimate a probability that the video data belongs to at least one of the categories in a set of semantic categories. The scoring model may determine a desirability score for the video data. New video data may be processed to extract low and high level features, and feature values may be determined based on the extracted features. The learned classifier and scoring model may be applied to the feature values to determine a desirability score associated with the new video data.
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公开(公告)号：US09485184B2
公开(公告)日：2016-11-01
申请号：US13917441
申请日：2013-06-13
Applicant: Microsoft Technology Licensing, LLC
Inventor： Sanjeev Mehrotra , Tong L. Wynn , Jin Li , Sudipta Sengupta
IPC: H04L12/801 , H04L12/825 , H04L12/835 , H04L12/815 , H04L12/853
CPC classification number: H04L47/25 , H04L47/22 , H04L47/2416 , H04L47/29 , H04L47/30
Abstract: In various embodiments, methods and systems are disclosed for a hybrid rate plus window based congestion protocol that controls the rate of packet transmission into the network and provides low queuing delay, practically zero packet loss, fair allocation of network resources amongst multiple flows, and full link utilization. In one embodiment, a congestion window may be used to control the maximum number of outstanding bits, a transmission rate may be used to control the rate of packets entering the network (packet pacing), a queuing delay based rate update may be used to control queuing delay within tolerated bounds and minimize packet loss, and aggressive ramp-up/graceful back-off may be used to fully utilize the link capacity and additive-increase, multiplicative-decrease (AIMD) rate control may be used to provide fairness amongst multiple flows.
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Patent Agency Ranking
6.发明申请公开(公告)号：US20160217349A1
公开(公告)日：2016-07-28
申请号：US14602524
申请日：2015-01-22
Applicant: Microsoft Technology Licensing, LLC.
Inventor： Xian-Sheng Hua , Jin Li , Ishan Misra
CPC classification number: G06K9/66 , G06K9/6218 , G06K9/6269 , G06K9/6284 , G06N99/005
Abstract: Techniques for optimizing multi-class image classification by leveraging negative multimedia data items to train and update classifiers are described. The techniques describe accessing positive multimedia data items of a plurality of multimedia data items, extracting features from the positive multimedia data items, and training classifiers based at least in part on the features. The classifiers may include a plurality of model vectors each corresponding to one of the individual labels. The system may iteratively test the classifiers using positive multimedia data and negative multimedia data and may update one or more model vectors associated with the classifiers differently, depending on whether multimedia data items are positive or negative. Techniques for applying the classifiers to determine whether a new multimedia data item is associated with a topic based at least in part on comparing similarity values with corresponding statistics derived from classifier training are also described.
Abstract translation: 描述了通过利用负多媒体数据项来训练和更新分类器来优化多类图像分类的技术。 该技术描述了访问多个多媒体数据项中的正多媒体数据项，至少部分地基于特征从正向多媒体数据项中提取特征，以及训练分类器。 分类器可以包括多个模型向量，每个模型向量对应于单个标签之一。 系统可以使用正多媒体数据和负多媒体数据迭代地测试分类器，并且可以根据多媒体数据项是正还是负来更新与分类器相关联的一个或多个模型向量。 还描述了至少部分地基于将相似性值与从分类器训练得到的相应统计量进行比较来应用分类器来确定新的多媒体数据项是否与主题相关联的技术。
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公开(公告)号：US20160217344A1
公开(公告)日：2016-07-28
申请号：US14602494
申请日：2015-01-22
Applicant: Microsoft Technology Licensing, LLC.
Inventor： Ishan Misra , Jin Li , Xian-Sheng Hua
IPC: G06K9/62
CPC classification number: G06K9/6227 , G06K9/6218 , G06K9/623 , G06K9/6262
Abstract: Optimizing multi-class image classification by leveraging patch-based features extracted from weakly supervised images to train classifiers is described. A corpus of images associated with a set of labels may be received. One or more patches may be extracted from individual images in the corpus. Patch-based features may be extracted from the one or more patches and patch representations may be extracted from individual patches of the one or more patches. The patches may be arranged into clusters based at least in part on the patch-based features. At least some of the individual patches may be removed from individual clusters based at least in part on determined similarity values that are representative of similarity between the individual patches. The system may train classifiers based in part on patch-based features extracted from patches in the refined clusters. The classifiers may be used to accurately and efficiently classify new images.
Abstract translation: 描述了通过利用从弱监督图像提取的基于补丁的特征来训练分类器来优化多类图像分类。 可以接收与一组标签相关联的图像语料库。 可以从语料库中的各个图像中提取一个或多个补丁。 可以从一个或多个补丁中提取基于补丁的特征，并且可以从一个或多个补丁的各个补丁提取补丁表示。 该补丁可以至少部分地基于基于补丁的特征来布置成群集。 可以至少部分地基于代表各个贴片之间的相似性的所确定的相似度值，从单个簇中移除至少一些单独的贴片。 该系统可以部分地基于从精简集群中的补丁提取的基于补丁的特征来训练分类器。 分类器可用于准确和有效地对新图像进行分类。
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公开(公告)号：US09338234B2
公开(公告)日：2016-05-10
申请号：US14254795
申请日：2014-04-16
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jin Li , Sanjeev Mehrotra
CPC classification number: H04L67/1097 , G06F9/5066 , G06F21/10 , H04L29/06 , H04L67/02
Abstract: Disclosed herein are systems and methods for executing programs written in functional style. A distributed computing system receives a program that expresses computation upon one or more sets of distributed key-value pairs (DKVs) and one or more global variables (GVs). The system distributes an assembly that includes at least a compiled binary of the program to the nodes of a computing cluster, with different portions of the DKVs being stored across the plurality of nodes of the computing cluster. The system causes execution of the assembly by each of the plurality of nodes of the computing cluster, the ones of the plurality of nodes executing the assembly using the different portions of the one or more DKVs stored thereon.
Abstract translation: 这里公开了用于执行以功能性风格书写的程序的系统和方法。 分布式计算系统接收在一组或多组分布式键值对（DKV）和一个或多个全局变量（GV）上表达计算的程序。 该系统将至少包含程序的编译二进制文件的程序集分发到计算集群的节点，其中DKV的不同部分被存储在计算集群的多个节点之间。 该系统使得计算集群的多个节点中的每一个节点执行组装，多个节点中的节点使用其上存储的一个或多个DKV的不同部分来执行组件。
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公开(公告)号：US11003532B2
公开(公告)日：2021-05-11
申请号：US15626073
申请日：2017-06-16
Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC
Inventor： Cheng Huang , Jin Li , Aaron William Ogus , Douglas W. Phillips , Yu Lin Chen , Shuai Mu , Jinyang Li
Abstract: In various embodiments, methods and systems for implementing distributed data object management are provided. The distributed data object management system includes a local metadata-consensus information store and one or more remote metadata-consensus information stores for metadata-consensus information and a local data store and one or more remote data stores for erasure coded fragments. For a write operation, corresponding metadata writes and data writes are performed in parallel using a metadata write path and a data write path, respectively, when writing to the local metadata-consensus information store and the one or more remote metadata-consensus information stores and the local data store and the one or more remote data stores. And, for a read operation, corresponding metadata reads and data reads are performed in parallel using a metadata read path and a data read path, respectively, when reading from the metadata-consensus information stores and the data stores.
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公开(公告)号：US10310943B2
公开(公告)日：2019-06-04
申请号：US15626070
申请日：2017-06-16
Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC
Inventor： Cheng Huang , Jin Li , Aaron William Ogus , Douglas W. Phillips , Yu Lin Chen , Shuai Mu , Jinyang Li
Abstract: In various embodiments, methods and systems for implementing distributed data object management are provided. The distributed data object management system includes a distributed storage system having a local metadata-consensus information store in and one or more remote metadata-consensus information stores. A metadata-consensus information store is configured to store metadata-consensus information. The metadata-consensus information corresponds to erasure coded fragments of a data object and instruct on how to manage the erasure coded fragments. The distributed storage system further includes a local data store and one or more remote data stores for the erasure coded fragments. The distributed data object management system includes a distributed data object manager for operations including, interface operations, configuration operations, write operations, read operations, delete operations, garbage collection operations and failure recovery operations. The distributed data object management system is operates based on metadata paths and data paths, operating in parallel, for write operations and read operations.
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