Microsoft Technology Licensing, LLC

Mao Yang

Jing Bai

Qi Zhang

Lidong Zhou

Jan O Pedersen

Qi Lu

Shuo Chen

Yang Chen

Zhongxin Guo

Shuguang Liu

Xian Zhang

Meng Ai

Jie Tong

Haoxiang Lin

Xiaobing Guo

Yue-Sheng Liu

Qiufeng Yin

Cheng Chen

Yujing Wang

Bixiong Xu

Guodong Xing

Congrui Huang

According to implementations of the subject matter described herein, a solution is provided for pirated copy tracing based on a third party. In the solution, a report on a pirated copy of a digital content is received from a third party, wherein the report comprises first secret information for characterizing a first identification, time information and tracing information of the pirated copy. Subsequently, a request for verifying the report is received to determine whether the report is valid. When the report is determined as valid, a licensee associated with the report is marked as a first status to indicate that the pirated copy might be leaked by the licensee. Therefore, the pirated copy may be effectively traced based on third parties. The tracing information in the report can be hidden, and other third parties can therefore be prevented from using the tracing information for duplicate reports.

Third party based pirated copy tracing

Methods and apparatuses for implementing customized anomaly detection. A time-series data including a plurality of data points is obtained. Anomaly detection is performed to the time-series data with an anomaly detection model. A feedback associated with an anomaly detection result of at least one data point in the time-series data is received. The anomaly detection model is updated based at least on the feedback through reinforcement learning.

Customized anomaly detection

A new architecture is provided to support a precise information retrieval system on a web scale. The architecture provides algorithms to generate candidates and select the top N results via ranking models (e.g., Semantic ranking models, Aggregation ranking models) to capture term relationships between query and result contents at search-time.

Relevant passage retrieval system

Implementations of the present disclosure relate to visual programming for deep learning. A computer-implemented method comprises presenting a visual representation of an artificial neural network, the visual representation comprising graphical elements representing layers of the artificial neural network; in response to receiving a drag-and-drop operation on the graphical elements, modifying an intermediate representation of the artificial neural network, wherein the intermediate representation is independent of a deep learning framework and the drag-and-drop operation is configured to modify connections between the graphical elements; and modifying, based on the intermediate representation of the artificial neural network, code of the artificial neural network for a target deep learning framework.

Visual programming for deep learning

Growing community of inventors

Redmond, WA, United States of America

Mao Yang