Microsoft Technology Licensing, LLC

Google Inc.

Anthony Hsu

Yi Wu

Wei Guang Lu

Qi Guo

Anjan Kumar Amirishetty

Simhachala Sasikanth Gottapu

Daniel Sairom Krishnan Hewlett

Dan Liu

Baolei Li

Mingzhou Zhou

Xuhong Zhang

Keqiu Hu

Chenya Zhang

Niranjan Nilakantan

Wensheng Sun

Junjie Liang

Aspects of the disclosure are directed to a parallel recovery mode that applies log records while allowing read queries on read-replica databases. The parallel recovery mode can include applying log records in log sequence number (LSN) order for a block or for multiple blocks, and managing log records affecting multiple blocks. The parallel recovery mode can further manage dependency between different log records and maintain transactional consistency on read queries.

Technique for parallel recovery on read replica

In an example embodiment, a platform is provided that utilizes information available to a computer system to feed a neural network. The neural network is trained to determine both the probability that a searcher would select a given potential search result if it was presented to him or her and the probability that a subject of the potential search result would respond to a communication from the searcher. These probabilities are essentially combined to produce a single score that can be used to determine whether to present the searcher with the potential search result and, if so, how high to rank the potential search result among other search results. In a further example embodiment, embeddings used for the input features are modified during training to maximize an objective.

Embedding layer in neural network for ranking candidates

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Sunnyvale, CA, United States of America

Anthony Hsu