Adobe, Inc.

Amazon Technologies, Inc.

Zhao Song

Ryan A Rossi

Gang Wu

Viswanathan Swaminathan

Eunyee Koh

Anup Bandigadi Rao

Georgios Theocharous

Nedim Lipka

Stefano Petrangeli

Tung Mai

Haoliang Wang

Balakrishnan Narayanaswamy

Jeremiah Wilton

Vijay Gopal Joshi

Maxym Kharchenko

Sridhar Mahadevan

Yang Li

Aravind Reddy Talla

Kai Wang

Vikramank Yogendra Singh

Joshua Tobey Oberwetter

Kyle Henderson Hailey

Embodiments provide systems, methods, and computer storage media for a Nonsymmetric Determinantal Point Process (NDPPs) for compatible set recommendations in a setting where data representing entities (e.g., items) arrives in a stream. A stream representing compatible sets of entities is received and used to update a latent representation of the entities and a compatibility distribution indicating likelihood of compatibility of subsets of the entities. The probability distribution is accessed in a single sequential pass to predict a compatible complete set of entities that completes an incomplete set of entities. The predicted complete compatible set is provided a recommendation for entities that complete the incomplete set of entities.

Online inference and learning for nonsymmetric determinantal point processes

Embodiments described herein provide methods and systems for facilitating actively-learned context modeling. In one embodiment, a subset of data is selected from a training dataset corresponding with an image to be compressed, the subset of data corresponding with a subset of data of pixels of the image. A context model is generated using the selected subset of data. The context model is generally in the form of a decision tree having a set of leaf nodes. Entropy values corresponding with each leaf node of the set of leaf nodes are determined. Each entropy value indicates an extent of diversity of context associated with the corresponding leaf node. Additional data from the training dataset is selected based on the entropy values corresponding with the leaf nodes. The updated subset of data is used to generate an updated context model for use in performing compression of the image.

Actively-learned context modeling for image compression

An anomalous period of operation of a database management system is detected by analyzing a time series of data points indicating the number of database queries pending processing by the system. Conditions associated with execution of the pending database queries are recorded and analyzed to identify conditions correlated with the anomalous period of operation. A recommendation for tuning the database is generated based on analysis of the conditions.

Assisted database anomaly mitigation

A control system facilitates active management of a streaming data system. Given historical data traffic for each data stream processed by a streaming data system, the control system uses a machine learning model to predict future data traffic for each data stream. The control system selects a matching between data streams and servers for a future time that minimizes a cost comprising a switching cost and a server imbalance cost based on the predicted data traffic for the future time. In some configurations, the matching is selected using a planning window comprising a number of future time steps dynamically selected based on uncertainty associated with the predicted data traffic. Given the selected matching, the control system may manage the streaming data system by causing data streams to be moved between servers based on the matching.

Active control system for data stream allocation

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San Jose, CA, United States of America

Zhao Song