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Institution

Amazon.com

CompanySeattle, Washington, United States
About: Amazon.com is a company organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Service (business) & Service provider. The organization has 13363 authors who have published 17317 publications receiving 266589 citations.


Papers
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Patent
06 Nov 2012
TL;DR: In this paper, a candidate word for evaluation as a wake word that activates a natural language control functionality of a computing device is provided, which may include one or more words or sounds.
Abstract: Natural language controlled devices may be configured to activate command recognition in response to one or more wake words Techniques are provided to receive a candidate word for evaluation as a wake word that activates a natural language control functionality of a computing device The candidate word may include one or more words or sounds Values for multiple wake word metrics are then determined The candidate word is evaluated based on the various wake word metrics

67 citations

Patent
29 Mar 2012
TL;DR: In this article, a storage controller is implemented for controlling a storage system and includes components for servicing client data requests based on the characteristics of the distributed computer system, the client, or the data requests.
Abstract: A storage controller is implemented for controlling a storage system. The storage controller may be implemented using a distributed computer system and may include components for servicing client data requests based on the characteristics of the distributed computer system, the client, or the data requests. The storage controller is scalable independently of the storage system it controls. All components of the storage controller, as well as the client, may be virtual or hardware-based instances of a distributed computer system.

67 citations

Patent
31 Mar 2015
TL;DR: In this paper, a variable stripe size selection policy is used to determine the size of a particular stripe of storage space to be allocated for the storage object, which differs from the size allocated earlier for the same storage object.
Abstract: A write request directed to a storage object is received at a distributed file storage service. Based on a variable stripe size selection policy, a size of a particular stripe of storage space to be allocated for the storage object is determined, which differs from the size of another stripe allocated earlier for the same storage object. Allocation of storage for the particular stripe at a particular storage device is requested, and if the allocation succeeds, the contents of the storage device are modified in accordance with the write request.

67 citations

Patent
31 Mar 2005
TL;DR: In this paper, the authors describe a method and apparatus for measuring true end-to-end latency for calls to Web services, where a Web service client and a web service provider collaborate to collect timing/latency data for calls.
Abstract: Method and apparatus for measuring true end-to-end latency for calls to Web services are described. In embodiments, a Web service client and a Web service provider may collaborate to collect timing/latency data for calls to the Web service. This data may be collected, stored, and analyzed by a latency measurement service to generate displays and/or reports on true end-to-end latency measurements for Web service calls. Embodiments may collect Internet/network infrastructure latency for Web service calls up to and including the “last mile” to the Web service client and the Web service processing time. Additionally, by analyzing latency data collected from a number of Web services clients and/or Web service providers, embodiments may provide a macro-level view into overall Internet performance. In one embodiment, the latency measurement service may be a Web service.

67 citations

Posted Content
TL;DR: In this paper, the authors propose a plan online and learn offline (POLO) framework for the setting where an agent, with an internal model, needs to continually act and learn in the world.
Abstract: We propose a plan online and learn offline (POLO) framework for the setting where an agent, with an internal model, needs to continually act and learn in the world. Our work builds on the synergistic relationship between local model-based control, global value function learning, and exploration. We study how local trajectory optimization can cope with approximation errors in the value function, and can stabilize and accelerate value function learning. Conversely, we also study how approximate value functions can help reduce the planning horizon and allow for better policies beyond local solutions. Finally, we also demonstrate how trajectory optimization can be used to perform temporally coordinated exploration in conjunction with estimating uncertainty in value function approximation. This exploration is critical for fast and stable learning of the value function. Combining these components enable solutions to complex simulated control tasks, like humanoid locomotion and dexterous in-hand manipulation, in the equivalent of a few minutes of experience in the real world.

67 citations


Authors

Showing all 13498 results

NameH-indexPapersCitations
Jiawei Han1681233143427
Bernhard Schölkopf1481092149492
Christos Faloutsos12778977746
Alexander J. Smola122434110222
Rama Chellappa120103162865
William F. Laurance11847056464
Andrew McCallum11347278240
Michael J. Black11242951810
David Heckerman10948362668
Larry S. Davis10769349714
Chris M. Wood10279543076
Pietro Perona10241494870
Guido W. Imbens9735264430
W. Bruce Croft9742639918
Chunhua Shen9368137468
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20234
2022168
20212,015
20202,596
20192,002
20181,189