Institution
Amazon.com
Company•Seattle, 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 published on a yearly basis
Papers
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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
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29 Mar 2012TL;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
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31 Mar 2015TL;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
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31 Mar 2005TL;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
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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
Name | H-index | Papers | Citations |
---|---|---|---|
Jiawei Han | 168 | 1233 | 143427 |
Bernhard Schölkopf | 148 | 1092 | 149492 |
Christos Faloutsos | 127 | 789 | 77746 |
Alexander J. Smola | 122 | 434 | 110222 |
Rama Chellappa | 120 | 1031 | 62865 |
William F. Laurance | 118 | 470 | 56464 |
Andrew McCallum | 113 | 472 | 78240 |
Michael J. Black | 112 | 429 | 51810 |
David Heckerman | 109 | 483 | 62668 |
Larry S. Davis | 107 | 693 | 49714 |
Chris M. Wood | 102 | 795 | 43076 |
Pietro Perona | 102 | 414 | 94870 |
Guido W. Imbens | 97 | 352 | 64430 |
W. Bruce Croft | 97 | 426 | 39918 |
Chunhua Shen | 93 | 681 | 37468 |