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Institution

Carnegie Mellon University

EducationPittsburgh, Pennsylvania, United States
About: Carnegie Mellon University is a education organization based out in Pittsburgh, Pennsylvania, United States. It is known for research contribution in the topics: Population & Robot. The organization has 36317 authors who have published 104359 publications receiving 5975734 citations. The organization is also known as: CMU & Carnegie Mellon.


Papers
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Proceedings Article
03 Jul 2018

1,094 citations

Journal ArticleDOI
TL;DR: In this article, the authors review studies conducted by themselves and coauthors that document a "self-serving" bias in judgments of fairness and demonstrate that the bias is an important cause of impasse in negotiations.
Abstract: The authors review studies conducted by themselves and coauthors that document a 'self-serving' bias in judgments of fairness and demonstrate that the bias is an important cause of impasse in negotiations. They discuss experimental evidence showing that (1) the bias causes impasse; (2) it is possible to reduce impasses by debiasing bargainers; and (3) the bias results from selective evaluation of information. The authors also review results from a field study of negotiations between teachers' unions and school boards in Pennsylvania that both document the fairness bias in a naturalistic setting and demonstrates its impact on strikes.

1,092 citations

Book ChapterDOI
08 Sep 2018
TL;DR: This paper proposes AutoML for Model Compression (AMC) which leverages reinforcement learning to efficiently sample the design space and can improve the model compression quality and achieves state-of-the-art model compression results in a fully automated way without any human efforts.
Abstract: Model compression is an effective technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets. Conventional model compression techniques rely on hand-crafted features and require domain experts to explore the large design space trading off among model size, speed, and accuracy, which is usually sub-optimal and time-consuming. In this paper, we propose AutoML for Model Compression (AMC) which leverages reinforcement learning to efficiently sample the design space and can improve the model compression quality. We achieved state-of-the-art model compression results in a fully automated way without any human efforts. Under 4\(\times \) FLOPs reduction, we achieved 2.7% better accuracy than the hand-crafted model compression method for VGG-16 on ImageNet. We applied this automated, push-the-button compression pipeline to MobileNet-V1 and achieved a speedup of 1.53\(\times \) on the GPU (Titan Xp) and 1.95\(\times \) on an Android phone (Google Pixel 1), with negligible loss of accuracy.

1,086 citations

Book
30 Aug 1993
TL;DR: Part 1 Systems: Pygmalion tinker a predictive calculator rehearsal world smallStar peridot metamouse TELS eager garnet the Turvy experience chimera the geometer's sketchpad tourmaline a history-based macro by example system mondrian triggers the AIDE project.
Abstract: Part 1 Systems: Pygmalion tinker a predictive calculator rehearsal world smallStar peridot metamouse TELS eager garnet the Turvy experience chimera the geometer's sketchpad tourmaline a history-based macro by example system mondrian triggers the AIDE project. Part 2 Components: a history of editable graphical histories graphical representation and feedback in a PBD system PBD invocation techniques a system-wide macro facility based on aggregate events making programming accessible to visual problem solvers using voice input to disambiguate intent. Part 3 Perspectives: characterizing PBD systems demonstrational interfaces just-in-time programming.

1,083 citations

Journal ArticleDOI
TL;DR: The design and implementation of Coda, a file system for a large-scale distributed computing environment composed of Unix workstations, is described, which provides resiliency to server and network failures through the use of two distinct but complementary mechanisms.
Abstract: The design and implementation of Coda, a file system for a large-scale distributed computing environment composed of Unix workstations, is described. It provides resiliency to server and network failures through the use of two distinct but complementary mechanisms. One mechanism, server replication, stores copies of a file at multiple servers. The other mechanism, disconnected operation, is a mode of execution in which a caching site temporarily assumes the role of a replication site. The design of Coda optimizes for availability and performance and strives to provide the highest degree of consistency attainable in the light of these objectives. Measurements from a prototype show that the performance cost of providing high availability in Coda is reasonable. >

1,083 citations


Authors

Showing all 36645 results

NameH-indexPapersCitations
Yi Chen2174342293080
Rakesh K. Jain2001467177727
Robert C. Nichol187851162994
Michael I. Jordan1761016216204
Jasvinder A. Singh1762382223370
J. N. Butler1722525175561
P. Chang1702154151783
Krzysztof Matyjaszewski1691431128585
Yang Yang1642704144071
Geoffrey E. Hinton157414409047
Herbert A. Simon157745194597
Yongsun Kim1562588145619
Terrence J. Sejnowski155845117382
John B. Goodenough1511064113741
Scott Shenker150454118017
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023120
2022499
20214,980
20205,375
20195,420
20184,972