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» From Algorithmic to Subjective Randomness
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138
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COLT
1992
Springer
15 years 7 months ago
Language Learning from Stochastic Input
Language learning from positive data in the Gold model of inductive inference is investigated in a setting where the data can be modeled as a stochastic process. Specifically, the...
Shyam Kapur, Gianfranco Bilardi
ASPDAC
2009
ACM
115views Hardware» more  ASPDAC 2009»
15 years 10 months ago
Incremental and on-demand random walk for iterative power distribution network analysis
— Power distribution networks (PDNs) are designed and analyzed iteratively. Random walk is among the most efficient methods for PDN analysis. We develop in this paper an increme...
Yiyu Shi, Wei Yao, Jinjun Xiong, Lei He
MICCAI
2002
Springer
16 years 4 months ago
Recognizing Deviations from Normalcy for Brain Tumor Segmentation
A framework is proposed for the segmentation of brain tumors from MRI. Instead of training on pathology, the proposed method trains exclusively on healthy tissue. The algorithm att...
David T. Gering, W. Eric L. Grimson, Ron Kikinis
118
Voted
FOCS
2007
IEEE
15 years 10 months ago
Lower Bounds on Signatures From Symmetric Primitives
We show that every construction of one-time signature schemes from a random oracle achieves black-box security at most 2(1+o(1))q , where q is the total number of oracle queries a...
Boaz Barak, Mohammad Mahmoody-Ghidary
STOC
2009
ACM
167views Algorithms» more  STOC 2009»
16 years 4 months ago
Universally utility-maximizing privacy mechanisms
A mechanism for releasing information about a statistical database with sensitive data must resolve a trade-off between utility and privacy. Publishing fully accurate information ...
Arpita Ghosh, Tim Roughgarden, Mukund Sundararajan