• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Advanced Search Include Citations

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 1,585,208
Next 10 →

Data Streams: Algorithms and Applications

by S. Muthukrishnan , 2005
"... In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerg ..."
Abstract - Cited by 538 (22 self) - Add to MetaCart
In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has

Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer

by Peter W. Shor - SIAM J. on Computing , 1997
"... A digital computer is generally believed to be an efficient universal computing device; that is, it is believed able to simulate any physical computing device with an increase in computation time by at most a polynomial factor. This may not be true when quantum mechanics is taken into consideration. ..."
Abstract - Cited by 1268 (5 self) - Add to MetaCart
quantum computer. These algorithms take a number of steps polynomial in the input size, e.g., the number of digits of the integer to be factored.

Simulating Physics with Computers

by Richard Feynman, Peter W. Shor - SIAM Journal on Computing , 1982
"... A digital computer is generally believed to be an efficient universal computing device; that is, it is believed able to simulate any physical computing device with an increase in computation time of at most a polynomial factor. This may not be true when quantum mechanics is taken into consideration. ..."
Abstract - Cited by 601 (1 self) - Add to MetaCart
computer. These algorithms take a number of steps polynomial in the input size, e.g., the number of digits of the integer to be factored. AMS subject classifications: 82P10, 11Y05, 68Q10. 1 Introduction One of the first results in the mathematics of computation, which underlies the subsequent development

Algorithms for Quantum Computation: Discrete Logarithms and Factoring

by Peter W. Shor , 1994
"... A computer is generally considered to be a universal computational device; i.e., it is believed able to simulate any physical computational device with a increase in computation time of at most a polynomial factor. It is not clear whether this is still true when quantum mechanics is taken into consi ..."
Abstract - Cited by 1103 (7 self) - Add to MetaCart
of steps which is polynomial in the input size, e.g., the number of digits of the integer to be factored. These two problems are generally considered hard on a classical computer and have been used as the basis of several proposed cryptosystems. (We thus give the first examples of quantum cryptanalysis.) 1

Hiding the Input-Size in Secure Two-Party Computation ∗

by Yehuda Lindell, Kobbi Nissim, Claudio Orl
"... In the setting of secure multiparty computation, a set of parties wish to compute a joint function of their inputs, while preserving properties like privacy, correctness, and independence of inputs. One security property that has typically not been considered in the past relates to the length or siz ..."
Abstract - Add to MetaCart
or size of the parties inputs. This is despite the fact that in many cases the size of a party’s input can be confidential. The reason for this omission seems to have been the folklore belief that, as with encryption, it is impossible to carry out non-trivial secure computation while hiding the size

The Role of Input Size and Generativity in Simulating Language Acquisition

by Daniel Freudenthal, Julian Pine, Fernand Gobet
"... This paper presents an analysis of the role of input size and generativity (ability to produce novel utterances) in simulating developmental data on a phenomenon in first language acquisition. An existing model that has already simulated the basic phenomenon is trained on input sets of varying sizes ..."
Abstract - Cited by 2 (2 self) - Add to MetaCart
This paper presents an analysis of the role of input size and generativity (ability to produce novel utterances) in simulating developmental data on a phenomenon in first language acquisition. An existing model that has already simulated the basic phenomenon is trained on input sets of varying

Multivariable Feedback Control: Analysis

by Sigurd Skogestad, Ian Postlethwaite - span (B∗) und Basis B∗ = { ω1 , 2005
"... multi-input, multi-output feed-back control design for linear systems using the paradigms, theory, and tools of robust con-trol that have arisen during the past two decades. The book is aimed at graduate students and practicing engineers who have a basic knowledge of classical con-trol design and st ..."
Abstract - Cited by 529 (24 self) - Add to MetaCart
multi-input, multi-output feed-back control design for linear systems using the paradigms, theory, and tools of robust con-trol that have arisen during the past two decades. The book is aimed at graduate students and practicing engineers who have a basic knowledge of classical con-trol design

Fuzzy extractors: How to generate strong keys from biometrics and other noisy data. Technical Report 2003/235, Cryptology ePrint archive, http://eprint.iacr.org, 2006. Previous version appeared at EUROCRYPT 2004

by Yevgeniy Dodis, Rafail Ostrovsky, Leonid Reyzin, Adam Smith - 34 [DRS07] [DS05] [EHMS00] [FJ01] Yevgeniy Dodis, Leonid Reyzin, and Adam , 2004
"... We provide formal definitions and efficient secure techniques for • turning noisy information into keys usable for any cryptographic application, and, in particular, • reliably and securely authenticating biometric data. Our techniques apply not just to biometric information, but to any keying mater ..."
Abstract - Cited by 532 (38 self) - Add to MetaCart
material that, unlike traditional cryptographic keys, is (1) not reproducible precisely and (2) not distributed uniformly. We propose two primitives: a fuzzy extractor reliably extracts nearly uniform randomness R from its input; the extraction is error-tolerant in the sense that R will be the same even

Fast texture synthesis using tree-structured vector quantization

by Li-yi Wei, Marc Levoy , 2000
"... Figure 1: Our texture generation process takes an example texture patch (left) and a random noise (middle) as input, and modifies this random noise to make it look like the given example texture. The synthesized texture (right) can be of arbitrary size, and is perceived as very similar to the given ..."
Abstract - Cited by 562 (12 self) - Add to MetaCart
Figure 1: Our texture generation process takes an example texture patch (left) and a random noise (middle) as input, and modifies this random noise to make it look like the given example texture. The synthesized texture (right) can be of arbitrary size, and is perceived as very similar to the given

The cascade-correlation learning architecture

by Scott E. Fahlman, Christian Lebiere - Advances in Neural Information Processing Systems 2 , 1990
"... Cascade-Correlation is a new architecture and supervised learning algorithm for artificial neural networks. Instead of just adjusting the weights in a network of fixed topology, Cascade-Correlation begins with a minimal network, then automatically trains and adds new hidden units one by one, creatin ..."
Abstract - Cited by 796 (6 self) - Add to MetaCart
, creating a multi-layer structure. Once a new hidden unit has been added to the network, its input-side weights are frozen. This unit then becomes a permanent feature-detector in the network, available for producing outputs or for creating other, more complex feature detectors. The Cascade
Next 10 →
Results 1 - 10 of 1,585,208
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University