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Minimum Probability Flow Learning
"... Fitting probabilistic models to data is often difficult, due to the general intractability of the partition function and its derivatives. Here we propose a new parameter estimation technique that does not require computing an intractable normalization factor or sampling from the equilibrium distrib ..."
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for an infinitesimal time. Score matching, minimum velocity learning, and certain forms of contrastive divergence are shown to be special cases of this learning technique. We demonstrate parameter estimation in Ising models, deep belief networks and a product of Studentt test model of natural scenes. In the Ising
Minimum Probability of Error Image Retrieval
 IEEE Trans. Signal Processing
"... Abstract—We address the design of optimal architectures for image retrieval from large databases. Minimum probability of error (MPE) is adopted as the optimality criterion and retrieval formulated as a problem of statistical classification. The probability of retrieval error is lower and upperboun ..."
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Cited by 25 (14 self)
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Abstract—We address the design of optimal architectures for image retrieval from large databases. Minimum probability of error (MPE) is adopted as the optimality criterion and retrieval formulated as a problem of statistical classification. The probability of retrieval error is lower and upper
Approximating discrete probability distributions with dependence trees
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 1968
"... A method is presented to approximate optimally an ndimensional discrete probability distribution by a product of secondorder distributions, or the distribution of the firstorder tree dependence. The problem is to find an optimum set of n1 first order dependence relationship among the n variables ..."
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Cited by 881 (0 self)
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A method is presented to approximate optimally an ndimensional discrete probability distribution by a product of secondorder distributions, or the distribution of the firstorder tree dependence. The problem is to find an optimum set of n1 first order dependence relationship among the n
Universal Coding with Minimum Probability of Codeword Length Overflow
 IEEE Trans. Information Theory
, 1991
"... AbstractLossless blocktovariable length source coding is studied for finitestate, finitealphabet sources. We aim to minimize the probability that the normalized length of the codeword will exceed a given threshold B, subject to the Kraft inequality. It is shown that the LempelZiv (LZ) algorit ..."
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Cited by 24 (3 self)
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AbstractLossless blocktovariable length source coding is studied for finitestate, finitealphabet sources. We aim to minimize the probability that the normalized length of the codeword will exceed a given threshold B, subject to the Kraft inequality. It is shown that the LempelZiv (LZ
Sayeed, “Minimum probability of error in sparse wideband channels
 in Allerton Conference on Communication, Control and Computing
, 2006
"... Abstract — This paper studies the impact of channel coherence and the role of channel state information (CSI) on the probability of error in wideband multipath fading channels. Inspired by recent ultra wideband channel measurement campaigns, we propose a sparse channel model for time and frequency s ..."
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Cited by 2 (1 self)
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Abstract — This paper studies the impact of channel coherence and the role of channel state information (CSI) on the probability of error in wideband multipath fading channels. Inspired by recent ultra wideband channel measurement campaigns, we propose a sparse channel model for time and frequency
Minimum Probability Flow Learning Jascha SohlDicksteinab ∗
"... Fitting probabilistic models to data is often difficult, due to the general intractability of the partition function and its derivatives. Here we propose a new parameter estimation technique that does not require computing an intractable normalization factor or sampling from the equilibrium distribu ..."
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for an infinitesimal time. Score matching, minimum velocity learning, and certain forms of contrastive divergence are shown to be special cases of this learning technique. We demonstrate parameter estimation in Ising models, deep belief networks and an independent component analysis model of natural scenes
Fast Folding and Comparison of RNA Secondary Structures (The Vienna RNA Package)
"... Computer codes for computation and comparison of RNA secondary structures, the Vienna RNA package, are presented, that are based on dynamic programming algorithms and aim at predictions of structures with minimum free energies as well as at computations of the equilibrium partition functions and bas ..."
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Cited by 809 (117 self)
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Computer codes for computation and comparison of RNA secondary structures, the Vienna RNA package, are presented, that are based on dynamic programming algorithms and aim at predictions of structures with minimum free energies as well as at computations of the equilibrium partition functions
A Fast Quantum Mechanical Algorithm for Database Search
 ANNUAL ACM SYMPOSIUM ON THEORY OF COMPUTING
, 1996
"... Imagine a phone directory containing N names arranged in completely random order. In order to find someone's phone number with a probability of , any classical algorithm (whether deterministic or probabilistic)
will need to look at a minimum of names. Quantum mechanical systems can be in a supe ..."
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Cited by 1135 (10 self)
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Imagine a phone directory containing N names arranged in completely random order. In order to find someone's phone number with a probability of , any classical algorithm (whether deterministic or probabilistic)
will need to look at a minimum of names. Quantum mechanical systems can be in a
Critical Power for Asymptotic Connectivity in Wireless Networks
, 1998
"... : In wireless data networks each transmitter's power needs to be high enough to reach the intended receivers, while generating minimum interference on other receivers sharing the same channel. In particular, if the nodes in the network are assumed to cooperate in routing each others ' pack ..."
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Cited by 541 (19 self)
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: In wireless data networks each transmitter's power needs to be high enough to reach the intended receivers, while generating minimum interference on other receivers sharing the same channel. In particular, if the nodes in the network are assumed to cooperate in routing each others &apos
Exact Matrix Completion via Convex Optimization
, 2008
"... We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at random from a matrix M. Can we complete the matrix and recover the entries that we have not seen? We show that one can perfe ..."
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Cited by 873 (26 self)
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perfectly recover most lowrank matrices from what appears to be an incomplete set of entries. We prove that if the number m of sampled entries obeys m ≥ C n 1.2 r log n for some positive numerical constant C, then with very high probability, most n × n matrices of rank r can be perfectly recovered
Results 1  10
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