Results 1  10
of
973
A Unifying Review of Linear Gaussian Models
, 1999
"... Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, Kalman filter models, and hidden Markov models can all be unified as variations of unsupervised learning under a single basic generative model. This is achieved by collecting together disparate observa ..."
Abstract

Cited by 351 (18 self)
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Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, Kalman filter models, and hidden Markov models can all be unified as variations of unsupervised learning under a single basic generative model. This is achieved by collecting together disparate
Bethe Ansatz for Quantum Strings
, 2004
"... We propose Bethe equations for the diagonalization of the Hamiltonian of quantum strings on AdS5×S 5 at large string tension and restricted to certain large charge states from a closed su(2) subsector. The ansatz differs from the recently proposed allloop gauge theory asymptotic Bethe ansatz by add ..."
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Cited by 281 (16 self)
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by additional factorized scattering terms for the local excitations. We also show that our ansatz quantitatively reproduces everything that is currently known about the string spectrum of these states. Firstly, by construction, we recover the integral Bethe equations describing semiclassical spinning strings
Factors affecting the zwidth of a haptic display
 in Proceedings of the IEEE International Conference on Robotics and Automation
, 1994
"... This paper addresses the performance of forcereflecting interfaces (“haptic displays”). We suggest that an important measure of performance is the dynamic range of achievable impedances — “ZWidth ” — and that an impedance is achievable if it satisfies a robustness property such as passivity. Seve ..."
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Cited by 178 (7 self)
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. Several factors affecting ZWidth — sampleandhold, inherent interface dynamics, displacement sensor quantization, and velocity filtering — are discussed. A set of experiments designed to evaluate these factors is described, and experimental results are presented. A striking result is that inherent
Generalized Relevance Learning Vector Quantization
 Neural Networks
, 2002
"... We propose a new scheme for enlarging generalized learning vector quantization (GLVQ) with weighting factors for the input dimensions. The factors allow an appropriate scaling of the input dimensions according to their relevance. They are adapted automatically during training according to the specif ..."
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Cited by 68 (23 self)
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We propose a new scheme for enlarging generalized learning vector quantization (GLVQ) with weighting factors for the input dimensions. The factors allow an appropriate scaling of the input dimensions according to their relevance. They are adapted automatically during training according
Factorization in Quantized Weyl Algebras
, 2005
"... (F) is a noncommutative ring generated by variables x, y such that yx = qxy + 1. We study factorization in A q 1 (F) and consider irreducible and prime elements. We classify irreducible quadratic forms and identify two important classes. A result on Ore extensions is needed to describe prime quadrat ..."
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(F) is a noncommutative ring generated by variables x, y such that yx = qxy + 1. We study factorization in A q 1 (F) and consider irreducible and prime elements. We classify irreducible quadratic forms and identify two important classes. A result on Ore extensions is needed to describe prime
QUANTIZED RANK R MATRICES
, 2001
"... First some old as well as new results about P.I. algebras, Ore extensions, and degrees are presented. Then quantized n × r matrices as well as certain quantized factor algebras M r+1 q (n) of Mq(n) are analyzed. For r = 1,...,n − 1, Mr+1 q (n) is the quantized function algebra of rank r matrices ob ..."
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Cited by 2 (1 self)
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First some old as well as new results about P.I. algebras, Ore extensions, and degrees are presented. Then quantized n × r matrices as well as certain quantized factor algebras M r+1 q (n) of Mq(n) are analyzed. For r = 1,...,n − 1, Mr+1 q (n) is the quantized function algebra of rank r matrices
QUANTIZED RANK R MATRICES
, 2011
"... First some old as well as new results about P.I. algebras, Ore extensions, and degrees are presented. Then quantized n × r matrices as well as certain quantized factor algebras M r+1 q (n) of Mq(n) are analyzed. For r = 1,..., n − 1, M r+1 q (n) is the quantized function algebra of rank r matrices ..."
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First some old as well as new results about P.I. algebras, Ore extensions, and degrees are presented. Then quantized n × r matrices as well as certain quantized factor algebras M r+1 q (n) of Mq(n) are analyzed. For r = 1,..., n − 1, M r+1 q (n) is the quantized function algebra of rank r matrices
TOPOLOGY AND QUANTIZATION ∗
, 1997
"... A simple algebraic model of charged particle coupled to singular magnetic field is given. Quantization is described as gradation by certain abelian group G. Statistics is determined by a commutation factor λ on the grading group G. Composite fermions and composite bosons are described in an unified ..."
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A simple algebraic model of charged particle coupled to singular magnetic field is given. Quantization is described as gradation by certain abelian group G. Statistics is determined by a commutation factor λ on the grading group G. Composite fermions and composite bosons are described in an unified
Quantization conditions and . . .
"... The partition function of ABJ(M) theories on the threesphere can be regarded as the canonical partition function of an ideal Fermi gas with a nontrivial Hamiltonian. We propose an exact expression for the spectral determinant of this Hamiltonian, which generalizes recent results obtained in the ma ..."
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in the maximally supersymmetric case. As a consequence, we find an exact WKB quantization condition determining the spectrum which is in agreement with numerical results. In addition, we investigate the factorization properties and functional equations for our conjectured spectral determinants. These functional
Multiple Cause Vector Quantization
 Advances in Neural Information Processing Systems 15
, 2003
"... We propose a model that can learn partsbased representations of highdimensional data. Our key assumption is that the dimensions of the data can be separated into several disjoint subsets, or factors, which take on values independently of each other. We assume each factor has a small number of d ..."
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Cited by 13 (3 self)
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of discrete states, and model it using a vector quantizer. The selected states of each factor represent the multiple causes of the input.
Results 1  10
of
973