Results 11  20
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598
Probabilistic Independent Component Analysis
, 2003
"... Independent Component Analysis is becoming a popular exploratory method for analysing complex data such as that from FMRI experiments. The application of such 'modelfree' methods, however, has been somewhat restricted both by the view that results can be uninterpretable and by the lack of ..."
Abstract

Cited by 208 (13 self)
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of the number of intrinsic sources not only enables us to carry out probabilistic modelling, but also achieves an asymptotically unique decomposition of the data. This reduces problems of interpretation, as each final independent component is now much more likely to be due to only one physical or physiological
The Missing Link  A Probabilistic Model of Document Content and Hypertext Connectivity
, 2001
"... We describe a joint probabilistic model for modeling the contents and interconnectivity of document collections such as sets of web pages or research paper archives. The model is based on a probabilistic factor decomposition and allows identifying principal topics of the collection as well as autho ..."
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Cited by 218 (3 self)
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We describe a joint probabilistic model for modeling the contents and interconnectivity of document collections such as sets of web pages or research paper archives. The model is based on a probabilistic factor decomposition and allows identifying principal topics of the collection as well
Quantitative Robust Uncertainty Principles and Optimally Sparse Decompositions
, 2004
"... In this paper, we develop a robust uncertainty principle for finite signals in C N which states that for nearly all choices T, Ω ⊂ {0,..., N − 1} such that T  + Ω  ≍ (log N) −1/2 · N, there is no signal f supported on T whose discrete Fourier transform ˆ f is supported on Ω. In fact, we can mak ..."
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Cited by 181 (17 self)
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) is the unique sparsest possible decomposition (all other decompositions have more nonzero terms). In addition, if T  + Ω  ≤ Const · (log N) −1 · N, then the sparsest (α1, α2) can be found by solving a convex optimization problem. Underlying our results is a new probabilistic approach which insists
Comparative Study of Probabilistic Cell Decomposition And Probabilistic Roadmap
"... Abstract. This report looks at a new approach to motion planning known as Probabilistic Cell Decomposition (PCD). This approach combines ideas from Approximate Cell Decomposition (ACD) and Samplingbased motion planning to create a planner that can work in highdimensional static configuration space ..."
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Abstract. This report looks at a new approach to motion planning known as Probabilistic Cell Decomposition (PCD). This approach combines ideas from Approximate Cell Decomposition (ACD) and Samplingbased motion planning to create a planner that can work in highdimensional static configuration
Probabilistic methods in coloring and decomposition problems
 Discrete Math
, 1994
"... Numerous problems in Graph Theory and Combinatorics can be formulated in terms of the existence of certain colorings of graphs or hypergraphs. Many of these problems can be solved or partially solved by applying probabilistic arguments. In this paper we discuss several examples that illustrate the m ..."
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Cited by 6 (1 self)
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Numerous problems in Graph Theory and Combinatorics can be formulated in terms of the existence of certain colorings of graphs or hypergraphs. Many of these problems can be solved or partially solved by applying probabilistic arguments. In this paper we discuss several examples that illustrate
MULTILEVEL DECOMPOSITION OF PROBABILISTIC RELATIONS
"... Two methods of decomposition of probabilistic relations are presented. They consist of splitting relations (blocks) into pairs of smaller blocks related to each other by new variables generated in such a way as to minimize a cost function which depends on the size and structure of the result. The de ..."
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Cited by 1 (1 self)
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Two methods of decomposition of probabilistic relations are presented. They consist of splitting relations (blocks) into pairs of smaller blocks related to each other by new variables generated in such a way as to minimize a cost function which depends on the size and structure of the result
PROBABILISTIC METHODS FOR DECOMPOSITION DIMENSION OF GRAPHS
, 2003
"... In a graph G, the distance from an edge e to a set F ⊆ E(G) is the vertex distance from e to F in the line graph L(G). For a decomposition of E(G) into k sets, the distance vector of e is the ktuple of distances from e to these sets. The decomposition dimension dec(G) of G is the smallest k such t ..."
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such that G has a decomposition into k sets so that the distance vectors of the edges are distinct. For the complete graph Kn and the kdimensional hypercube Qk, we prove (2 − o(1)) lg n ≤ dec(Kn) ≤ (3.2 + o(1)) lg n and k / lg k ≤ dec(Qk) ≤ (3.17 + o(1))k / lg k. The upper bounds use probabilistic methods
Path planning using probabilistic cell decomposition
 in Proc. of the IEEE Int. Conf. on Robotics and Automation
, 2004
"... The problem of path planning occurs in many areas, such as computational biology, computer animations and computeraided design. It is of particular importance in the field of robotics. Here, the task is to find a feasible path/trajectory that the robot can follow from a start to a goal configuratio ..."
Abstract

Cited by 23 (0 self)
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presents a novel path planning method called Probabilistic Cell Decomposition (PCD). This approach combines the underlying method of cell decomposition with the concept of probabilistic sampling. The cell decomposition
Hierarchical probabilistic neural network language model
 In AISTATS
, 2005
"... In recent years, variants of a neural network architecture for statistical language modeling have been proposed and successfully applied, e.g. in the language modeling component of speech recognizers. The main advantage of these architectures is that they learn an embedding for words (or other symbo ..."
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Cited by 101 (4 self)
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. As an alternative to an importance sampling method proposed to speedup training, we introduce a hierarchical decomposition of the conditional probabilities that yields a speedup of about 200 both during training and recognition. The hierarchical decomposition is a binary hierarchical clustering constrained
Probabilistic topic decomposition of an eighteenthcentury american newspaper
 J. Am. Soc. Inf. Sci. Technol
, 2006
"... We use a probabilistic mixture decomposition method to determine topics in the Pennsylvania Gazette, a major colonial U.S. newspaper from 1728–1800. We assess the value of several topic decomposition techniques for historical research and compare the accuracy and efficacy of various methods. After d ..."
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Cited by 16 (1 self)
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We use a probabilistic mixture decomposition method to determine topics in the Pennsylvania Gazette, a major colonial U.S. newspaper from 1728–1800. We assess the value of several topic decomposition techniques for historical research and compare the accuracy and efficacy of various methods. After
Results 11  20
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598