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ICML
2010
IEEE
15 years 2 months ago
Learning Deep Boltzmann Machines using Adaptive MCMC
When modeling high-dimensional richly structured data, it is often the case that the distribution defined by the Deep Boltzmann Machine (DBM) has a rough energy landscape with man...
Ruslan Salakhutdinov
ICDM
2009
IEEE
142views Data Mining» more  ICDM 2009»
14 years 11 months ago
Building Classifiers with Independency Constraints
In this paper we study the problem of classifier learning where the input data contains unjustified dependencies between some data attributes and the class label. Such cases arise...
Toon Calders, Faisal Kamiran, Mykola Pechenizkiy
ICANN
2003
Springer
15 years 7 months ago
The Acquisition of New Categories through Grounded Symbols: An Extended Connectionist Model
Abstract. Solutions to the symbol grounding problem, in psychologically plausible cognitive models, have been based on hybrid connectionist/symbolic architectures, on robotic appro...
Alberto Greco, Thomas Riga, Angelo Cangelosi
125
Voted
DATE
2006
IEEE
102views Hardware» more  DATE 2006»
15 years 8 months ago
A systematic IP and bus subsystem modeling for platform-based system design
The topic on platform-based system modeling has received a great deal of attention today. One of the important tasks that significantly affect the effectiveness and efficiency of ...
Junhyung Um, Woo-Cheol Kwon, Sungpack Hong, Young-...
SODA
2010
ACM
201views Algorithms» more  SODA 2010»
15 years 11 months ago
Efficient Broadcast on Random Geometric Graphs
A Random Geometric Graph (RGG) in two dimensions is constructed by distributing n nodes independently and uniformly at random in [0, n ]2 and creating edges between every pair of...
Milan Bradonji, Robert Elsässer, Tobias Friedrich...