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JMLR
2012
11 years 11 months ago
Deep Boltzmann Machines as Feed-Forward Hierarchies
The deep Boltzmann machine is a powerful model that extracts the hierarchical structure of observed data. While inference is typically slow due to its undirected nature, we argue ...
Grégoire Montavon, Mikio L. Braun, Klaus-Ro...
NN
2000
Springer
170views Neural Networks» more  NN 2000»
13 years 8 months ago
Synthetic brain imaging: grasping, mirror neurons and imitation
The article contributes to the quest to relate global data on brain and behavior (e.g. from PET, Positron Emission Tomography, and fMRI, functional Magnetic Resonance Imaging) to ...
Michael A. Arbib, Aude Billard, Marco Iacoboni, Er...
MICAI
2004
Springer
14 years 1 months ago
A Biologically Motivated and Computationally Efficient Natural Language Processor
Abstract. Conventional artificial neural network models lack many physiological properties of the neuron. Current learning algorithms are more concerned to computational performanc...
João Luís Garcia Rosa
DCOSS
2006
Springer
14 years 8 days ago
Evaluating Local Contributions to Global Performance in Wireless Sensor and Actuator Networks
Wireless sensor networks are often studied with the goal of removing information from the network as efficiently as possible. However, when the application also includes an actuato...
Christopher J. Rozell, Don H. Johnson
SOCIALCOM
2010
13 years 6 months ago
A Methodology for Integrating Network Theory and Topic Modeling and its Application to Innovation Diffusion
Text data pertaining to socio-technical networks often are analyzed separately from relational data, or are reduced to the fact and strength of the flow of information between node...
Jana Diesner, Kathleen M. Carley