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JMLR
2008
188views more  JMLR 2008»
15 years 6 months ago
Maximal Causes for Non-linear Component Extraction
We study a generative model in which hidden causes combine competitively to produce observations. Multiple active causes combine to determine the value of an observed variable thr...
Jörg Lücke, Maneesh Sahani
ICML
2007
IEEE
16 years 7 months ago
Conditional random fields for multi-agent reinforcement learning
Conditional random fields (CRFs) are graphical models for modeling the probability of labels given the observations. They have traditionally been trained with using a set of obser...
Xinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanat...
ICIC
2007
Springer
16 years 19 days ago
Fuzzy Modeling Via On-Line Clustering and Support Vector Machine
Abstract. This paper describes a novel fuzzy rule-based modeling approach for some slow industrial processses. Structure identification is realized by clustering and support vecto...
Julio César Tovar, Wen Yu, Xiaoou Li
FCCM
2000
IEEE
103views VLSI» more  FCCM 2000»
15 years 11 months ago
A Networked FPGA-Based Hardware Implementation of a Neural Network Application
This paper describes a networked FPGA-based implementation of the FAST (Flexible Adaptable-Size Topology) architecture, a Arti cial Neural Network (ANN) that dynamically adapts it...
Héctor Fabio Restrepo, Ralph Hoffmann, Andr...
ICML
2010
IEEE
15 years 7 months ago
Deep networks for robust visual recognition
Deep Belief Networks (DBNs) are hierarchical generative models which have been used successfully to model high dimensional visual data. However, they are not robust to common vari...
Yichuan Tang, Chris Eliasmith