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cture with application to high-dimensional pattern recognition. In Biologically Inspired Cognitive Architec- tures, 2009. AAAI Press, 2009. ARKO9. I. Arel, D. Rose, and T. Karnowski. A deep learning architecture comprising homogeneous cortical circuits for scalable spatiotemporal pattern inference. NIP
tions, this refers to a software server that performs "offline" learning tasks (e.g. using MOSES or hilIclimbing), and is in communica- tion with an Operational Agent Controller software server that performs real-time agent control and dispatches learning tasks to and receives results from the Learning Serve
Ami89. Daniel J. Amit. Modeling brain function - the world of attractor neural networks. Cambridge University Press, New York, USA, 1989. ARC09. I. Arel, D. Rose, and R. Coop. Destin: A scalable deep learning architecture with application to high-dimensional robust pattern recognition. Proc. AAA! Wor
tions, this refers to a software server that performs "offline" learning tasks (e.g. using MOSES or hilIclimbing), and is in communica- tion with an Operational Agent Controller software server that performs real-time agent control and dispatches learning tasks to and receives results from the Learning Serve
1. Ami89. Daniel J. Amit. Modeling brain function — the world of attractor neural networks. Cambridge University Press, New York, USA, 1989. ARCO9. I. Arel, D. Rose, and R. Coop. Destin: A scalable deep learning architecture with application to high-dimensional robust pattern recognition. Proc. AAAI Work
ations, this refers to a software server that performs “offline” learning tasks (e.g. using MOSES or hillclimbing), and is in communica- tion with an Operational Agent Controller software server that performs real-time agent control and dispatches learning tasks to and receives results from the Learning Server
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