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temporal_learning [2018/09/08 12:23]
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temporal_learning [2018/11/12 22:25] (current)
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 We demonstrate on a 1M time series Earth observation dataset that Proximity Forest retains this accuracy on datasets that are many orders of magnitude greater than those in the UCR repository, while learning its models at least 100,000 times faster than current state of the art models Elastic Ensemble and COTE. We demonstrate on a 1M time series Earth observation dataset that Proximity Forest retains this accuracy on datasets that are many orders of magnitude greater than those in the UCR repository, while learning its models at least 100,000 times faster than current state of the art models Elastic Ensemble and COTE.
  
 +https://​arxiv.org/​pdf/​1809.04423.pdf https://​github.com/​codeaudit/​neuronal_circuit_policies Re-purposing Compact Neuronal Circuit Policies to Govern Reinforcement
 +Learning Tasks
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 +https://​openreview.net/​forum?​id=BJl_VnR9Km A model cortical network for spatiotemporal sequence learning and prediction
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 +A new hierarchical cortical model for encoding spatiotemporal memory and video prediction
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 +The architecture includes feedforward,​ feedback, and local recurrent connections,​ which together implement a predictive coding scheme. Some versions of the network are shown to outperform the similar PredNet and PredRNN architectures on two video prediction tasks: moving MNIST and KTH human actions.