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temporal_learning [2018/04/27 11:39]
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temporal_learning [2018/11/12 22:25]
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 https://​arxiv.org/​abs/​1703.06846v3 Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions https://​arxiv.org/​abs/​1703.06846v3 Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions
  
 +https://​arxiv.org/​abs/​1808.04063 Time Perception Machine: Temporal Point Processes for the When, Where and What of Activity Prediction
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 + We propose an integrated framework of neural networks and temporal point processes for predicting when the next activity will happen. Because point processes are limited to taking event frames as input, we propose a simple yet effective mechanism to extract features at frames of interest while also preserving the rich information in the remaining frames. We evaluate our model on two challenging datasets. The results show that our model outperforms traditional statistical point process approaches significantly,​ demonstrating its effectiveness in capturing the underlying temporal dynamics as well as the correlation within sequential activities. Furthermore,​ we also extend our model to a joint estimation framework for predicting the timing, spatial location, and category of the activity simultaneously,​ to answer the when, where, and what of activity prediction.
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 +https://​arxiv.org/​abs/​1802.04687 Neural Relational Inference for Interacting Systems
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 +https://​arxiv.org/​abs/​1808.10594 Proximity Forest: An effective and scalable distance-based classifier for time series
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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.
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 +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.