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Universal Recurrent Event Memories for Streaming Data
In this paper, we propose a new event memory architecture (MemNet) for recurrent neural networks, which is universal for different types of time series data such as scalar, multivariate or symbolic.
Dynamic Analysis and an Eigen Initializer for Recurrent Neural Networks
We propose a new perspective to analyze the hidden state space based on an eigen decomposition of the weight matrix.
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