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Found 3 papers, 2 papers with code
Biologically-Inspired Continual Learning of Human Motion Sequences
This work proposes a model for continual learning on tasks involving temporal sequences, specifically, human motions.
Recurrent Neural Networks With Limited Numerical Precision
Recurrent Neural Networks (RNNs) produce state-of-art performance on many machine learning tasks but their demand on resources in terms of memory and computational power are often high.
Recurrent Neural Networks With Limited Numerical Precision
We present results from the use of different stochastic and deterministic reduced precision training methods applied to three major RNN types which are then tested on several datasets.
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