Trajectory modification of recurrent neural networks is a training algorithm that modifies both the network representations in each time step and the common weight matrix.
Trajectory modification of recurrent neural networks is a training algorithm that modifies both the network representations in each time step and the common ...
Traj ectory modification of recurrent neur al networks is a training algorithm that modifies both th e network representat ions in each tim e ste p and th e ...
Nov 27, 2019 · This paper introduces a new method to train recurrent neural networks using dynamical trajectory-based optimization.
Abstract—This paper introduces a new method to train recurrent neural networks using dynamical trajectory-based optimization.
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Sep 23, 2019 · The presented work demonstrates the training of recurrent neural networks (RNNs) from distributions of atom coordinates in solid state ...
We will demonstrate the inefficiency of directly adopting RNN on trajectory modeling via theoreti- cal proof, and propose two new models. Second, we conduct.
Abstract. This paper introduces a new method to train recurrent neural networks using dynamical trajectory-based optimization.
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In the first training step, the RNN model is used to predict trajectories of objects for shorter time horizons (e.g., less than 1 second such as 100 ms, 200 ms, ...
The Minimal Trajectory (MINT) algorithm for training recurrent neural networks with a stable end point is based on an algorithmic search for the systems' ...