16 citations · 62 across the 13 of their papers we have counts for
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cs.LG2018★ 4 cited
Information Maximizing Exploration with a Latent Dynamics Model
Trevor Barron, Oliver Obst, Heni Ben Amor
All reinforcement learning algorithms must handle the trade-off between exploration and exploitation. Many state-of-the-art deep reinforcement learning methods use noise in the act…
cs.LG2018★ 3 cited
Efficient Time-Series Approximation with Linear Recurrent Neural Networks: Architecture Learning and Predictive Power
Frieder Stolzenburg, Sandra Litz, Olivia Michael +1
Recurrent neural networks are a powerful means to cope with time series. We show how autoregressive linear, i.e., linearly activated recurrent neural networks (LRNNs) can approxima…