Evolving Deep LSTM-based Memory Networks using an Information Maximization Objective

By Aditya Rawal, Risto Miikkulainen. 2016

47 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

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Citations in the corpus

Bayesian Neural Architecture Search using A Training-Free Performance Metric (2020, methodology): “For RNNs, NEAT-LSTM [39] and CoDeepNeat [30] extend NEAT to mitigate its limitations when evolving the topology and weights of the network.”
Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “Recurrent neural architectures based on long short-term memory (LSTM) allow very complex tasks to be solved through gradient descent training (Greff et al., 2015; Hochreiter and Schmidhuber, 1997) and have recently shown promise when combined with evolution (Rawal and Miikkulainen, 2016).”
Using ant colony optimization to optimize long short-term memory recurrent neural networks (2018, methodology): “More recent works by Miikkulainen, Rawal et al. have extended NEAT to evolve LSTM RNNs with success on a sequence recall task [18] and CoDeepNeat to utilize LSTM variants for image captioning [15].”
Investigating recurrent neural network memory structures using neuro-evolution (2019, background): “Other recent work by Rawal and Miikkulainen has investigated an information maximization objective [21] strategy to evolve RNNs.”
A Specialized Evolutionary Strategy Using Mean Absolute Error Random Sampling to Design Recurrent Neural Networks (2019, methodology): “Focusing on RNNs, NEAT-LSTM [34] and CoDeepNeat [35] extend NEAT to mitigate its limitations when evolving the topologies and weights of the network.”
Random error sampling-based recurrent neural network architecture optimization (2019, methodology): “Focusing on RNNs, NEAT-LSTM [33] and CoDeepNeat [34] extend NEAT to mitigate its limitations when evolving the topologies and weights of the network.”
Evolving Recurrent Neural Networks for Time Series Data Prediction of Coal Plant Parameters (2019, methodology): “Other more recent work by Rawal and Miikkulainen has utilized tree based encoding [21] and information maximization objectives [22] to evolve RNNs.”