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2002.10585
Cited By
Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity
International Conference on Learning Representations (ICLR), 2018
24 February 2020
Thomas Miconi
Aditya Rawal
Jeff Clune
Kenneth O. Stanley
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Papers citing
"Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity"
50 / 60 papers shown
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Theory of coupled neuronal-synaptic dynamics
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Hebbian and Gradient-based Plasticity Enables Robust Memory and Rapid Learning in RNNs
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Meta-Learning Biologically Plausible Plasticity Rules with Random Feedback Pathways
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A Modern Self-Referential Weight Matrix That Learns to Modify Itself
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Meta-Reinforcement Learning with Self-Modifying Networks
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Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-Learning
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The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning
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Linear Transformers Are Secretly Fast Weight Programmers
International Conference on Machine Learning (ICML), 2021
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Meta Learning Backpropagation And Improving It
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Learning Associative Inference Using Fast Weight Memory
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Learning Not to Learn: Nature versus Nurture in Silico
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Meta-Learning with Sparse Experience Replay for Lifelong Language Learning
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LoCo: Local Contrastive Representation Learning
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Neural Information Processing Systems (NeurIPS), 2020
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309
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