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Enabling Continual Learning with Differentiable Hebbian Plasticity
Continual learning is the problem of sequentially learning new tasks or ...
06/30/2020 ∙ by Vithursan Thangarasa, et al. ∙
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Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity
The impressive lifelong learning in animal brains is primarily enabled b...
02/24/2020 ∙ by Thomas Miconi, et al. ∙
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Learning to Continually Learn
Continual lifelong learning requires an agent or model to learn many seq...
02/21/2020 ∙ by Shawn Beaulieu, et al. ∙
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Estimating Q(s,s') with Deep Deterministic Dynamics Gradients
In this paper, we introduce a novel form of value function, Q(s, s'), th...
02/21/2020 ∙ by Ashley D. Edwards, et al. ∙
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First-Order Preconditioning via Hypergradient Descent
Standard gradient descent methods are susceptible to a range of issues t...
10/18/2019 ∙ by Ted Moskovitz, et al. ∙
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Differentiable plasticity: training plastic neural networks with backpropagation
How can we build agents that keep learning from experience, quickly and ...
04/06/2018 ∙ by Thomas Miconi, et al. ∙
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The impossibility of "fairness": a generalized impossibility result for decisions
Various measures can be used to estimate bias or unfairness in a predict...
07/05/2017 ∙ by Thomas Miconi, et al. ∙
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Learning to learn with backpropagation of Hebbian plasticity
Hebbian plasticity is a powerful principle that allows biological brains...
09/08/2016 ∙ by Thomas Miconi, et al. ∙
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Neural networks with differentiable structure
While gradient descent has proven highly successful in learning connecti...
06/20/2016 ∙ by Thomas Miconi, et al. ∙
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