103 citations · 355 across the 24 of their papers we have counts for
Showing 2018 · cs.LGShow all
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cs.LG2018★ 103 cited
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference
Matthew Riemer, Ignacio Cases, Robert Ajemian +4
Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neural network learning to more human realist…
cs.LG2018★ 40 cited
Learning Abstract Options
Matthew Riemer, Miao Liu, Gerald Tesauro
Building systems that autonomously create temporal abstractions from data is a key challenge in scaling learning and planning in reinforcement learning. One popular approach for ad…