213 citations · 954 across the 24 of their papers we have counts for
4 papers · 1 filter
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…
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…
Diverse Few-Shot Text Classification with Multiple Metrics
Mo Yu, Xiaoxiao Guo, Jinfeng Yi +6
We study few-shot learning in natural language domains. Compared to many existing works that apply either metric-based or optimization-based meta-learning to image domain with low…
Learning to Teach in Cooperative Multiagent Reinforcement Learning
Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu +5
Collective human knowledge has clearly benefited from the fact that innovations by individuals are taught to others through communication. Similar to human social groups, agents in…