6 citations · 11 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 3 cited
Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization
Leo Feng, Padideh Nouri, Aneri Muni +2
The ability to accelerate the design of biological sequences can have a substantial impact on the progress of the medical field. The problem can be framed as a global optimization…
cs.LG2022★ 6 cited
Continuous-Time Meta-Learning with Forward Mode Differentiation
Tristan Deleu, David Kanaa, Leo Feng +4
Drawing inspiration from gradient-based meta-learning methods with infinitely small gradient steps, we introduce Continuous-Time Meta-Learning (COMLN), a meta-learning algorithm wh…
cs.LG2019★ 2 cited
VIABLE: Fast Adaptation via Backpropagating Learned Loss
Leo Feng, Luisa Zintgraf, Bei Peng +1
In few-shot learning, typically, the loss function which is applied at test time is the one we are ultimately interested in minimising, such as the mean-squared-error loss for a re…