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Found 6 papers, 5 papers with code
La-MAML: Look-ahead Meta Learning for Continual Learning
The continual learning problem involves training models with limited capacity to perform well on a set of an unknown number of sequentially arriving tasks.
Look-ahead Meta Learning for Continual Learning
The continual learning problem involves training models with limited capacity to perform well on a set of an unknown number of sequentially arriving tasks.
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages
We show that the additive term is bounded proportional to the Lipschitz constant of the value function, which offers theoretical grounding for spectral normalization of critic weights.
Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales
PAC Bayes is a generalized framework which is more resistant to overfitting and that yields performance bounds that hold with arbitrarily high probability even on the unjustified extrapolations.
Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning?
To answer this question, we consider a set of tailored offline reinforcement learning datasets that exhibit causal ambiguity and assess the ability of active sampling techniques to reduce causal confusion at evaluation.
Geometric Consistency for Self-Supervised End-to-End Visual Odometry
We show that using a noisy teacher, which could be a standard VO pipeline, and by designing a loss term that enforces geometric consistency of the trajectory, we can train accurate deep models for VO that do not require ground-truth labels.
