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Found 6 papers, 2 papers with code
Reset & Distill: A Recipe for Overcoming Negative Transfer in Continual Reinforcement Learning
We argue that one of the main obstacles for developing effective Continual Reinforcement Learning (CRL) algorithms is the negative transfer issue occurring when the new task to learn arrives.
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks
Moreover, the complexity of this trade-off is compounded in the heterogeneous graph case due to the disparate heterophily relationships between nodes of different types.
SS-IL: Separated Softmax for Incremental Learning
To that end, we analyze that computing the softmax probabilities by combining the output scores for all old and new classes could be the main cause of the bias.
Continual Learning with Node-Importance based Adaptive Group Sparse Regularization
We propose a novel regularization-based continual learning method, dubbed as Adaptive Group Sparsity based Continual Learning (AGS-CL), using two group sparsity-based penalties.
Uncertainty-based Continual Learning with Adaptive Regularization
We introduce a new neural network-based continual learning algorithm, dubbed as Uncertainty-regularized Continual Learning (UCL), which builds on traditional Bayesian online learning framework with variational inference.
Ranked #11 on
Continual Learning
on ASC (19 tasks)
Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty
We propose a novel iterative channel estimation (ICE) algorithm that essentially removes the critical known noisy channel assumption for universal discrete denoising problem.
