64 citations · 102 across the 16 of their papers we have counts for
5 papers · 1 filter
Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse Networks
Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy
Using task-specific components within a neural network in continual learning (CL) is a compelling strategy to address the stability-plasticity dilemma in fixed-capacity models with…
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity
Shiwei Liu, Tianlong Chen, Zahra Atashgahi +6
The success of deep ensembles on improving predictive performance, uncertainty estimation, and out-of-distribution robustness has been extensively studied in the machine learning l…
Dynamic Sparse Training for Deep Reinforcement Learning
Ghada Sokar, Elena Mocanu, Decebal Constantin Mocanu +2
Deep reinforcement learning (DRL) agents are trained through trial-and-error interactions with the environment. This leads to a long training time for dense neural networks to achi…
Self-Attention Meta-Learner for Continual Learning
Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy
Continual learning aims to provide intelligent agents capable of learning multiple tasks sequentially with neural networks. One of its main challenging, catastrophic forgetting, is…
Learning Invariant Representation for Continual Learning
Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy
Continual learning aims to provide intelligent agents that are capable of learning continually a sequence of tasks, building on previously learned knowledge. A key challenge in thi…