4 papers · 1 filter
Learn it or Leave it: Module Composition and Pruning for Continual Learning
Mingyang Wang, Heike Adel, Lukas Lange +2
In real-world environments, continual learning is essential for machine learning models, as they need to acquire new knowledge incrementally without forgetting what they have alrea…
Rehearsal-Free Modular and Compositional Continual Learning for Language Models
Mingyang Wang, Heike Adel, Lukas Lange +2
Continual learning aims at incrementally acquiring new knowledge while not forgetting existing knowledge. To overcome catastrophic forgetting, methods are either rehearsal-based, i…
GradSim: Gradient-Based Language Grouping for Effective Multilingual Training
Mingyang Wang, Heike Adel, Lukas Lange +2
Most languages of the world pose low-resource challenges to natural language processing models. With multilingual training, knowledge can be shared among languages. However, not al…
Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning
Mingyang Wang, Zhenshan Bing, Xiangtong Yao +5
Meta-reinforcement learning enables artificial agents to learn from related training tasks and adapt to new tasks efficiently with minimal interaction data. However, most existing…