88 citations · 141 across the 4 of their papers we have counts for
4 papers · 1 filter
Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling
Harry Jake Cunningham, Giorgio Giannone, Mingtian Zhang +1
Global convolutions have shown increasing promise as powerful general-purpose sequence models. However, training long convolutions is challenging, and kernel parameterizations must…
Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence
Liyuan Wang, Xingxing Zhang, Qian Li +4
Continual learning aims to empower artificial intelligence (AI) with strong adaptability to the real world. For this purpose, a desirable solution should properly balance memory st…
AFEC: Active Forgetting of Negative Transfer in Continual Learning
Liyuan Wang, Mingtian Zhang, Zhongfan Jia +5
Continual learning aims to learn a sequence of tasks from dynamic data distributions. Without accessing to the old training samples, knowledge transfer from the old tasks to each n…
Wasserstein Robust Reinforcement Learning
Mohammed Amin Abdullah, Hang Ren, Haitham Bou Ammar +4
Reinforcement learning algorithms, though successful, tend to over-fit to training environments hampering their application to the real-world. This paper proposes $\text{W}\text{R}…