84 citations · 216 across the 9 of their papers we have counts for
8 papers · 1 filter
Information-theoretic Online Memory Selection for Continual Learning
Shengyang Sun, Daniele Calandriello, Huiyi Hu +2
A challenging problem in task-free continual learning is the online selection of a representative replay memory from data streams. In this work, we investigate the online memory se…
Task-agnostic Continual Learning with Hybrid Probabilistic Models
Polina Kirichenko, Mehrdad Farajtabar, Dushyant Rao +6
Learning new tasks continuously without forgetting on a constantly changing data distribution is essential for real-world problems but extremely challenging for modern deep learnin…
The Effectiveness of Memory Replay in Large Scale Continual Learning
Yogesh Balaji, Mehrdad Farajtabar, Dong Yin +2
We study continual learning in the large scale setting where tasks in the input sequence are not limited to classification, and the outputs can be of high dimension. Among multiple…
Optimization and Generalization of Regularization-Based Continual Learning: a Loss Approximation Viewpoint
Dong Yin, Mehrdad Farajtabar, Ang Li +2
Neural networks have achieved remarkable success in many cognitive tasks. However, when they are trained sequentially on multiple tasks without access to old data, their performanc…
Learning to Incentivize Other Learning Agents
Jiachen Yang, Ang Li, Mehrdad Farajtabar +3
The challenge of developing powerful and general Reinforcement Learning (RL) agents has received increasing attention in recent years. Much of this effort has focused on the single…
Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing
Ge Liu, Rui Wu, Heng-Tze Cheng +7
Deep Reinforcement Learning (RL) is proven powerful for decision making in simulated environments. However, training deep RL model is challenging in real world applications such as…