8 citations · 21 across the 7 of their papers we have counts for
8 papers
Concept-modulated model-based offline reinforcement learning for rapid generalization
Nicholas A. Ketz, Praveen K. Pilly
The robustness of any machine learning solution is fundamentally bound by the data it was trained on. One way to generalize beyond the original training is through human-informed a…
Lifelong Learning with Sketched Structural Regularization
Haoran Li, Aditya Krishnan, Jingfeng Wu +3
Preventing catastrophic forgetting while continually learning new tasks is an essential problem in lifelong learning. Structural regularization (SR) refers to a family of algorithm…
Evolving Inborn Knowledge For Fast Adaptation in Dynamic POMDP Problems
Eseoghene Ben-Iwhiwhu, Pawel Ladosz, Jeffery Dick +3
Rapid online adaptation to changing tasks is an important problem in machine learning and, recently, a focus of meta-reinforcement learning. However, reinforcement learning (RL) al…
Generative Continual Concept Learning
Mohammad Rostami, Soheil Kolouri, James McClelland +1
After learning a concept, humans are also able to continually generalize their learned concepts to new domains by observing only a few labeled instances without any interference wi…
Attention-Based Structural-Plasticity
Soheil Kolouri, Nicholas Ketz, Xinyun Zou +2
Catastrophic forgetting/interference is a critical problem for lifelong learning machines, which impedes the agents from maintaining their previously learned knowledge while learni…
Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay
Mohammad Rostami, Soheil Kolouri, Praveen K. Pilly
Despite huge success, deep networks are unable to learn effectively in sequential multitask learning settings as they forget the past learned tasks after learning new tasks. Inspir…