27 citations · 57 across the 6 of their papers we have counts for
13 papers
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games
Indranil Sur, Zachary Daniels, Abrar Rahman +16
As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world applications, it is important that they exhibit the ability to continually learn and adap…
Replay in Deep Learning: Current Approaches and Missing Biological Elements
Tyler L. Hayes, Giri P. Krishnan, Maxim Bazhenov +3
Replay is the reactivation of one or more neural patterns, which are similar to the activation patterns experienced during past waking experiences. Replay was first observed in bio…
Selective Replay Enhances Learning in Online Continual Analogical Reasoning
Tyler L. Hayes, Christopher Kanan
In continual learning, a system learns from non-stationary data streams or batches without catastrophic forgetting. While this problem has been heavily studied in supervised image…
Avalanche: an End-to-End Library for Continual Learning
Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu +25
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing…
Self-Supervised Training Enhances Online Continual Learning
Jhair Gallardo, Tyler L. Hayes, Christopher Kanan
In continual learning, a system must incrementally learn from a non-stationary data stream without catastrophic forgetting. Recently, multiple methods have been devised for increme…
Improved Robustness to Open Set Inputs via Tempered Mixup
Ryne Roady, Tyler L. Hayes, Christopher Kanan
Supervised classification methods often assume that evaluation data is drawn from the same distribution as training data and that all classes are present for training. However, rea…