191 citations · 426 across the 21 of their papers we have counts for
9 papers · 1 filter
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…
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…
Towards calibrated and scalable uncertainty representations for neural networks
Nabeel Seedat, Christopher Kanan
For many applications it is critical to know the uncertainty of a neural network's predictions. While a variety of neural network parameter estimation methods have been proposed fo…
REMIND Your Neural Network to Prevent Catastrophic Forgetting
Tyler L. Hayes, Kushal Kafle, Robik Shrestha +2
People learn throughout life. However, incrementally updating conventional neural networks leads to catastrophic forgetting. A common remedy is replay, which is inspired by how the…
Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis
Tyler L. Hayes, Christopher Kanan
When an agent acquires new information, ideally it would immediately be capable of using that information to understand its environment. This is not possible using conventional dee…
Rethinking Continual Learning for Autonomous Agents and Robots
German I. Parisi, Christopher Kanan
Continual learning refers to the ability of a biological or artificial system to seamlessly learn from continuous streams of information while preventing catastrophic forgetting, i…