36 citations · 197 across the 20 of their papers we have counts for
8 papers · 1 filter
Position: Modular Memory is the Key to Continual Learning Agents
Vaggelis Dorovatas, Malte Schwerin, Andrew D. Bagdanov +21
Foundation models have transformed machine learning through large-scale pretraining and increased test-time compute. Despite surpassing human performance in several domains, these…
Continual Learning: Applications and the Road Forward
Eli Verwimp, Rahaf Aljundi, Shai Ben-David +17
Continual learning is a subfield of machine learning, which aims to allow machine learning models to continuously learn on new data, by accumulating knowledge without forgetting wh…
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…
Online Continual Learning for Embedded Devices
Tyler L. Hayes, Christopher Kanan
Real-time on-device continual learning is needed for new applications such as home robots, user personalization on smartphones, and augmented/virtual reality headsets. However, thi…
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…
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…
Memory Efficient Experience Replay for Streaming Learning
Tyler L. Hayes, Nathan D. Cahill, Christopher Kanan
In supervised machine learning, an agent is typically trained once and then deployed. While this works well for static settings, robots often operate in changing environments and m…