Continual Learning
Continual learning (CL) aims to design algorithms that can learn from non-stationarystreams of stationary tasks without forgetting. …
Rapid development of large-scale pre-training has resulted in foundation models that can act as effective feature extractors on a …
Rapid development of large-scale pre-training has resulted in foundation models that can act as effective feature extractors on a …
Modularity is a compelling solution to continual learning (CL), the problem of modeling sequences of related tasks. Learning and then …
Continual learning studies agents that learn from streams of tasks without forgetting previous ones while adapting to new ones. Two …
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing …
In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the …
We introduce and study the problem of Online Continual Compression, where one attempts to simultaneously learn to compress and store a …
Continual learning studies agents that learn from streams of tasks without forgetting previous ones while adapting to new ones. Two …
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing …