Federated Learning (FL) is an emerging paradigm that allows a model to b...
In Federated Learning, a global model is learned by aggregating model up...
The ability to continuously acquire new knowledge and skills is crucial ...
Parameter-efficient fine-tuning (PEFT) methods can adapt large language
...
In the online continual learning paradigm, agents must learn from a chan...
Classical machine learning frameworks assume access to a possibly large
...
This paper presents SPeCiaL: a method for unsupervised pretraining of
re...
A commonly cited inefficiency of neural network training using
back-prop...
We study the online continual learning paradigm, where agents must learn...
Learning from non-stationary data remains a great challenge for machine
...
We introduce and study the problem of Online Continual Compression, wher...
Continual learning, the setting where a learning agent is faced with a n...
Despite recent successes in Reinforcement Learning, value-based methods ...
Building models capable of generating structured output is a key challen...
Generating high-quality text with sufficient diversity is essential for ...