Continual Lifelong Learning Workshop (ACML 2022)
12th December (1:35pm-5:05pm IST), hybrid format (in-person events in Hyderabad, India)
Deep-learning methods require extremely large amounts of data and computational resources, and lack human-like natural abilities to quickly adapt to their surroundings and learn continually. Continual lifelong learning methods aim to bridge this gap between humans and machines. In this workshop, our goal is to bring together Asian researchers working on this topic, and connect them to other communities in the rest of the world. Similar workshops have taken place in other Machine Learning conferences, but they have largely focused on researchers from North America and Europe. This workshop will provide networking opportunities for researchers, allowing them to collaborate and work towards solving continual lifelong learning.
The workshop will focus on a broad range of topics covering many aspects of continual lifelong learning, including (but not limited to): fast adaptation, forward/backward transfer, continual reinforcement learning, skill learning, abstraction and representations for lifelong learning, and relationship to similar ideas such as multi-task learning, meta learning, curriculum learning, and active learning. Our program is available here.
Please see our Call for Papers. The submission deadline (31st October 2022 AoE) has passed. To attend the workshop, you must be registered for the ACML conference as a ‘Secondary authors / Other attendees’: register here.
Accepted papers can be found on OpenReview.
Please contact email@example.com if you have any questions.
This workshop is supported by JST CREST Grant Number JPMJCR2112, “A new Bayes-duality principle for adaptive, robust, and lifelong learning of AI systems”.