Continual learning with quick remembering
Positions: Student Researcher
Created: 2023-10-22 Deadline:
Location: Poland
In continual learning we want to understand the phenomenon of catastrophic forgetting - network quickly losing performance at previously learned tasks after encountering new tasks. However, this usually concerns zero-shot forgetting – what happens if we’re allowed to quickly recall the old problem before attempting to solve it? The goal of the project is to investigate how quickly we can recall the “forgotten” knowledge and build a CL method optimized for that
General must-have requirements
PyTorch or Jax
Contact: Maciej Wołczyk (maciej.wolczyk [ at ] gmail.com)
Project's lab:
GMUM (Group of Machine Learning Research) is a group of researchers working on various aspects of machine learning, and in particular deep learning - in both fundamental and applied settings. The group is led by prof. Jacek Tabor. We are based in the Jagiellonian University in the beautiful city of Kraków, Poland.
Some of the research directions our group pursues include:
- Generative models: efficient training and sampling; inpainting; super-resolution,
- Theoretical understanding of deep learning and optimization,
- Natural language processing,
- Drug design and cheminformatics,
- Unsupervised learning and clustering,
- Computer vision and medical image analysis.
In 2023, we organized the second edition of Machine Learning Summer School (MLSS^S) with a focus on Applications in Science. We invite participants to collaborate with us on various ongoing research projects - learn more here.
See lab's page