1 citations · 1 across the 5 of their papers we have counts for
5 papers
Continual Learning Should Move Beyond Incremental Classification
Rupert Mitchell, Antonio Alliegro, Raffaello Camoriano +17
Continual learning (CL) is the sub-field of machine learning concerned with accumulating knowledge in dynamic environments. So far, CL research has mainly focused on incremental cl…
Where is the Truth? The Risk of Getting Confounded in a Continual World
Florian Peter Busch, Roshni Kamath, Rupert Mitchell +3
A dataset is confounded if it is most easily solved via a spurious correlation, which fails to generalize to new data. In this work, we show that, in a continual learning setting w…
BOWL: A Deceptively Simple Open World Learner
Roshni . R. Kamath, Rupert Mitchell, Subarnaduti Paul +2
Traditional machine learning excels on static benchmarks, but the real world is dynamic and seldom as carefully curated as test sets. Practical applications may generally encounter…
Masked Autoencoders are Efficient Continual Federated Learners
Subarnaduti Paul, Lars-Joel Frey, Roshni Kamath +2
Machine learning is typically framed from a perspective of i.i.d., and more importantly, isolated data. In parts, federated learning lifts this assumption, as it sets out to solve…
JUWELS Booster -- A Supercomputer for Large-Scale AI Research
Stefan Kesselheim, Andreas Herten, Kai Krajsek +15
In this article, we present JUWELS Booster, a recently commissioned high-performance computing system at the Jülich Supercomputing Center. With its system architecture, most import…