13 citations · 15 across the 2 of their papers we have counts for
3 papers · 1 filter
Rethinking Tabular Foundation Models On Data Streams
Nilesh Verma, Daniel Nowak-Assis, Afonso Lourenço +4
Tabular foundation models (TFMs) outperform established machine learning models on tabular benchmarks through in-context learning. Building on this success, interest is growing in…
Detecting Domain Shifts in Myoelectric Activations: Challenges and Opportunities in Stream Learning
Yibin Sun, Nick Lim, Guilherme Weigert Cassales +4
Detecting domain shifts in myoelectric activations poses a significant challenge due to the inherent non-stationarity of electromyography (EMG) signals. This paper explores the det…
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal
Yaqian Zhang, Bernhard Pfahringer, Eibe Frank +3
Online continual learning (OCL) aims to train neural networks incrementally from a non-stationary data stream with a single pass through data. Rehearsal-based methods attempt to ap…