475 citations · 542 across the 12 of their papers we have counts for
4 papers · 1 filter
Diagnosing Catastrophe: Large parts of accuracy loss in continual learning can be accounted for by readout misalignment
Daniel Anthes, Sushrut Thorat, Peter König +1
Unlike primates, training artificial neural networks on changing data distributions leads to a rapid decrease in performance on old tasks. This phenomenon is commonly referred to a…
Keep Moving: identifying task-relevant subspaces to maximise plasticity for newly learned tasks
Daniel Anthes, Sushrut Thorat, Peter König +1
Continual learning algorithms strive to acquire new knowledge while preserving prior information. Often, these algorithms emphasise stability and restrict network updates upon lear…
End-to-end topographic networks as models of cortical map formation and human visual behaviour: moving beyond convolutions
Zejin Lu, Adrien Doerig, Victoria Bosch +4
Computational models are an essential tool for understanding the origin and functions of the topographic organisation of the primate visual system. Yet, vision is most commonly mod…
Characterising representation dynamics in recurrent neural networks for object recognition
Sushrut Thorat, Adrien Doerig, Tim C. Kietzmann
Recurrent neural networks (RNNs) have yielded promising results for both recognizing objects in challenging conditions and modeling aspects of primate vision. However, the represen…