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Found 8 papers, 2 papers with code
Diagnosing Catastrophe: Large parts of accuracy loss in continual learning can be accounted for by readout misalignment
Unlike primates, training artificial neural networks on changing data distributions leads to a rapid decrease in performance on old tasks.
Keep Moving: identifying task-relevant subspaces to maximise plasticity for newly learned tasks
Continual learning algorithms strive to acquire new knowledge while preserving prior information.
Characterising representation dynamics in recurrent neural networks for object recognition
Recurrent neural networks (RNNs) have yielded promising results for both recognizing objects in challenging conditions and modeling aspects of primate vision.
The neuroconnectionist research programme
Artificial Neural Networks (ANNs) inspired by biology are beginning to be widely used to model behavioral and neural data, an approach we call neuroconnectionism.
Category-orthogonal object features guide information processing in recurrent neural networks trained for object categorization
Recurrent neural networks (RNNs) have been shown to perform better than feedforward architectures in visual object categorization tasks, especially in challenging conditions such as cluttered images.
An ecologically motivated image dataset for deep learning yields better models of human vision
This dataset comprises images from 1, 000 categories, selected to provide a challenging testbed for automated visual object recognition systems.
Learning robust visual representations using data augmentation invariance
Deep convolutional neural networks trained for image object categorization have shown remarkable similarities with representations found across the primate ventral visual stream.
Recurrence is required to capture the representational dynamics of the human visual system
Here, we measure and model the rapid representational dynamics across multiple stages of the human ventral stream using time-resolved brain imaging and deep learning.
