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Found 8 papers, 3 papers with code
Computational characterization of the role of an attention schema in controlling visuospatial attention
We found that the more uncertain the agent was about the location of its attentional window, the more it benefited from these additional resources, which developed an attention schema.
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.
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.
Modulation of early visual processing alleviates capacity limits in solving multiple tasks
In daily life situations, we have to perform multiple tasks given a visual stimulus, which requires task-relevant information to be transmitted through our visual system.
The functional role of cue-driven feature-based feedback in object recognition
Visual object recognition is not a trivial task, especially when the objects are degraded or surrounded by clutter or presented briefly.
Implementing a Reverse Dictionary, based on word definitions, using a Node-Graph Architecture
In this paper, we outline an approach to build graph-based reverse dictionaries using word definitions.
