The representation of individual memories in a recurrent neural network can be efficiently differentiated using chaotic recurrent dynamics.
GenAI isn’t magic — it’s transformers using attention to understand context at scale. Knowing how they work will help CIOs ...
The study highlights that autonomous vehicle infrastructure presents a large and complex attack surface. Vehicles now contain ...
This valuable study links psychological theories of chunking with a physiological implementation based on short-term synaptic plasticity and synaptic augmentation. The theoretical derivation for ...
A new technical paper titled “Solving sparse finite element problems on neuromorphic hardware” was published by researchers ...
Artificial intelligence is quietly transforming how scientists monitor and manage invisible biological pollutants in rivers, ...
Artificial intelligence is quietly transforming how scientists monitor and manage invisible biological pollutants in rivers, lakes, and coastal ...
Adapting to the Stream: An Instance-Attention GNN Method for Irregular Multivariate Time Series Data
DynIMTS replaces static graphs with instance-attention that updates edge weights on the fly, delivering SOTA imputation and P12 classification ...
New research shows that a deeply ancient part of the brain can process visual information on its own, without help from the cortex. Scientists found that the superior colliculus, a structure shared by ...
Calculations show that injecting randomness into a quantum neural network could help it determine properties of quantum ...
Early-2026 explainer reframes transformer attention: tokenized text becomes Q/K/V self-attention maps, not linear prediction.
The world’s most powerful supercomputers can now run simulations of billions of neurons, and researchers hope such models ...
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