The representation of individual memories in a recurrent neural network can be efficiently differentiated using chaotic recurrent dynamics.
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 ...
The study highlights that autonomous vehicle infrastructure presents a large and complex attack surface. Vehicles now contain ...
GenAI isn’t magic — it’s transformers using attention to understand context at scale. Knowing how they work will help CIOs ...
A new technical paper titled “Solving sparse finite element problems on neuromorphic hardware” was published by researchers ...
A new technical paper titled “Enabling Physical AI at the Edge: Hardware-Accelerated Recovery of System Dynamics” was ...
Artificial intelligence is quietly transforming how scientists monitor and manage invisible biological pollutants in rivers, lakes, and coastal ...
Traditional water management approaches are increasingly unfit for modern pressures. Periodic manual measurements, delayed ...
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 ...
A South Korean research team has discovered for the first time in the world that a common, intractable brain tumor in young ...
Early-2026 explainer reframes transformer attention: tokenized text becomes Q/K/V self-attention maps, not linear prediction.
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