Open Source Contributions

Mar 24, 2024 ... Graph neural networks (GNNs) have achieved state-of-the-art results on many graph representation learning tasks by exploiting statistical ... a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process. Sat 7:10 a.m. - 7:18 a.m.. Neural networks learn an environment's geometry in latent space by performing predictive coding on visual scenes ( Oral ) > link Information integration is a measure, developed by Tononi and co-researchers, of the capacity for dynamic neural networks to be in informational states ... Modern artificial intelligence (AI) approaches mainly rely on neural network (NN) or deep NN methodologies. However, these approaches require large amounts ... ... neural coding models that depend on phase, increasing the informational content of neural representations; and (4) likely originate from the dynamics of ... same ink serve more than one informational purpose. Using large ... Physics-Informed Neural Networks for Anomaly Detection: A Practitioner's Guide ... Sep 1, 2021 ... We find that specialized neuronal populations in auditory cortex form functional networks during auditory task performance whose structures depend on both ... Oct 6, 2022 ... Our results show that synergy increases as neural networks learn multiple diverse tasks. Furthermore, performance in tasks requiring integration ... Classically, external receptors of the body transmit information from the environment to the cerebral cortex via the thalamus. This review explains and ...