New model frames human reinforcement learning in the context of memory and habits

Humans and most other animals are known to be strongly driven by expected rewards or adverse consequences. The process of ...
Beebom

What is Deep Learning in AI? Understanding How AI Really Works

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter ...
Tech Xplore

Overparameterized neural networks: Feature learning precedes overfitting, research finds

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, ...
IEEE

PSELDNets: Pre-Trained Neural Networks on a Large-Scale Synthetic Dataset for Sound Event Localization and Detection

Sound event localization and detection (SELD) has seen substantial advancements through learning-based methods. These systems, typically trained from scratch on specific datasets, have shown ...
GitHub

Sobhan-Lotfi/INR-toolkit

INR papers are exciting. But going from paper to working code? That's where most people get stuck.
IEEE

Advancement in Graph Neural Networks for EEG Signal Analysis and Application: A Review

Abstract: Electroencephalography (EEG) can non-invasively measure neuronal events and reflect brain activity at different locations on the scalp. Early studies for EEG signal processing have focused ...
The Lancet

Dynamic modelling of the ALSFRS-R: leveraging population-based scores using neural networks

aAcademic Unit of Neurology, Trinity Biomedical Sciences Institute, School of Medicine, Trinity College Dublin, The University of Dublin, Dublin, Ireland ...