Abstract: Decoding emotion processing from electroencephalogram (EEG) signals is challenging yet promising. We investigate the use of Graph Neural Networks (GNNs) for interpreting EEG data.
According to Andrew Ng (@AndrewYNg), DeepLearning.AI has launched the PyTorch for Deep Learning Professional Certificate taught by Laurence Moroney (@lmoroney). This three-course program covers core ...
Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...
This library provides PyTorch implementations of tensor-train decomposed neural network layers that can significantly reduce the number of parameters in deep neural networks while maintaining accuracy ...
From a neuroscience perspective, artificial neural networks are regarded as abstract models of biological neurons, yet they rely on biologically implausible backpropagation for training. Energy-based ...
Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.5c01525. Efficiency analysis of different normalization strategies ...
A new study out this month from Stanford University researchers uses microelectrodes implanted in the motor cortex and generative AI to decode the intended and inner speech of four paralyzed patients.
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