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Found 4 papers, 3 papers with code
KAM -- a Kernel Attention Module for Emotion Classification with EEG Data
In this work, a kernel attention module is presented for the task of EEG-based emotion classification with neural networks.
A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG Data
In this work, a parameter-efficient attention module is presented for emotion classification using a limited, or relatively small, number of electroencephalogram (EEG) signals.
Solving Irregular and Data-enriched Differential Equations using Deep Neural Networks
Next, a shock solution to compressible magnetohydrodynamics (MHD) is solved for, and used in a scenario where experimental data is utilized to enhance a PDE system that is \emph{a priori} insufficient to validate against the observed/experimental data.
FanStore: Enabling Efficient and Scalable I/O for Distributed Deep Learning
Our experiments with benchmarks and real applications show that FanStore can scale DL training to 512 compute nodes with over 90\% scaling efficiency.
Distributed, Parallel, and Cluster Computing
