Search Results for author:
Found 6 papers, 5 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.
On Reducing Negative Jacobian Determinant of the Deformation Predicted by Deep Registration Networks
Ideally, the transformation that registers one image to another should be a diffeomorphism that is both invertible and smooth.
A 1d convolutional network for leaf and time series classification
In the forward direction, the proposed network can be used in two ways: a classifier and an automatic feature extractor.
FAIM -- A ConvNet Method for Unsupervised 3D Medical Image Registration
We found that FAIM is able to maintain both the advantages of higher accuracy and fewer "folding" locations over VoxelMorph, over a range of hyper-parameters (with the same values used for both networks).
A landmark-based algorithm for automatic pattern recognition and abnormality detection
For illustration, we apply this framework to a small database of brain images for detecting structure abnormality.
