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Found 3 papers, 2 papers with code
Equivariant Neural Operator Learning with Graphon Convolution
We propose a general architecture that combines the coefficient learning scheme with a residual operator layer for learning mappings between continuous functions in the 3D Euclidean space.
Equivariant Point Cloud Analysis via Learning Orientations for Message Passing
In this work, we propose a novel and simple framework to achieve equivariance for point cloud analysis based on the message passing (graph neural network) scheme.
DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature
We consider the problem of Named Entity Recognition (NER) on biomedical scientific literature, and more specifically the genomic variants recognition in this work.
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