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Found 6 papers, 4 papers with code
Definition-independent Formalization of Soundscapes: Towards a Formal Methodology
Soundscapes have been studied by researchers from various disciplines, each with different perspectives, goals, approaches, and terminologies.
GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings
We present GNNAutoScale (GAS), a framework for scaling arbitrary message-passing GNNs to large graphs.
Hierarchical Inter-Message Passing for Learning on Molecular Graphs
We present a hierarchical neural message passing architecture for learning on molecular graphs.
Ranked #26 on
Graph Property Prediction
on ogbg-molhiv
Adversarial Generation of Continuous Implicit Shape Representations
This work presents a generative adversarial architecture for generating three-dimensional shapes based on signed distance representations.
Recognizing Cuneiform Signs Using Graph Based Methods
To this end, the distance measure is used to implement a nearest neighbor classifier leading to a high computational cost for the prediction phase with increasing training set size.
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e. g., graphs or meshes.
Ranked #3 on
Node Classification
on Cora
