Random sampling maps (with-replacement, binning, species) induce metrics that give uniform any-dimensional generalization and sketching rates for continuous functions on sequences, graphs and tensors.
The graph neural network model.IEEE Transactions on Neural Networks, 20(1):61–80
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OgBench is the first benchmark platform for GNN graph-level prediction in the n << p omics regime and finds that common GNNs often underperform MLPs and classical baselines.
Neural networks learn to construct argumentation structures that explain classifications through support and attack relations, trained jointly with differentiable semantics and structure constraints.
GHR uses hierarchical recurrence on pooled graph abstractions to improve long-range dependency capture and out-of-range generalization while using far fewer parameters than existing models.
citing papers explorer
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Any-Dimensional Learning by Sampling
Random sampling maps (with-replacement, binning, species) induce metrics that give uniform any-dimensional generalization and sketching rates for continuous functions on sequences, graphs and tensors.
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OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data
OgBench is the first benchmark platform for GNN graph-level prediction in the n << p omics regime and finds that common GNNs often underperform MLPs and classical baselines.
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Deep Arguing
Neural networks learn to construct argumentation structures that explain classifications through support and attack relations, trained jointly with differentiable semantics and structure constraints.
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Graph Hierarchical Recurrence for Long-Range Generalization
GHR uses hierarchical recurrence on pooled graph abstractions to improve long-range dependency capture and out-of-range generalization while using far fewer parameters than existing models.