GABI learns geometry-conditioned latent priors from multi-geometry physical response datasets for use in Bayesian inversion, yielding geometry-adapted posteriors via ABC sampling.
Deepergcn: All you need to train deeper gcns.arXiv preprint arXiv:2006.07739
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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SigGate-GT adds a per-head sigmoid gate to graph transformer attention outputs, relaxing the softmax convex-combination constraint to reduce over-smoothing and improve stability at ~1% parameter overhead.
BIC-Hunter combines confident learning for label denoising and GCNs on homogeneous graphs to identify bug-inducing commits, reporting gains of 6.16-7.13% on Recall@K and 8.43-32.82% on MFR over prior methods.
citing papers explorer
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Geometric Autoencoder Priors for Bayesian Inversion: Learn First Observe Later
GABI learns geometry-conditioned latent priors from multi-geometry physical response datasets for use in Bayesian inversion, yielding geometry-adapted posteriors via ABC sampling.
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Capacity-Controlled Global Attention for Graph Transformers
SigGate-GT adds a per-head sigmoid gate to graph transformer attention outputs, relaxing the softmax convex-combination constraint to reduce over-smoothing and improve stability at ~1% parameter overhead.
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Confident Learning-based Network for Detecting Bug-Inducing Commits on SZZ with Noisy Labels
BIC-Hunter combines confident learning for label denoising and GCNs on homogeneous graphs to identify bug-inducing commits, reporting gains of 6.16-7.13% on Recall@K and 8.43-32.82% on MFR over prior methods.