Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
Differentiable surface splatting for point-based geometry processing , year =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
SplAttN uses Gaussian soft splatting and attention to avoid sparse projection collapse in point cloud completion, achieving SOTA results and demonstrating genuine visual cue reliance on KITTI.
citing papers explorer
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Reliable model selection in the presence of parameter non-identifiability
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
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SplAttN: Bridging 2D and 3D with Gaussian Soft Splatting and Attention for Point Cloud Completion
SplAttN uses Gaussian soft splatting and attention to avoid sparse projection collapse in point cloud completion, achieving SOTA results and demonstrating genuine visual cue reliance on KITTI.