Gaussian process regression enables implicit multi-camera calibration by learning 2D-to-3D mappings with built-in uncertainty and active learning for efficient data use.
Dark Energy Survey year 1 results: Constraints on extended cosmological models from galaxy clustering and weak lensing
7 Pith papers cite this work, alongside 172 external citations. Polarity classification is still indexing.
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Pith papers citing it
172
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years
2026 7verdicts
UNVERDICTED 7representative citing papers
HEaD+ detects object hallucinations early in diffusion generation via cross-attention maps, text, and a Predicted Final Image, raising complete image rates by 6-8% for four-object prompts and reducing time by up to 32%.
A new tail dependence measure for linear processes with regularly varying distributions is introduced, incorporating persistence effects and validated via simulations and cryptocurrency data analysis.
MCBP detects boundaries by computing discrete mean curvature from k-nearest neighbor patches on the data manifold, then decomposes data into low-curvature smooth and high-curvature boundary subsets to improve clustering.
A cascade pipeline on 400 AIE tiles evaluates gg→ttg leading-order matrix elements at 1 million per second with parts-per-million accuracy to MadGraph, delivering 34× CPU speedup and 7.7× better energy efficiency at 54.8 W.
ProMoTA integrates process modeling with automated end-to-end traceability generation and analysis for model transformation chains in MDE, demonstrated on a wireless sensor network IoT application.
The paper identifies key unresolved questions in giant planet formation, interiors, and their role in planetary systems.
citing papers explorer
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Implicit Multi-Camera System Calibration Using Gaussian Processes
Gaussian process regression enables implicit multi-camera calibration by learning 2D-to-3D mappings with built-in uncertainty and active learning for efficient data use.
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Hallucination Early Detection in Diffusion Models
HEaD+ detects object hallucinations early in diffusion generation via cross-attention maps, text, and a Predicted Final Image, raising complete image rates by 6-8% for four-object prompts and reducing time by up to 32%.
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Measuring Tail Dependence in Linear Processes: Theory and Empirics
A new tail dependence measure for linear processes with regularly varying distributions is introduced, incorporating persistence effects and validated via simulations and cryptocurrency data analysis.
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A Mean Curvature Approach to Boundary Detection: Geometric Insights for Unsupervised Learning
MCBP detects boundaries by computing discrete mean curvature from k-nearest neighbor patches on the data manifold, then decomposes data into low-curvature smooth and high-curvature boundary subsets to improve clustering.
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Cascade Pipeline for Leading-Order Matrix Element Evaluation on AMD Versal AI Engine Arrays
A cascade pipeline on 400 AIE tiles evaluates gg→ttg leading-order matrix elements at 1 million per second with parts-per-million accuracy to MadGraph, delivering 34× CPU speedup and 7.7× better energy efficiency at 54.8 W.
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ProMoTA: a model-driven framework for end-to-end traceability analysis
ProMoTA integrates process modeling with automated end-to-end traceability generation and analysis for model transformation chains in MDE, demonstrated on a wireless sensor network IoT application.
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Outstanding Questions in Giant Planet Theory
The paper identifies key unresolved questions in giant planet formation, interiors, and their role in planetary systems.