ADELIA is the first AD-enabled INLA system that computes exact hyperparameter gradients via a structure-exploiting multi-GPU backward pass, delivering 4.2-7.9x per-gradient speedups and 5-8x better energy efficiency than finite differences on models with up to 1.9 million latent variables.
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4representative citing papers
DiV-INR integrates implicit neural representations as conditioning signals for diffusion models to achieve better perceptual quality than HEVC, VVC, and prior neural codecs at extremely low bitrates under 0.05 bpp.
EmbGen creates synthetic QA data by entity decomposition, embedding-based reassembly into clusters, and multi-level sampling with cluster-specific prompts, yielding up to 88.9% higher Binary Accuracy than baselines on heterogeneous datasets under fixed token budgets.
A two-stage gradient boosted model with random effects predicts Arctic vessel movement probability (AUC 0.85) and conditional positive speed (77% out-of-fold variance explained), highlighting distance to coast and bathymetric depth as dominant factors.
citing papers explorer
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ADELIA: Automatic Differentiation for Efficient Laplace Inference Approximations
ADELIA is the first AD-enabled INLA system that computes exact hyperparameter gradients via a structure-exploiting multi-GPU backward pass, delivering 4.2-7.9x per-gradient speedups and 5-8x better energy efficiency than finite differences on models with up to 1.9 million latent variables.
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DiV-INR: Extreme Low-Bitrate Diffusion Video Compression with INR Conditioning
DiV-INR integrates implicit neural representations as conditioning signals for diffusion models to achieve better perceptual quality than HEVC, VVC, and prior neural codecs at extremely low bitrates under 0.05 bpp.
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EmbGen: Teaching with Reassembled Corpora
EmbGen creates synthetic QA data by entity decomposition, embedding-based reassembly into clusters, and multi-level sampling with cluster-specific prompts, yielding up to 88.9% higher Binary Accuracy than baselines on heterogeneous datasets under fixed token budgets.
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A Gradient Boosted Mixed-Model Machine Learning Framework for Vessel Speed in the U.S. Arctic
A two-stage gradient boosted model with random effects predicts Arctic vessel movement probability (AUC 0.85) and conditional positive speed (77% out-of-fold variance explained), highlighting distance to coast and bathymetric depth as dominant factors.