On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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5 results for “Space-Variant”

Deep Learning for space-variant deconvolution in galaxy surveysastro-ph.IM

Deep learning improves galaxy image deconvolution in surveys.

problem Deconvolving large survey images with space-variant PSFs.

method Employed a U-Net DNN architecture for supervised galaxy image processing. Two strategies: Tikhonov deconvolution and ADMM-based iterative deconvolution.

result Deep learning approaches outperform standard convex optimization techniques in galaxy image reconstruction and shape recovery.

Deep Learning Galaxy Imaging Deconvolution Point Spread Function Space-Variant

2019-11-01abs ↗pdf ↗

On embeddings and acyclic mapsmath.AT

Study embeddings of manifolds via acyclic maps and surgery.

problem Relationship between embeddings of manifolds in spheres.

method Solve Poincaré duality variant, apply surgery machine, focus on homology spheres.

result Deduce results about homotopy type of embedding spaces.

embeddings manifolds spheres surgery Poincaré duality

2024-08-21abs ↗pdf ↗

Newton's method for nonlinear mappings into vector bundlesmath.DG

Newton's method tackles nonlinear mappings into vector bundles with connections and retractions.

problem Finding zeros of mappings from a manifold into a vector bundle.

method Local convergence using differentiability concepts, Banach space Riemannian distance, and affine covariant damping strategy.

result Illustrated application to generalized non-symmetric eigenvalue problems.

Newton's method nonlinear mappings vector bundles connections retractions

2024-04-05abs ↗pdf ↗

Gaussian Material Synthesiscs.LG

We present a learning-based system for rapid mass-scale material synthesis that is useful for novice and expert users alike. The user preferences are learned via Gaussian Process Regression and can be easily sampled for new recommendations. Typically, each recommendation takes 40-60 seconds to render with global illumi…

2018-04-23abs ↗pdf ↗

Curiosity-Critic: Cumulative Prediction Error Improvement as a Tractable Intrinsic Reward for World Model Trainingcs.LG

Curiosity-Critic improves world model training by focusing on cumulative prediction error.

problem Training world models with intrinsic rewards that consider cumulative prediction error.

method Curiosity-Critic uses a surrogate reward based on the difference between current and asymptotic prediction errors, estimated online by a co-trained critic.

result Curiosity-Critic outperforms other methods in training speed and final world model accuracy.

world model training intrinsic reward prediction error curiosity asymptotic error

2026-04-20abs ↗pdf ↗