Hugging Face Trending Papers
Efficient Neural Field Learning via Adaptive Coverage and Focused Sampling
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arXiv AI
Oct 5
Efficient Neural Field Learning via Adaptive Coverage and Focused Sampling
The paper introduces ACES, a structured sampling framework for implicit neural representations that separates coverage from importance. ACES builds adaptive spatial partitions to ensure domain coverage and reduce redundant sampling, while applying region-level importance weighting to focus on informative areas. The authors provide theoretical analysis showing reduced gradient variance and potential optimization gains, and demonstrate faster convergence and lower error on scientific field learning tasks compared to uniform and pointwise adaptive sampling.
arXiv Computer Vision
5d ago
Improving Category-Aware Active Domain Adaptation via Uncertainty-Guided Region Coverage
arXiv:2610.19803v1 Announce Type: new Abstract: Active domain adaptation researches focus on alleviating the annotation cost, which comprises source data and target data labelling, as well as enhanci...
arXiv AI
Jul 3
Beyond the Performance Illusion: Structure-Aware Stratified Partitioning and Curriculum Distributionally Robust Optimization for Spatially Correlated Domains
arXiv:2607. 02055v1 Announce Type: cross Abstract: Performance evaluation in AI systems commonly assumes that random dataset splits produce independent and identically distributed (i.
arXiv Machine Learning
Jun 9
Neural Field Tokenizations with Hierarchy and Spatial Locality Priors
arXiv:2606. 08204v1 Announce Type: new Abstract: Neural fields parameterize data as functions from coordinates to values, providing a unified framework for representation learning across modalities.
arXiv Machine Learning
4d ago
SPARCL: Spatially Adaptive Regional Coverage for Active Learning in Chest X-ray Analysis
SPARCL (Spatially Adaptive Regional Coverage for Active Learning) is a new active learning strategy for chest X‑ray lesion segmentation that balances predictive uncertainty with spatial coverage using a concave coverage function. It directs annotation effort toward under‑labeled regions, reverts to standard uncertainty sampling when spatial variation is absent, and guarantees near‑optimal greedy batch selection through a monotone submodular objective. The authors also propose Spatial Inconsistency and Performance Coverage metrics to reveal regional fairness, and demonstrate that SPARCL achieves 95% of fully supervised performance using only 24% of the data versus 29% for the best baseline.
arXiv Machine Learning
Jul 9
Prior-aware and Context-guided Group Sampling for Active Probabilistic Subsampling
arXiv:2607. 07083v1 Announce Type: cross Abstract: Subsampling significantly reduces the number of measurements, thereby streamlining data processing and transfer overhead, and shortening acquisition time across diverse real-world applications.