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.
Implicit neural representations (INRs) provide a flexible framework for modeling high-dimensional continuous fields, but their training is often inefficient due to uniform subsampling that ignores spa...
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: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: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.
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: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.
arXiv:2606. 27171v1 Announce Type: new Abstract: This work addresses the problem of variance in stochastic gradient estimation for machine learning optimization.
The paper investigates how data scaling should be viewed as an allocation problem rather than a simple sample count, focusing on spatially structured data from human brain histology. By separating unique sample count, source diversity, and spatial coverage, the authors conduct 93 pretraining runs on 11.6 million image patches from 21 brains, showing that performance improves with more unique samples, broader spatial coverage, more compute, and larger models. However, at a fixed sample budget, distributing samples across multiple subjects does not yield additional benefit, indicating that inter‑subject variation impacts generalization but adding more sources does not help when the sample count is held constant.
The paper introduces a framework for synthetic‑augmented inference that balances the number of synthetic observations with their assigned weight. It defines a size‑weight frontier, estimating for each weight the maximum synthetic sample size that still guarantees target task‑marginal coverage for all smaller sizes. The authors provide finite‑sample coverage guarantees for configurations on or below this frontier and demonstrate that, when applied to augment opinion survey data with large language model responses, the method achieves the desired coverage while significantly tightening confidence intervals.
arXiv:2607. 20970v1 Announce Type: new Abstract: Implicit neural representations (INRs) for time-varying volumetric data are typically trained using dense sampling over spatiotemporal coordinates, where each observation corresponds to a single point in space and time.
arXiv:2610. 13915v1 Announce Type: new Abstract: Self-supervised learning on 3D medical images commonly relies on random sub-volume sampling during pretraining.
arXiv:2507. 21449v2 Announce Type: replace-cross Abstract: Degeneracy is an inherent feature of the loss landscape of neural networks, but it is not well understood how stochastic gradient MCMC (SGMCMC) algorithms interact with this degeneracy.