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.
By Guang Zhao, Xihaier Luo, Huan-Hsin Tseng, Seungjun Lee, Shinjae Yoo, Yihui Ren, Wei Xu
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...
By My Tra Nguyen Huynh, Viet Cuong Ta
arXiv:2607. 02055v1 Announce Type: cross Abstract: Performance evaluation in AI systems commonly assumes that random dataset splits produce independent and identically distributed (i.
By Prathamesh Patil, Arpit Jain, Aswanth Krishnan
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.
By Alonso Urbano, David W. Romero, Max Zimmer, Sebastian Pokutta
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.
By Juho Jung, Seongjae Kang, Mijoo Kim, Sungduk Cho, Yunkyoung Jun, Sunghoon Joo, Yoona Hwang
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.
By Beomgu Kang, Hyunseok Seo
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.
By Chengpiao Huang, Kaizheng Wang
arXiv:2606. 27171v1 Announce Type: new Abstract: This work addresses the problem of variance in stochastic gradient estimation for machine learning optimization.
By Jonne Pohjankukka, Jukka Heikkonen
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.
By Christian Schiffer, Mathis Bode, Thomas Lippert, Katrin Amunts, Timo Dickscheid
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.
By Rohan Hitchcock, Jesse Hoogland
arXiv:2610. 13915v1 Announce Type: new Abstract: Self-supervised learning on 3D medical images commonly relies on random sub-volume sampling during pretraining.
By Junkai Liu, Le Zhang
The paper introduces MIND, a method that distills specialist geospatial model embeddings into a single generalist coordinate embedding with adjustable spatial granularity, using nested supervision across multiple embedding dimensions. MIND’s design allows downstream predictors to use only leading chunks or apply a Chunked Penalty to downweight finer details without retraining the INR. The authors evaluate MIND on CoordBench, a large INR benchmark of 52 datasets and 78 targets, and report that MIND and its Chunked Penalty variant achieve the highest regression and classification scores, especially under regional holdout, establishing a new state‑of‑the‑art for geographic implicit neural representations.
By Isaac Corley, Arjun Rao, Esther Rolf, Konstantin Klemmer, Evan Shelhamer, Nils Lehmann, Marc Ru{\ss}wurm, Gengchen Mai, Nathan Jacobs, Hannah Kerner