English(EN)Enhancing Deep Neural Network Reliability with Refinement and Calibration
新研究解决深度学习偏差、训练动态和可靠性问题
作者PulseAugur 编辑部·[11 个来源]·
研究人员正在探索新的理论框架和实用方法来改进深度学习模型。一篇论文介绍了DISCO，一种通过估计条件距离相关性来减轻数据集偏差的技术，在各种数据集上的表现优于现有方法。另一项研究将神经网络训练视为一个Hamilton-Jacobi问题，将其与热带代数和偏微分方程联系起来，并提供了对泛化和鲁棒性的见解。此外，新的研究挑战了校准本身就能改善早期退出神经网络的假设，提出了一种考虑预测正确性和计算成本的替代方法。最后，研究正在调查深度网络在训练过程中如何保留或忘记其初始偏差，这对理解归纳偏差和泛化具有重要意义。
AI
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arXiv cs.AI
TIER_1English(EN)·Jose Marie Antonio Mi\~noza, Erika Fille T. Legara, Christopher P. Monterola·
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arXiv cs.LG
TIER_1English(EN)·Piotr Kubaty, Filip Szatkowski, Grzegorz Choczy\'nski, Eric Nalisnick, Bartosz W\'ojcik·
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arXiv:2605.29152v1 Announce Type: cross Abstract: Randomly initialized neural networks induce a prior over functions, but the predictor used in practice is produced only after training. We ask how much of this initial bias survives the training pipeline. To make the question meas…
arXiv:2605.27991v1 Announce Type: new Abstract: Deep neural networks (DNNs) have achieved remarkable empirical success, yet their training dynamics remain understood mainly from optimization rather than statistical principles. Here we develop a statistical framework for DNN train…
Randomly initialized neural networks induce a prior over functions, but the predictor used in practice is produced only after training. We ask how much of this initial bias survives the training pipeline. To make the question measurable, we introduce initialization memory: the de…
Deep neural networks (DNNs) have achieved remarkable empirical success, yet their training dynamics remain understood mainly from optimization rather than statistical principles. Here we develop a statistical framework for DNN training in the over-parameterized regime by showing …
Medium — fine-tuning tag
TIER_1English(EN)·Louis Develle·
