The test error of random-feature ridge regression with arbitrary data augmentation admits a closed-form asymptotic characterization in the proportional regime that depends only on population covariances and augmentation statistics.
arXiv preprint arXiv:2002.12478 , year =
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DAD4TS trains a diffusion-based generator jointly with a forecaster under RL control and geometric projections to produce augmentation samples that boost accuracy on small-scale time-series data, with validation reported on five of six real-world datasets.
EDL learns a transferable classification loss from unlimited synthetic data via evolutionary optimization and a ranking-consistency objective, serving as a competitive drop-in replacement for cross-entropy on CIFAR-10 with ResNet models.
Dynamics-centric mixtures of local reconstruction, temporal continuity, and in-context dynamics objectives in PathoFM yield the most balanced transfer across tasks and subjects on clinical gait time series.
UTOPYA fuses eight modalities via FiLM-conditioned attention and physics-informed regularization to reach AUROC 0.874 for anomaly detection in batch distillation, outperforming baselines by 0.147.
citing papers explorer
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Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation
The test error of random-feature ridge regression with arbitrary data augmentation admits a closed-form asymptotic characterization in the proportional regime that depends only on population covariances and augmentation statistics.
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DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data
DAD4TS trains a diffusion-based generator jointly with a forecaster under RL control and geometric projections to produce augmentation samples that boost accuracy on small-scale time-series data, with validation reported on five of six real-world datasets.
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Distribution-Free Pretraining of Classification Losses via Evolutionary Dynamics
EDL learns a transferable classification loss from unlimited synthetic data via evolutionary optimization and a ranking-consistency objective, serving as a competitive drop-in replacement for cross-entropy on CIFAR-10 with ResNet models.
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On the Role of Inductive Bias in Time-Series Pretraining: A Case Study in Learning Generalizable Representations for Clinical Time Series
Dynamics-centric mixtures of local reconstruction, temporal continuity, and in-context dynamics objectives in PathoFM yield the most balanced transfer across tasks and subjects on clinical gait time series.
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UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction
UTOPYA fuses eight modalities via FiLM-conditioned attention and physics-informed regularization to reach AUROC 0.874 for anomaly detection in batch distillation, outperforming baselines by 0.147.