arXiv Machine Learning
Weight Space Learning for Certifiable Few-shot Transfer Learning
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arXiv Machine Learning
Jun 2
Non-vacuous Generalization Bounds for Deep Neural Networks without any modification to the trained models
arXiv:2503. 07325v2 Announce Type: replace Abstract: Understanding and certifying the behavior of modern deep neural networks remains a fundamental challenge in reliable machine learning.
arXiv Machine Learning
Jun 4
Breaking the Scale Barrier: One-Shot Knowledge Transfer via Frequency Transform
arXiv:2603. 07523v3 Announce Type: replace Abstract: Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a pre-trained model is tightly coupled with monolithic architecture, which restricts flexible reuse across models of varying scales.
arXiv Computer Vision
Oct 7
Learning to Curate What You Generate for Generalizable Few-Shot Class-Incremental Learning
arXiv:2610.07008v1 Announce Type: new Abstract: Few-shot class-incremental learning (FSCIL) aims to learn novel classes from limited annotations while preserving prior knowledge. Existing methods typ...
arXiv Machine Learning
Jul 24
Statistical physics of deep learning: Optimal learning of a multi-layer perceptron near interpolation
arXiv:2510. 24616v4 Announce Type: replace-cross Abstract: For four decades statistical physics has been providing a framework to analyse neural networks.
arXiv Machine Learning
Oct 23
High-Value Training Data from Noisy, Diverse Sources
The paper presents a scalable method for learning dataweights that optimizes training-example importance using metagradients, guided by a small high‑quality target dataset. It introduces parametric weighting rules that generalize to new and streaming data and can transfer from smaller to larger models. Applied to reward modeling, the learned weights improve held‑out performance, adapt models to specialized domains, and enable human‑synthetic supervision mixtures to outperform either source alone.
arXiv Machine Learning
Oct 19
Physics-Transfer Learning: A Physics-Constrained Theory for Zero-Shot Generalization Along the Ladder of Complexity
Physics-Transfer Learning (PT) is a physics-constrained learning theory that enables zero-shot generalization across domains of increasing complexity. PT builds chains of anchor domains (CADs) linking low-cost source domains to complex targets and introduces computable feasibility and reliability indicators to assess transfer before target labels are available. The authors derive an upper bound on target risk under physical-consistency and stable-representation assumptions, and demonstrate PT’s effectiveness on quantum excited-state energy prediction and classical dynamical-transition prediction, achieving accurate high-fidelity predictions across substantial distribution shifts.