ArticleNature communications2023
Transfer Learning with Kernel Methods.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- A Hybrid Multi-CNN Feature Fusion and LASSO Optimization Approach for High-Performance Breast Cancer Classification.Journal of imaging informatics in medicine · 2026Article
- Leveraging scenario differences for cross-task generalization in water plant transfer machine learning models.Environmental science and ecotechnology · 2025Article
- Post-variational classical quantum transfer learning for binary classification.Scientific reports · 2025Article
- Machine learning-driven imaging data for early prediction of lung toxicity in breast cancer radiotherapy.Scientific reports · 2025Article
- Experimental quantum-enhanced kernel-based machine learning on a photonic processor.Nature photonics · 2025Article
- Transfer learning enabled transformer-based generative adversarial networks for modeling and generating terahertz channels.Communications engineering · 2024Article
- Focal cortical dysplasia (type II) detection with multi-modal MRI and a deep-learning framework.Npj imaging · 2024Article
- Progressive transfer learning for advancing machine learning-based reduced-order modeling.Scientific reports · 2024Article
- Transfer Learning with Kernel Methods.Nature communications · 2023Article
- Improved YOLOv5 s and transfer learning for floater detection.Science progressArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple models that are competitive on a variety of tasks, it has been unclear how to develop scalable kernel-based transfer learning methods across general source and target tasks with possibly differing label dimensions. In this work, we propose a transfer learning framework for kernel methods by projecting and translating the source model to the target task. We demonstrate the effectiveness of our framework in applications to image classification and virtual drug screening. For both applications, we identify simple scaling laws that characterize the performance of transfer-learned kernels as a function of the number of target examples. We explain this phenomenon in a simplified linear setting, where we are able to derive the exact scaling laws.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.