Latest Meta-Learning Research Papers
The newest Meta-Learning papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Meta-Learning so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.
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- TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific ScenariosHong Lyu, Mingru Yang, Qianhua He, Yanxiong Li et al. · arXiv · Jul 7, 2026
There are some datasets of varying scales for audio classification (AC) applied to different tasks. However, annotated data is limited for most scenarios, such as domestic environments. To address this challenge, we propose an $\textbf{A}$u…
- Neuron-Aware Active Few-Shot Learning for LLMsZhuowei Chen, Liwei Chen, Christian Schunn, Raquel Coelho et al. · arXiv · Jul 2, 2026
Active Few-Shot Learning (AFSL) adapts LLMs to specialized domains by identifying the most valuable unlabeled samples for annotation and use as few-shot demonstrations, effectively reducing human annotation costs while promoting high perfor…
- Hedgementation = Hedgerow Segmentation: A Remote Sensing BenchmarkNathan Senyard, Salem Hamdani, Astrid Zhang, Derek Wang et al. · arXiv · Jun 22, 2026
We propose Hedgementation: a new benchmark to evaluate machine learning models for hedgerow mapping from remote sensing data at country scale and 10m$^2$ spatial resolution. We combine and harmonize multiple remote sensing data products and…
- Scaling Linear Mode Connectivity and Merging to Billion Parameter Pretrained TransformersTianyi Li, Zhiqiang Shen · arXiv · Jun 22, 2026
Linear mode connectivity (LMC) provides a promising foundation for understanding and merging independently trained neural networks, but existing methods typically optimize the interpolation path from only one model endpoint, limiting their …
- Claw-SWE-Bench: A Benchmark for Evaluating OpenClaw-style Agent Harnesses on Coding TasksMengyu Zheng, Kai Han, Boxun Li, Haiyang Xu et al. · arXiv · Jun 10, 2026
General-purpose agents such as OpenClaw are increasingly used as autonomous tool users, but their coding ability is difficult to measure under SWE-bench: a generic agent does not by itself satisfy the clean Docker workspace, patch, and pred…
- OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinibAbhijoy Sarkar, Aarchi Singh Thakur · arXiv · Jun 9, 2026
Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computatio…
- CoMetaPNS: Continually Meta-learning Personalized Neural Surrogates for Cardiac Electrophysiology SimulationsRyan Missel, Xiajun Jiang, Linwei Wang · arXiv · Jun 5, 2026
Personalized virtual heart simulations face challenges in model personalization and computational cost. While neural surrogates offer state-of-the-art solutions, they typically address either efficient personalization or training generaliza…
- A Multimodal 3D Foundation Model for Light Sheet Fluorescence Microscopy Enables Few-Shot Segmentation, Classification, and DeblurringAdina Scheinfeld, Haotan Zhang, Shang Mu, Rudolf L. M. van Herten et al. · arXiv · May 25, 2026
Light sheet fluorescence microscopy (LSM) enables high-resolution, three-dimensional (3D) imaging of biological specimens, providing rich volumetric data for studying cellular organization, pathology, and vascular networks. However, the siz…
- Federated Meta-Learning Based Computation Offloading Approach With Energy-Delay Tradeoffs in UAV-Assisted VECChunlin Li, Chao Deng, Yong Zhang, Shaohua Wan · IEEE Transactions on Mobile Computing · Oct 1, 2025
Federated learning (FL) provides an applicable solution for computation offloading in Unmanned Aerial Vehicle(UAV)-assisted Vehicular Edge Computing (VEC) by preserving privacy. However, the heterogeneity of clients brings challenges to the…
- A Gradient Meta-Learning Joint Optimization for Beamforming and Antenna Position in Pinching-Antenna SystemsKang Zhou, Weixi Zhou, Donghong Cai, Xianfu Lei et al. · IEEE Transactions on Communications · Jun 14, 2025
In this paper, we consider a novel optimization design for multi-waveguide pinching-antenna systems, aiming to maximize the weighted sum rate (WSR) by jointly optimizing beamforming coefficients and antenna position. To handle the formulate…
- Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit EmergenceGouki Minegishi, Hiroki Furuta, Shohei Taniguchi, Yusuke Iwasawa et al. · International Conference on Machine Learning · May 22, 2025
Transformer-based language models exhibit In-Context Learning (ICL), where predictions are made adaptively based on context. While prior work links induction heads to ICL through a sudden jump in accuracy, this can only account for ICL when…
- Meta-XPFL: An Explainable and Personalized Federated Meta-Learning Framework for Privacy-Aware IoMTM. Serhani, Asadullah Tariq, Tariq Qayyum, Ikbal Taleb et al. · IEEE Internet of Things Journal · May 15, 2025
In the Internet of Medical Things (IoMT), specifically in the field of medical image classification—particularly for skin cancer detection—traditional methods face challenges related to data privacy, heterogeneity, and the need for personal…
- System Prompt Optimization with Meta-LearningYumin Choi, Jinheon Baek, Sung Ju Hwang · arXiv.org · May 14, 2025
Large Language Models (LLMs) have shown remarkable capabilities, with optimizing their input prompts playing a pivotal role in maximizing their performance. However, while LLM prompts consist of both the task-agnostic system prompts and tas…
- MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-LearningBin-Bin Gao · arXiv.org · May 14, 2025
Zero- and few-shot visual anomaly segmentation relies on powerful vision-language models that detect unseen anomalies using manually designed textual prompts. However, visual representations are inherently independent of language. In this p…
- CAMeL: Cross-Modality Adaptive Meta-Learning for Text-Based Person RetrievalHang Yu, Jiahao Wen, Zhedong Zheng · IEEE Transactions on Information Forensics and Security · Apr 26, 2025
Text-based person retrieval aims to identify specific individuals within an image database using textual descriptions. Due to the high cost of annotation and privacy protection, researchers resort to synthesized data for the paradigm of pre…
- A framework reforming personalized Internet of Things by federated meta-learningLinlin You, Zihan Guo, Chau Yuen, C. Chen et al. · Nature Communications · Apr 20, 2025
Advances in Artificial Intelligence envision a promising future, where the personalized Internet of Things can be revolutionized with the ability to continuously improve system efficiency and service quality. However, with the introduction …
- An Adaptive Framework for Intrusion Detection in IoT Security Using MAML (Model-Agnostic Meta-Learning)Fatma S. Alrayes, Syed Umar Amin, N. Hakami · Italian National Conference on Sensors · Apr 1, 2025
With the rapid emergence of the Internet of Things (IoT) devices, there were new vectors for attacking cyber, so there was a need for approachable intrusion detection systems (IDSs) with more innovative custom tactics. The traditional IDS m…
- Adaptive Meta-Learning Stochastic Gradient Hamiltonian Monte Carlo Simulation for Bayesian Updating of Structural Dynamic ModelsXianghao Meng, James L. Beck, Yong Huang, Hui Li · Computer Methods in Applied Mechanics and Engineering · Mar 1, 2025
In the last few decades, Markov chain Monte Carlo (MCMC) methods have been widely applied to Bayesian updating of structural dynamic models in the field of structural health monitoring. Recently, several MCMC algorithms have been developed …
- A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-LearningJia Gao, Shuangquan Lyu, Guiran Liu, Binrong Zhu et al. · 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE) · Feb 13, 2025
With the continuous development of natural language processing (NLP) technology, text classification tasks have been widely used in multiple application fields. However, obtaining labeled data is often expensive and difficult, especially in…
- Improving landslide susceptibility prediction through ensemble recursive feature elimination and meta-learning frameworkKrishnagopal Halder, A. Srivastava, Anitabha Ghosh, Subhabrata Das et al. · Scientific Reports · Feb 12, 2025
Landslides pose significant threats to ecosystems, lives, and economies, particularly in the geologically fragile Sub-Himalayan region of West Bengal, India. This study enhances landslide susceptibility prediction by developing an ensemble …
- Learning to Imbalanced Open Set Generalize: A Meta-Learning Framework for Enhanced Mechanical DiagnosisChangdong Wang, Zhou Shu, Jingli Yang, Zhenyu Zhao et al. · IEEE Transactions on Cybernetics · Feb 5, 2025
To alleviate data distribution under different operating conditions, domain generalization (DG) has been applied in mechanical diagnosis. Still, its effectiveness is limited when unknown fault states appear in the target domain. Consequentl…
- Global or Local Adaptation? Client-Sampled Federated Meta-Learning for Personalized IoT Intrusion DetectionHaorui Yan, Xi Lin, Shenghong Li, Hao Peng et al. · IEEE Transactions on Information Forensics and Security · Jan 1, 2025
With the increasing size of Internet of Things (IoT) devices, cyber threats to IoT systems have increased. Federated learning (FL) has been implemented in an anomaly-based intrusion detection system (NIDS) to detect malicious traffic in IoT…
- Short-term Load Forecasting of Distribution Transformer Supply Zones Based on Federated Model-Agnostic Meta LearningChangsen Feng, Liang Shao, Jiaying Wang, Youbing Zhang et al. · IEEE Transactions on Power Systems · Jan 1, 2025
With the increasing data privacy concerns raised by not only organizations but also individuals in distribution systems, traditional centralized data-driven forecasting approaches for short-term load forecasting (STLF) in distribution trans…
- MAML-KalmanNet: A Neural Network-Assisted Kalman Filter Based on Model-Agnostic Meta-LearningShanli Chen, Yunfei Zheng, Dongyuan Lin, Peng Cai et al. · IEEE Transactions on Signal Processing · Jan 1, 2025
Neural network-assisted (NNA) Kalman filters provide an effective solution to addressing the filtering issues involving partially unknown system information by incorporating neural networks to compute the intermediate values influenced by u…
- Meta-learning with Heterogeneous TasksZhaofeng Si, Shu Hu, Kaiyi Ji, Siwei Lyu · Crossref · Jan 1, 2025
Meta-learning is a general approach to equip machine learning models with the ability to handle few-shot scenarios when dealing with many tasks. Most existing meta-learning methods work based on the assumption that all tasks are of equal im…
- Meta-Learning-Based Domain Generalization for Cost-Effective Tool Condition Monitoring in Ultrasonic Metal WeldingYuquan Meng, Zhiqiao Dong, Kuan-Chieh Lu, Shichen Li et al. · IEEE Transactions on Industrial Informatics · Jan 1, 2025
Online tool condition monitoring (TCM) is a pivotal capability in many manufacturing applications including ultrasonic metal welding (UMW). Effective and efficient TCM can facilitate predictive maintenance, improve product quality, and enha…
- Few-shot fault diagnosis of axial piston pump based on prior knowledge-embedded meta learning vision transformer under variable operating conditionsSuiyan Wang, Han Shuai, Junhui Hu, Jitong Zhang et al. · Expert systems with applications · Jan 1, 2025
- Meta-Learning Enhanced Model Predictive Contouring Control for Agile and Precise Quadrotor FlightMingxin Wei, Lanxiang Zheng, Ying Wu, Ruidong Mei et al. · IEEE Transactions on robotics · Jan 1, 2025
In agile quadrotor flight, accurately modeling the varying aerodynamic drag forces encountered at different speeds is critical. These drag forces significantly impact the performance and maneuverability of the quadrotor, especially during h…
- Informed Meta-LearningKasia Kobalczyk, Mihaela van der Schaar · ICML 2024 Workshop MHFAIA Poster · Jun 17, 2024
In noisy and low-data regimes prevalent in real-world applications, a key challenge of machine learning lies in effectively incorporating inductive biases that promote data efficiency and robustness. Meta-learning and informed ML stand out …
- Informed Meta-LearningKasia Kobalczyk, Mihaela van der Schaar · 2nd SPIGM @ ICML Poster · Jun 17, 2024
In noisy and low-data regimes prevalent in real-world applications, a key challenge of machine learning lies in effectively incorporating inductive biases that promote data efficiency and robustness. Meta-learning and informed ML stand out …