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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- 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…
- Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language ModelsGongbo Zhang, Wen Wang, Ye Tian, Li Yuan · arXiv · Apr 29, 2026
Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters for competitive performance. While existing distillation methods for dLLMs reduce inference…
- Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival EstimationGeorgios I. Orfanidis, Dimitris A. Pados, George Sklivanitis, Elizabeth Serena Bentley · arXiv · Apr 29, 2026
We consider the problems of computing the optimal rank-$1$ Hankel and Toeplitz-structured approximation of arbitrary matrices under $L_2$ and $L_1$-norm error. Such problems arise naturally in engineered systems, including the basic few-sho…
- VLA Foundry: A Unified Framework for Training Vision-Language-Action ModelsJean Mercat, Sedrick Keh, Kushal Arora, Isabella Huang et al. · arXiv · Apr 21, 2026
We present VLA Foundry, an open-source framework that unifies LLM, VLM, and VLA training in a single codebase. Most open-source VLA efforts specialize on the action training stage, often stitching together incompatible pretraining pipelines…
- 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…
- 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…
- 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…
- 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…
- 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…
- 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…
- 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-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…
- 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…
- 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
- Advances and Challenges in Meta-Learning: A Technical ReviewAnna Vettoruzzo, Mohamed-Rafik Bouguelia, Joaquin Vanschoren, Thorsteinn Rögnvaldsson et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · Jul 1, 2024
Meta-learning empowers learning systems with the ability to acquire knowledge from multiple tasks, enabling faster adaptation and generalization to new tasks. This review provides a comprehensive technical overview of meta-learning, emphasi…
- 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 …
- Meta-Learning with Heterogeneous TasksZhaofeng Si, Shu Hu, Kaiyi Ji, Siwei Lyu · CoRR 2024 · Jan 1, 2024
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…
- Neuromodulated Meta-LearningJingyao Wang, Huijie Guo, Wenwen Qiang, Jiangmeng Li et al. · CoRR 2024 · Jan 1, 2024
Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain region…
- A Game Theoretic Approach to Meta-Learning: Nash Model-Agnostic Meta-LearningJihwan Yu, Jaeyeon Jo, Taeyoung Yun, Jinkyoo Park · ICLR 2024 Conference Withdrawn Submission · Sep 22, 2023
Meta-learning, or learning to learn, aims to develop algorithms that can quickly adapt to new tasks and environments. Model-agnostic meta-learning (MAML), proposed as a bi-level optimization problem, is widely used as a baseline for gradien…
- Making Scalable Meta Learning PracticalSang Keun Choe, Sanket Vaibhav Mehta, Hwijeen Ahn, Willie Neiswanger et al. · NeurIPS 2023 poster · Sep 21, 2023
Despite its flexibility to learn diverse inductive biases in machine learning programs, meta learning (i.e.,\ learning to learn) has long been recognized to suffer from poor scalability due to its tremendous compute/memory costs, training i…
- Meta-Learning with Task-Environment InteractionMaofa Wang, Quan Wan, Zhixiong Leng, Bingchen Yan et al. · Submitted to ICLR 2024 · Sep 21, 2023
The goal of meta-learning is to learn a universal model from various meta-training tasks, enabling rapid adaptation to new tasks with minimal training. Currently, mainstream meta-learning algorithms randomly sample meta-training tasks from …