{"total":238,"rows":[{"id":"2109.01652","title":"Finetuned Language Models Are Zero-Shot Learners","created":"2021-09-03","cited_by_count":3780,"area":null},{"id":"2110.11334","title":"Generalized Out-of-Distribution Detection: A Survey","created":"2021-10-21","cited_by_count":802,"area":null},{"id":"2103.03097","title":"Generalizing to Unseen Domains: A Survey on Domain Generalization","created":"2021-03-02","cited_by_count":766,"area":null},{"id":"2103.00550","title":"A Survey on Deep Semi-supervised Learning","created":"2021-02-28","cited_by_count":251,"area":null},{"id":"2110.01167","title":"Trustworthy AI: From Principles to Practices","created":"2021-10-04","cited_by_count":207,"area":null},{"id":"2205.06743","title":"A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities","created":"2022-05-13","cited_by_count":163,"area":null},{"id":"2505.24760","title":"REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewards","created":"2025-05-30","cited_by_count":130,"area":null},{"id":"2207.04179","title":"Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling","created":"2022-07-09","cited_by_count":127,"area":null},{"id":"2203.07615","title":"Learning What Not to Segment: A New Perspective on Few-Shot Segmentation","created":"2022-03-15","cited_by_count":120,"area":null},{"id":"2107.11277","title":"Machine Learning with a Reject Option: A survey","created":"2021-07-23","cited_by_count":109,"area":null},{"id":"2211.14238","title":"Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time","created":"2022-11-25","cited_by_count":90,"area":null},{"id":"2303.14652","title":"Hierarchical Dense Correlation Distillation for Few-Shot Segmentation","created":"2023-03-26","cited_by_count":84,"area":null},{"id":"2204.11051","title":"$\\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization","created":"2022-04-23","cited_by_count":70,"area":null},{"id":"2111.07736","title":"Continual Learning via Local Module Composition","created":"2021-11-15","cited_by_count":63,"area":null},{"id":"2411.03687","title":"Beyond Model Adaptation at Test Time: A Survey","created":"2024-11-06","cited_by_count":59,"area":null},{"id":"2206.03996","title":"Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning","created":"2022-06-08","cited_by_count":58,"area":null},{"id":"2102.07952","title":"A Survey of Machine Learning for Computer Architecture and Systems","created":"2021-02-16","cited_by_count":53,"area":null},{"id":"2106.09017","title":"Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation","created":"2021-06-16","cited_by_count":50,"area":null},{"id":"2102.03832","title":"Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks","created":"2021-02-07","cited_by_count":36,"area":null},{"id":"2105.11905","title":"Exploiting Adapters for Cross-lingual Low-resource Speech Recognition","created":"2021-05-18","cited_by_count":35,"area":null},{"id":"2103.07492","title":"Continual Learning for Recurrent Neural Networks: an Empirical Evaluation","created":"2021-03-12","cited_by_count":34,"area":null},{"id":"2406.06210","title":"Quantum Architecture Search: A Survey","created":"2024-06-10","cited_by_count":32,"area":null},{"id":"2207.01932","title":"Image Coding for Machines with Omnipotent Feature Learning","created":"2022-07-05","cited_by_count":31,"area":null},{"id":"2209.06257","title":"A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge","created":"2022-09-13","cited_by_count":31,"area":null},{"id":"2209.11208","title":"A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases","created":"2022-09-22","cited_by_count":30,"area":null},{"id":"2106.05386","title":"Artificial Intelligence in Drug Discovery: Applications and Techniques","created":"2021-06-09","cited_by_count":28,"area":null},{"id":"2202.06881","title":"Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation","created":"2022-02-14","cited_by_count":26,"area":null},{"id":"2205.01500","title":"Meta Learning for Natural Language Processing: A Survey","created":"2022-05-03","cited_by_count":26,"area":null},{"id":"2107.08861","title":"VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition","created":"2021-07-19","cited_by_count":25,"area":null},{"id":"2111.05850","title":"Towards Green Automated Machine Learning: Status Quo and Future Directions","created":"2021-11-10","cited_by_count":25,"area":null},{"id":"2109.14035","title":"Formalizing the Generalization-Forgetting Trade-off in Continual Learning","created":"2021-09-28","cited_by_count":24,"area":null},{"id":"2305.02279","title":"Learngene: Inheriting Condensed Knowledge from the Ancestry Model to Descendant Models","created":"2023-05-03","cited_by_count":24,"area":null},{"id":"2103.09985","title":"A deep learning theory for neural networks grounded in physics","created":"2021-03-18","cited_by_count":23,"area":null},{"id":"2106.06098","title":"Meta-Adaptive Nonlinear Control: Theory and Algorithms","created":"2021-06-11","cited_by_count":23,"area":null},{"id":"2111.05326","title":"The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning","created":"2021-11-09","cited_by_count":23,"area":null},{"id":"2203.13903","title":"Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection","created":"2022-03-25","cited_by_count":23,"area":null},{"id":"2106.16245","title":"How to Train Your MAML to Excel in Few-Shot Classification","created":"2021-06-30","cited_by_count":22,"area":null},{"id":"2203.09137","title":"Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning","created":"2022-03-17","cited_by_count":22,"area":null},{"id":"2106.03819","title":"A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps","created":"2021-06-07","cited_by_count":21,"area":null},{"id":"2105.02741","title":"Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems","created":"2021-05-06","cited_by_count":20,"area":null},{"id":"2108.09435","title":"Fairness-Aware Online Meta-learning","created":"2021-08-21","cited_by_count":19,"area":null},{"id":"2211.01834","title":"Toward Unsupervised Outlier Model Selection","created":"2022-11-03","cited_by_count":19,"area":null},{"id":"2103.09815","title":"TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL","created":"2021-03-17","cited_by_count":18,"area":null},{"id":"2201.06814","title":"Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling","created":"2022-01-18","cited_by_count":18,"area":null},{"id":"2102.00127","title":"On Data Efficiency of Meta-learning","created":"2021-01-30","cited_by_count":16,"area":null},{"id":"2201.03801","title":"Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019","created":"2022-01-11","cited_by_count":16,"area":null},{"id":"2203.15664","title":"Nearly Minimax Algorithms for Linear Bandits with Shared Representation","created":"2022-03-29","cited_by_count":16,"area":null},{"id":"2410.03074","title":"MetaOOD: Automatic Selection of OOD Detection Models","created":"2024-10-04","cited_by_count":16,"area":null},{"id":"2112.04467","title":"CoMPS: Continual Meta Policy Search","created":"2021-12-08","cited_by_count":15,"area":null},{"id":"2109.07497","title":"Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGD","created":"2021-09-15","cited_by_count":14,"area":null},{"id":"2112.08524","title":"FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning","created":"2021-12-15","cited_by_count":14,"area":null},{"id":"2206.08476","title":"Zero-Shot AutoML with Pretrained Models","created":"2022-06-16","cited_by_count":14,"area":null},{"id":"2203.03059","title":"Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?","created":"2022-03-06","cited_by_count":13,"area":null},{"id":"2203.10939","title":"Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review","created":"2022-03-07","cited_by_count":13,"area":null},{"id":"2205.13457","title":"AutoTSG: Learning and Synthesis for Incident Troubleshooting","created":"2022-05-26","cited_by_count":12,"area":null},{"id":"2210.13111","title":"Federated Learning and Meta Learning: Approaches, Applications, and Directions","created":"2022-10-24","cited_by_count":12,"area":null},{"id":"2107.09414","title":"Algorithm Selection on a Meta Level","created":"2021-07-20","cited_by_count":11,"area":null},{"id":"2109.13576","title":"Multimodality in Meta-Learning: A Comprehensive Survey","created":"2021-09-28","cited_by_count":11,"area":null},{"id":"2303.13251","title":"A Bag-of-Prototypes Representation for Dataset-Level Applications","created":"2023-03-23","cited_by_count":11,"area":null},{"id":"2307.08924","title":"Towards Task Sampler Learning for Meta-Learning","created":"2023-07-18","cited_by_count":11,"area":null},{"id":"2501.13075","title":"Evolution and The Knightian Blindspot of Machine Learning","created":"2025-01-22","cited_by_count":11,"area":null},{"id":"2102.10744","title":"MetaDelta: A Meta-Learning System for Few-shot Image Classification","created":"2021-02-22","cited_by_count":10,"area":null},{"id":"2106.06410","title":"What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data","created":"2021-06-11","cited_by_count":10,"area":null},{"id":"2205.09995","title":"Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning","created":"2022-05-20","cited_by_count":10,"area":null},{"id":"2206.08138","title":"Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification","created":"2022-06-15","cited_by_count":10,"area":null},{"id":"2301.12095","title":"MetaNO: How to Transfer Your Knowledge on Learning Hidden Physics","created":"2023-01-28","cited_by_count":10,"area":null},{"id":"2106.15367","title":"MAML is a Noisy Contrastive Learner in Classification","created":"2021-06-29","cited_by_count":9,"area":null},{"id":"2107.02378","title":"Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks","created":"2021-07-06","cited_by_count":9,"area":null},{"id":"2205.09536","title":"A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction","created":"2022-05-19","cited_by_count":9,"area":null},{"id":"2206.09136","title":"Provable Generalization of Overparameterized Meta-learning Trained with SGD","created":"2022-06-18","cited_by_count":9,"area":null},{"id":"2209.08907","title":"Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning","created":"2022-09-19","cited_by_count":9,"area":null},{"id":"2301.00957","title":"Metalearning generalizable dynamics from trajectories","created":"2023-01-03","cited_by_count":9,"area":null},{"id":"2308.02877","title":"Meta-learning in healthcare: A survey","created":"2023-08-05","cited_by_count":9,"area":null},{"id":"2310.11291","title":"An Automatic Learning Rate Schedule Algorithm for Achieving Faster Convergence and Steeper Descent","created":"2023-10-17","cited_by_count":9,"area":null},{"id":"2406.16422","title":"Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Prompting","created":"2024-06-24","cited_by_count":9,"area":null},{"id":"2508.04153","title":"ICM-Fusion: In-Context Meta-Optimized LoRA Fusion for Multi-Task Adaptation","created":"2025-08-06","cited_by_count":9,"area":null},{"id":"2511.03685","title":"Structured Matrix Scaling for Multi-Class Calibration","created":"2025-11-05","cited_by_count":9,"area":null},{"id":"2106.06526","title":"Online Continual Adaptation with Active Self-Training","created":"2021-06-11","cited_by_count":8,"area":null},{"id":"2107.13682","title":"Bayesian Embeddings for Few-Shot Open World Recognition","created":"2021-07-29","cited_by_count":8,"area":null},{"id":"2107.14035","title":"ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback","created":"2021-07-23","cited_by_count":8,"area":null},{"id":"2205.10766","title":"Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey","created":"2022-05-22","cited_by_count":8,"area":null},{"id":"2206.06872","title":"On Provably Robust Meta-Bayesian Optimization","created":"2022-06-14","cited_by_count":8,"area":null},{"id":"2301.06806","title":"Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes","created":"2023-01-17","cited_by_count":8,"area":null},{"id":"2308.03811","title":"Non-Convex Bilevel Optimization with Time-Varying Objective Functions","created":"2023-08-07","cited_by_count":8,"area":null},{"id":"2308.14250","title":"Rule-Based Error Detection and Correction to Operationalize Movement Trajectory Classification","created":"2023-08-28","cited_by_count":8,"area":null},{"id":"2310.06167","title":"Predictable Artificial Intelligence","created":"2023-10-09","cited_by_count":8,"area":null},{"id":"2502.04573","title":"Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer","created":"2025-02-06","cited_by_count":8,"area":null},{"id":"2104.10527","title":"Stateless Neural Meta-Learning using Second-Order Gradients","created":"2021-04-21","cited_by_count":7,"area":null},{"id":"2203.04274","title":"Leveraging Initial Hints for Free in Stochastic Linear Bandits","created":"2022-03-08","cited_by_count":7,"area":null},{"id":"2205.03824","title":"A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges","created":"2022-05-08","cited_by_count":7,"area":null},{"id":"2208.01573","title":"Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning","created":"2022-08-02","cited_by_count":7,"area":null},{"id":"2211.14819","title":"Deep Active Learning for Computer Vision: Past and Future","created":"2022-11-27","cited_by_count":7,"area":null},{"id":"2312.05743","title":"Building Variable-sized Models via Learngene Pool","created":"2023-12-10","cited_by_count":7,"area":null},{"id":"2103.03901","title":"Fast On-Device Adaptation for Spiking Neural Networks via Online-Within-Online Meta-Learning","created":"2021-02-21","cited_by_count":6,"area":null},{"id":"2106.02393","title":"Multitask Online Mirror Descent","created":"2021-06-04","cited_by_count":6,"area":null},{"id":"2107.00801","title":"Meta-Learning for Relative Density-Ratio Estimation","created":"2021-07-02","cited_by_count":6,"area":null},{"id":"2111.10032","title":"Meta Clustering Learning for Large-scale Unsupervised Person Re-identification","created":"2021-11-19","cited_by_count":6,"area":null},{"id":"2201.07372","title":"Prospective Learning: Principled Extrapolation to the Future","created":"2022-01-19","cited_by_count":6,"area":null},{"id":"2405.06643","title":"Levels of AI Agents: from Rules to Large Language Models","created":"2024-03-06","cited_by_count":6,"area":null},{"id":"2101.07757","title":"Magnification Generalization for Histopathology Image Embedding","created":"2021-01-18","cited_by_count":5,"area":null}],"next_cursor":"100"}