{"total":256,"rows":[{"id":"2103.05677","title":"SMIL: Multimodal Learning with Severely Missing Modality","created":"2021-03-09","cited_by_count":245,"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":"2301.07608","title":"Human-Timescale Adaptation in an Open-Ended Task Space","created":"2023-01-18","cited_by_count":138,"area":null},{"id":"2101.07296","title":"Using Shape to Categorize: Low-Shot Learning with an Explicit Shape Bias","created":"2021-01-18","cited_by_count":118,"area":null},{"id":"2403.19154","title":"STaR-GATE: Teaching Language Models to Ask Clarifying Questions","created":"2024-03-28","cited_by_count":104,"area":null},{"id":"2105.06868","title":"Priors in Bayesian Deep Learning: A Review","created":"2021-05-14","cited_by_count":101,"area":null},{"id":"2310.11589","title":"Eliciting Human Preferences with Language Models","created":"2023-10-17","cited_by_count":92,"area":null},{"id":"2203.17030","title":"Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks","created":"2022-03-31","cited_by_count":88,"area":null},{"id":"2110.09813","title":"Multi-Objective Loss Balancing for Physics-Informed Deep Learning","created":"2021-10-19","cited_by_count":87,"area":null},{"id":"2209.12892","title":"Learning to Learn with Generative Models of Neural Network Checkpoints","created":"2022-09-26","cited_by_count":86,"area":null},{"id":"2108.00622","title":"Recurrent Mask Refinement for Few-Shot Medical Image Segmentation","created":"2021-08-02","cited_by_count":63,"area":null},{"id":"2110.03909","title":"Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning","created":"2021-10-08","cited_by_count":49,"area":null},{"id":"2106.02695","title":"Meta-Learning with Fewer Tasks through Task Interpolation","created":"2021-06-04","cited_by_count":45,"area":null},{"id":"2109.14595","title":"Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis","created":"2021-09-29","cited_by_count":45,"area":null},{"id":"2103.07531","title":"Uncertainty-guided Model Generalization to Unseen Domains","created":"2021-03-12","cited_by_count":41,"area":null},{"id":"2206.06614","title":"Transformers are Meta-Reinforcement Learners","created":"2022-06-14","cited_by_count":41,"area":null},{"id":"2301.01299","title":"Recent Advances on Federated Learning: A Systematic Survey","created":"2023-01-03","cited_by_count":41,"area":null},{"id":"2305.17891","title":"The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification","created":"2023-05-29","cited_by_count":41,"area":null},{"id":"2202.01339","title":"Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty","created":"2022-02-01","cited_by_count":37,"area":null},{"id":"2212.04196","title":"Learning Domain Invariant Prompt for Vision-Language Models","created":"2022-12-08","cited_by_count":36,"area":"mllm"},{"id":"2107.10991","title":"A novel meta-learning initialization method for physics-informed neural networks","created":"2021-07-23","cited_by_count":35,"area":null},{"id":"2204.08491","title":"Active Learning Helps Pretrained Models Learn the Intended Task","created":"2022-04-18","cited_by_count":33,"area":null},{"id":"2101.00562","title":"Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier","created":"2021-01-03","cited_by_count":32,"area":null},{"id":"2110.13361","title":"A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs","created":"2021-10-26","cited_by_count":32,"area":null},{"id":"2105.00131","title":"GistNet: a Geometric Structure Transfer Network for Long-Tailed Recognition","created":"2021-05-01","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":"2107.06196","title":"No Regrets for Learning the Prior in Bandits","created":"2021-07-13","cited_by_count":30,"area":null},{"id":"2212.10711","title":"Task Ambiguity in Humans and Language Models","created":"2022-12-20","cited_by_count":28,"area":null},{"id":"2111.01754","title":"Meta-Learning to Improve Pre-Training","created":"2021-11-02","cited_by_count":27,"area":null},{"id":"2204.11181","title":"Realistic Evaluation of Transductive Few-Shot Learning","created":"2022-04-24","cited_by_count":27,"area":null},{"id":"2206.13074","title":"Leveraging Language for Accelerated Learning of Tool Manipulation","created":"2022-06-27","cited_by_count":26,"area":null},{"id":"2202.06174","title":"Source-Free Progressive Graph Learning for Open-Set Domain Adaptation","created":"2022-02-13","cited_by_count":25,"area":null},{"id":"2204.00949","title":"Matching Feature Sets for Few-Shot Image Classification","created":"2022-04-02","cited_by_count":25,"area":null},{"id":"2303.07502","title":"Meta-learning approaches for few-shot learning: A survey of recent advances","created":"2023-03-13","cited_by_count":25,"area":null},{"id":"2305.19185","title":"Compression with Bayesian Implicit Neural Representations","created":"2023-05-30","cited_by_count":25,"area":null},{"id":"2203.11378","title":"HyperShot: Few-Shot Learning by Kernel HyperNetworks","created":"2022-03-21","cited_by_count":24,"area":null},{"id":"2106.03195","title":"Meta-Learning Reliable Priors in the Function Space","created":"2021-06-06","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":"2410.15320","title":"Amortized Probabilistic Conditioning for Optimization, Simulation and Inference","created":"2024-10-20","cited_by_count":23,"area":null},{"id":"2103.03697","title":"Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms","created":"2021-03-05","cited_by_count":22,"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":"2210.08654","title":"Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs","created":"2022-10-16","cited_by_count":21,"area":null},{"id":"2501.11039","title":"Model Predictive Task Sampling for Efficient and Robust Adaptation","created":"2025-01-19","cited_by_count":21,"area":null},{"id":"2206.00092","title":"FHIST: A Benchmark for Few-shot Classification of Histological Images","created":"2022-05-31","cited_by_count":20,"area":null},{"id":"2210.16834","title":"Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid","created":"2022-10-30","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":"2109.05749","title":"Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning","created":"2021-09-13","cited_by_count":19,"area":null},{"id":"2204.01208","title":"Attribute Prototype Network for Any-Shot Learning","created":"2022-04-04","cited_by_count":19,"area":null},{"id":"2108.02888","title":"Out-of-Domain Generalization from a Single Source: An Uncertainty Quantification Approach","created":"2021-08-05","cited_by_count":18,"area":null},{"id":"2110.00715","title":"An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset","created":"2021-10-02","cited_by_count":18,"area":null},{"id":"2305.18413","title":"Learning to Learn from APIs: Black-Box Data-Free Meta-Learning","created":"2023-05-28","cited_by_count":18,"area":"distill"},{"id":"2102.08291","title":"Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models","created":"2021-02-16","cited_by_count":17,"area":null},{"id":"2205.15745","title":"HyperMAML: Few-Shot Adaptation of Deep Models with Hypernetworks","created":"2022-05-31","cited_by_count":17,"area":null},{"id":"2210.03067","title":"Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Prediction","created":"2022-10-06","cited_by_count":17,"area":null},{"id":"2301.12023","title":"Meta Temporal Point Processes","created":"2023-01-27","cited_by_count":17,"area":null},{"id":"2401.16634","title":"The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration","created":"2024-01-30","cited_by_count":17,"area":null},{"id":"2404.12699","title":"SOPHON: Non-Fine-Tunable Learning to Restrain Task Transferability For Pre-trained Models","created":"2024-04-19","cited_by_count":17,"area":null},{"id":"2102.00127","title":"On Data Efficiency of Meta-learning","created":"2021-01-30","cited_by_count":16,"area":null},{"id":"2110.12358","title":"A Closer Look at Few-Shot Video Classification: A New Baseline and Benchmark","created":"2021-10-24","cited_by_count":16,"area":null},{"id":"2205.15795","title":"A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud","created":"2022-05-31","cited_by_count":16,"area":null},{"id":"2406.16087","title":"Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy","created":"2024-06-23","cited_by_count":16,"area":"offlinerl"},{"id":"2102.03909","title":"Meta-Learning with Neural Tangent Kernels","created":"2021-02-07","cited_by_count":15,"area":null},{"id":"2110.05076","title":"A Closer Look at Prototype Classifier for Few-shot Image Classification","created":"2021-10-11","cited_by_count":15,"area":null},{"id":"2111.05323","title":"Variational Multi-Task Learning with Gumbel-Softmax Priors","created":"2021-11-09","cited_by_count":15,"area":null},{"id":"2206.09027","title":"Landscape Learning for Neural Network Inversion","created":"2022-06-17","cited_by_count":15,"area":null},{"id":"2207.01723","title":"Adaptive Fine-Grained Sketch-Based Image Retrieval","created":"2022-07-04","cited_by_count":15,"area":null},{"id":"2112.08181","title":"Hierarchical Variational Memory for Few-shot Learning Across Domains","created":"2021-12-15","cited_by_count":14,"area":null},{"id":"2201.11775","title":"The Effect of Diversity in Meta-Learning","created":"2022-01-27","cited_by_count":14,"area":null},{"id":"2209.01501","title":"Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions","created":"2022-09-03","cited_by_count":14,"area":null},{"id":"2303.09128","title":"Exploring Distributional Shifts in Large Language Models for Code Analysis","created":"2023-03-16","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":"2406.05054","title":"Prototype Correlation Matching and Class-Relation Reasoning for Few-Shot Medical Image Segmentation","created":"2024-06-07","cited_by_count":13,"area":null},{"id":"2110.13561","title":"Non-Gaussian Gaussian Processes for Few-Shot Regression","created":"2021-10-26","cited_by_count":12,"area":null},{"id":"2111.04239","title":"Learning to Rectify for Robust Learning with Noisy Labels","created":"2021-11-08","cited_by_count":12,"area":null},{"id":"2205.01423","title":"Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration","created":"2022-05-03","cited_by_count":12,"area":null},{"id":"2210.11579","title":"Model-based Lifelong Reinforcement Learning with Bayesian Exploration","created":"2022-10-20","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":"2306.11264","title":"GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks","created":"2023-06-20","cited_by_count":12,"area":null},{"id":"2504.19139","title":"Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environments","created":"2025-04-27","cited_by_count":12,"area":null},{"id":"2102.03748","title":"PAC-Bayes Bounds for Meta-learning with Data-Dependent Prior","created":"2021-02-07","cited_by_count":11,"area":null},{"id":"2106.08523","title":"ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning","created":"2021-06-16","cited_by_count":11,"area":null},{"id":"2203.09308","title":"Few-Shot Learning on Graphs","created":"2022-03-17","cited_by_count":11,"area":null},{"id":"2211.07206","title":"Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice","created":"2022-11-14","cited_by_count":11,"area":null},{"id":"2304.01552","title":"Meta-Learning with a Geometry-Adaptive Preconditioner","created":"2023-04-04","cited_by_count":11,"area":null},{"id":"2103.08233","title":"Robust MAML: Prioritization task buffer with adaptive learning process for model-agnostic meta-learning","created":"2021-03-15","cited_by_count":10,"area":null},{"id":"2106.00252","title":"Information-Theoretic Analysis of Epistemic Uncertainty in Bayesian Meta-learning","created":"2021-06-01","cited_by_count":10,"area":null},{"id":"2106.01051","title":"Minimax and Neyman-Pearson Meta-Learning for Outlier Languages","created":"2021-06-02","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":"2202.12888","title":"Meta-Learning for Simple Regret Minimization","created":"2022-02-25","cited_by_count":10,"area":null},{"id":"2212.00131","title":"Evidential Conditional Neural Processes","created":"2022-11-30","cited_by_count":10,"area":null},{"id":"2303.11183","title":"Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning","created":"2023-03-20","cited_by_count":10,"area":null},{"id":"2310.00708","title":"A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm","created":"2023-10-01","cited_by_count":10,"area":null},{"id":"2310.11709","title":"Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT","created":"2023-10-18","cited_by_count":10,"area":null},{"id":"2402.17717","title":"AmbigNLG: Addressing Task Ambiguity in Instruction for NLG","created":"2024-02-27","cited_by_count":10,"area":null},{"id":"2102.11503","title":"Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution","created":"2021-02-23","cited_by_count":9,"area":null},{"id":"2105.00127","title":"Breadcrumbs: Adversarial Class-Balanced Sampling for Long-tailed Recognition","created":"2021-05-01","cited_by_count":9,"area":null},{"id":"2203.12768","title":"Multidimensional Belief Quantification for Label-Efficient Meta-Learning","created":"2022-03-23","cited_by_count":9,"area":null},{"id":"2203.13465","title":"CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification","created":"2022-03-25","cited_by_count":9,"area":null},{"id":"2207.10188","title":"Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning Approach","created":"2022-07-20","cited_by_count":9,"area":null}],"next_cursor":"100"}