Meta-Learning
1,719 papers
Papers per year
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Papers
Sharing Knowledge for Meta-learning with Feature Descriptions
NeurIPS 2022
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning
ICML 2022
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
JMLR 2022
Starting from “Zero”: An Incremental Zero-shot Learning Approach for Assessing Peer Feedback Comments
NAACL 2022
ST-MAML : A stochastic-task based method for task-heterogeneous meta-learning
UAI 2022
Subspace Learning for Effective Meta-Learning
ICML 2022
SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search
ICLR 2022
System-Agnostic Meta-Learning for MDP-based Dynamic Scheduling via Descriptive Policy
AISTATS 2022
Task Affinity with Maximum Bipartite Matching in Few-Shot Learning
ICLR 2022
Task-level Differentially Private Meta Learning
NeurIPS 2022
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding
AACL 2022
Task Relatedness-Based Generalization Bounds for Meta Learning
ICLR 2022
Teaching Interactively to Learn Emotions in Natural Language
NAACL 2022
Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning
JMLR 2022
The Role of Deconfounding in Meta-learning
ICML 2022
Thompson Sampling for Bandit Learning in Matching Markets
IJCAI 2022
Towards Few-Shot Identification of Morality Frames using In-Context Learning
EMNLP 2022
Towards Learning Universal Hyperparameter Optimizers with Transformers
NeurIPS 2022
Transferability and Stability of Learning with Limited Labelled Data in Multilingual Text Document Classification
IJCAI 2022
Transferring Hierarchical Structures with Dual Meta Imitation Learning
CoRL 2022
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling
ICML 2022
Transformers are Meta-Reinforcement Learners
ICML 2022
Triangulation candidates for Bayesian optimization
NeurIPS 2022
Using natural language and program abstractions to instill human inductive biases in machines
NeurIPS 2022
VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty
ICML 2022