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Meta-Learning

1524 directly classified papers

Papers per year

Papers

BigSmall: Efficient Multi-Task Learning for Disparate Spatial and Temporal Physiological Measurements WACV 2024
NeuralSolver: Learning Algorithms For Consistent and Efficient Extrapolation Across General Tasks NIPS 2024
Remote Sensing for Water Quality: A Multi-Task, Metadata-Driven Hypernetwork Approach IJCAI 2024
Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario EMNLP 2024
Meta-Learning via PAC-Bayesian with Data-Dependent Prior: Generalization Bounds from Local Entropy IJCAI 2024
Teaching Language Models to Self-Improve through Interactive Demonstrations NAACL 2024
QGFN: Controllable Greediness with Action Values NIPS 2024
ELF-UA: Efficient Label-Free User Adaptation in Gaze Estimation IJCAI 2024
Model Balancing Helps Low-data Training and Fine-tuning EMNLP 2024
MetaRLEC: Meta-Reinforcement Learning for Discovery of Brain Effective Connectivity AAAI 2024
Learning to Learn Faster from Human Feedback with Language Model Predictive Control RSS 2024
HyperLoRA: Efficient Cross-task Generalization via Constrained Low-Rank Adapters Generation EMNLP 2024
Inference and Verbalization Functions During In-Context Learning EMNLP 2024
How Does In-Context Learning Help Prompt Tuning? EACL 2024
Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning NIPS 2024
ACCEPT: Adaptive Codebook for Composite and Efficient Prompt Tuning EMNLP 2024
Large Language Models are In-context Teachers for Knowledge Reasoning EMNLP 2024
Metalearning with Very Few Samples Per Task COLT 2024
Knowledge-centered conversational agents with a drive to learn NAACL 2024
Scalable Fine-tuning from Multiple Data Sources: A First-Order Approximation Approach EMNLP 2024
Mothman at SemEval-2024 Task 9: An Iterative System for Chain-of-Thought Prompt Optimization NAACL 2024
MedINST: Meta Dataset of Biomedical Instructions EMNLP 2024
On task-relevant loss functions in meta-reinforcement learning L4DC 2024
Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity NIPS 2024
Practice Makes Perfect: Planning to Learning Skill Parameter Policies RSS 2024