Meta-Learning
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Benchmarks
Most implemented
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Prototypical Networks for Few-shot Learning
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
On First-Order Meta-Learning Algorithms
Learning to Compare: Relation Network for Few-Shot Learning
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning
Papers
Are encoders able to learn landmarkers for warm-starting of Hyperparameter Optimization?
Effectively representing heterogeneous tabular datasets for meta-learning purposes is still an open problem. Previous approaches rely on representations that are intended to be universal. This paper proposes two novel me…
Hyperparameter OptimizationMeta-LearningMetric LearningRepresentation LearningImbalanced Regression Pipeline Recommendation
Imbalanced problems are prevalent in various real-world scenarios and are extensively explored in classification tasks. However, they also present challenges for regression tasks due to the rarity of certain target value…
AutoMLMeta-LearningregressionCLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels
Learning with noisy labels (LNL) is essential for training deep neural networks with imperfect data. Meta-learning approaches have achieved success by using a clean unbiased labeled set to train a robust model. However, …
Learning with noisy labelsMeta-LearningMixture of Experts in Large Language Models
This paper presents a comprehensive review of the Mixture-of-Experts (MoE) architecture in large language models, highlighting its ability to significantly enhance model performance while maintaining minimal computationa…
DiversityLanguage ModelingLanguage ModellingLarge Language Model+2Iceberg: Enhancing HLS Modeling with Synthetic Data
Deep learning-based prediction models for High-Level Synthesis (HLS) of hardware designs often struggle to generalize. In this paper, we study how to close the generalizability gap of these models through pretraining on …
Data AugmentationHigh-Level SynthesisLanguage ModelingLanguage Modelling+2Meta-Reinforcement Learning for Fast and Data-Efficient Spectrum Allocation in Dynamic Wireless Networks
The dynamic allocation of spectrum in 5G / 6G networks is critical to efficient resource utilization. However, applying traditional deep reinforcement learning (DRL) is often infeasible due to its immense sample complexi…
Deep Reinforcement LearningFairnessMeta-LearningMeta Reinforcement Learning