← Learning Types
Machine Learning › Learning Types ›

Meta-Learning

357 directly classified papers

Papers per year

Papers

Learning-to-Learn Stochastic Gradient Descent with Biased Regularization ICML 2019
Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification EMNLP 2019
Meta-Learning With Differentiable Convex Optimization CVPR 2019
Few-Shot Learning With Localization in Realistic Settings CVPR 2019
Team Fernando-Pessa at SemEval-2019 Task 4: Back to Basics in Hyperpartisan News Detection SEMEVAL 2019
Compositional generalization through meta sequence-to-sequence learning NIPS 2019
Cross Attention Network for Few-shot Classification NIPS 2019
Discovery of Useful Questions as Auxiliary Tasks NIPS 2019
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning NIPS 2019
Machine Theory of Mind ICML 2018
Meta-Learning MCMC Proposals NIPS 2018
DiCE: The Infinitely Differentiable Monte Carlo Estimator ICML 2018
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior NIPS 2018
Probabilistic Matrix Factorization for Automated Machine Learning NIPS 2018
Learning To Learn Around A Common Mean NIPS 2018
Modular meta-learning CORL 2018
Meta-Gradient Reinforcement Learning NIPS 2018
Few-Shot Goal Inference for Visuomotor Learning and Planning CORL 2018
Meta-Level Control of Anytime Algorithms with Online Performance Prediction IJCAI 2018
Learning to Reweight Examples for Robust Deep Learning ICML 2018
Low-Shot Learning From Imaginary Data CVPR 2018
Gradient-free Policy Architecture Search and Adaptation CORL 2017
One-Shot Visual Imitation Learning via Meta-Learning CORL 2017
One-Shot Imitation Learning NIPS 2017
A Genetic Algorithm for Learning Parameters in Bayesian Networks using Expectation Maximization PGM 2016