Meta-Learning

Identifying Physical Law of Hamiltonian Systems via Meta-Learning ICLR 2021 Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods NeurIPS 2021 Improved Meta-Learning Training for Speaker Verification Interspeech 2021 Improving Generalization in Meta-learning via Task Augmentation ICML 2021 Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics Mixture NeurIPS 2021 Improving Weakly Supervised Lesion Segmentation using Multi-Task Learning MIDL 2021 Iterative Teaching by Label Synthesis NeurIPS 2021 Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification CVPR 2021 Keep Learning: Self-supervised Meta-learning for Learning from Inference EACL 2021 KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification AAAI 2021 Knowledge Consolidation based Class Incremental Online Learning with Limited Data IJCAI 2021 Large-Scale Meta-Learning with Continual Trajectory Shifting ICML 2021 Learn2Hop: Learned Optimization on Rough Landscapes ICML 2021 Learned Initializations for Optimizing Coordinate-Based Neural Representations CVPR 2021 Learning Associative Inference Using Fast Weight Memory ICLR 2021 Learning sparse mixtures of permutations from noisy information COLT 2021 Learning to Adapt via Latent Domains for Adaptive Semantic Segmentation NeurIPS 2021 Learning To Filter: Siamese Relation Network for Robust Tracking CVPR 2021 Learning to Generate Noise for Multi-Attack Robustness ICML 2021 Learning to Generate Task-Specific Adapters from Task Description ACL 2021 Learning to Learn Dense Gaussian Processes for Few-Shot Learning NeurIPS 2021 Learning to Learn Graph Topologies NeurIPS 2021 Learning-to-learn non-convex piecewise-Lipschitz functions NeurIPS 2021 Learning to Learn Personalized Neural Network for Ventricular Arrhythmias Detection on Intracardiac EGMs IJCAI 2021 Learning to learn with Gaussian processes UAI 2021