Learning Paradigms

Learning to Learn Cropping Models for Different Aspect Ratio Requirements CVPR 2020 Learning to Learn Kernels with Variational Random Features ICML 2020 Learning to Learn Morphological Inflection for Resource-Poor Languages AAAI 2020 Learning to Learn Single Domain Generalization CVPR 2020 Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation EMNLP 2020 Learning to Mutate with Hypergradient Guided Population NeurIPS 2020 Learning to Optimize on SPD Manifolds CVPR 2020 Learning to Select Base Classes for Few-Shot Classification CVPR 2020 Make One-Shot Video Object Segmentation Efficient Again NeurIPS 2020 MATE: Plugging in Model Awareness to Task Embedding for Meta Learning NeurIPS 2020 MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler NeurIPS 2020 Meta-Amortized Variational Inference and Learning AAAI 2020 Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation AAAI 2020 Meta Dropout: Learning to Perturb Latent Features for Generalization ICLR 2020 Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining EMNLP 2020 MetaFuse: A Pre-trained Fusion Model for Human Pose Estimation CVPR 2020 MetaIQA: Deep Meta-Learning for No-Reference Image Quality Assessment CVPR 2020 MetAL: Active Semi-Supervised Learning on Graphs via Meta-Learning ACML 2020 Meta-Learning for Few-Shot NMT Adaptation ACL 2020 Meta-Learning for Generalized Zero-Shot Learning AAAI 2020 Meta-Learning for Short Utterance Speaker Recognition with Imbalance Length Pairs Interspeech 2020 Meta-Learning of Neural Architectures for Few-Shot Learning CVPR 2020 Meta-Learning on Graph with Curvature-Based Analysis (Student Abstract) AAAI 2020 Meta-Learning PAC-Bayes Priors in Model Averaging AAAI 2020 Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes NeurIPS 2020