Abstract is missing.
- Advances in MetaDL: AAAI 2021 Challenge and WorkshopAdrian El Baz, Isabelle Guyon, Zhengying Liu, Jan N. van Rijn, Sébastien Treguer, Joaquin Vanschoren. 1-16 [doi]
- MetaDelta: A Meta-Learning System for Few-shot Image ClassificationYudong Chen, Chaoyu Guan, Zhikun Wei, Xin Wang 0019, Wenwu Zhu 0001. 17-28 [doi]
- Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural networkTomás Chobola, Daniel Vasata, Pavel Kordík. 29-37 [doi]
- Stress Testing of Meta-learning Approaches for Few-shot LearningAroof Aimen, Sahil Sidheekh, Vineet Madan, Narayanan C. Krishnan. 38-44 [doi]
- Semi-Supervised Few-Shot Learning with Prototypical Random WalksAhmed Ayyad, Yuchen Li, Raden Muaz, Shadi Albarqouni, Mohamed Elhoseiny. 45-57 [doi]
- Is Algorithm Selection Worth It? Comparing Selecting Single Algorithms and Parallel ExecutionHaniye Kashgarani, Lars Kotthoff. 58-64 [doi]
- Learning to Continually Learn Rapidly from Few and Noisy DataNicholas I-Hsien Kuo, Mehrtash Harandi, Nicolas Fourrier, Christian Walder, Gabriela Ferraro, Hanna Suominen. 65-76 [doi]
- Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot LearningHenry Kvinge, Zachary New, Nico Courts, Jung H. Lee, Lauren A. Phillips, Courtney D. Corley, Aaron Tuor, Andrew Avila, Nathan O. Hodas. 77-89 [doi]
- Exploiting Performance-based Similarity between Datasets in MetalearningRui Leite, Pavel Brazdil. 90-99 [doi]
- Asymptotic Analysis of Meta-learning as a Recommendation ProblemZhengying Liu, Isabelle Guyon. 100-114 [doi]
- Few-Shot Learning for Road Object DetectionAnay Majee, Kshitij Agrawal, Anbumani Subramanian. 115-126 [doi]
- Learning Abstract Task RepresentationsMikhail M. Meskhi, Adriano Rivolli, Rafael G. Mantovani, Ricardo Vilalta. 127-137 [doi]
- Challenges of Acquiring Compositional Inductive Biases via Meta-LearningEric Mitchell, Chelsea Finn, Christopher D. Manning. 138-148 [doi]