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Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation.

, and . CoRR, (2019)

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Multi-period Trading Prediction Markets with Connections to Machine Learning., and . ICML, volume 32 of JMLR Workshop and Conference Proceedings, page 1773-1781. JMLR.org, (2014)Training Deep Convolutional Neural Networks to Play Go., and . ICML, volume 37 of JMLR Workshop and Conference Proceedings, page 1766-1774. JMLR.org, (2015)Extracting Motion Primitives from Natural Handwriting Data., , and . ICANN (2), volume 4132 of Lecture Notes in Computer Science, page 634-643. Springer, (2006)How to train your MAML., , and . ICLR (Poster), OpenReview.net, (2019)A Probabilistic Model-Based Approach to Consistent White Matter Tract Segmentation., , and . IEEE Trans. Med. Imaging, 26 (11): 1555-1561 (2007)Improved segmentation reproducibility in group tractography using a quantitative tract similarity measure., , and . NeuroImage, 33 (2): 482-492 (2006)Deep attention super-resolution of brain magnetic resonance images acquired under clinical protocols., , , , , , , , , and 3 other author(s). Frontiers Comput. Neurosci., (2022)Detection of multiple retinal diseases in ultra-widefield fundus images using deep learning: data-driven identification of relevant regions., , , , , and . CoRR, (2022)HAKD: Hardware Aware Knowledge Distillation., , , , , and . CoRR, (2018)Adversarial robustness of β-VAE through the lens of local geometry., and . CoRR, (2022)