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Train and You'll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings.

, , , , , , , and . CoRR, (2020)

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Compiling Affine Loop Nests for a Dynamic Scheduling Runtime on Shared and Distributed Memory., , and . ACM Trans. Parallel Comput., 3 (2): 12:1-12:28 (2016)Dynamic Model Specialization for Efficient Inference, Training and Supervision.. Carnegie Mellon University, USA, (2022)Train and You'll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings., , , , , , , and . CoRR, (2020)Background Splitting: Finding Rare Classes in a Sea of Background., , , , and . CVPR, page 8043-8052. Computer Vision Foundation / IEEE, (2021)Learning Rare Category Classifiers on a Tight Labeling Budget., , , , and . ICCV, page 8403-8412. IEEE, (2021)Learning to Move with Affordance Maps., , , and . ICLR, OpenReview.net, (2020)Online Model Distillation for Efficient Video Inference, , , , and . (2018)cite arxiv:1812.02699.Background Splitting: Finding Rare Classes in a Sea of Background., , , , and . CoRR, (2020)Low-Shot Validation: Active Importance Sampling for Estimating Classifier Performance on Rare Categories., , , , , , and . ICCV, page 10685-10694. IEEE, (2021)HydraNets: Specialized Dynamic Architectures for Efficient Inference., , , and . CVPR, page 8080-8089. Computer Vision Foundation / IEEE Computer Society, (2018)