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Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019, , , , , , , , , and 15 other author(s). IEEE Transactions on Pattern Analysis and Machine Intelligence, (2021)To appear.Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks, , , and . (2019)cite arxiv:1903.00954.UniGrasp: Learning a Unified Model to Grasp With Multifingered Robotic Hands., , , , , , , , and . IEEE Robotics Autom. Lett., 5 (2): 2286-2293 (2020)Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How., , , , and . CoRR, (2023)Detecting code smells in React-based Web apps., and . Inf. Softw. Technol., (2023)FICAvis: Data Visualization to Prevent University Dropout., , , and . IV, page 57-62. IEEE, (2020)Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification., , , , , , , , , and 2 other author(s). CoRR, (2022)On the (un-)adoption of JavaScript front-end frameworks., , and . Softw. Pract. Exp., 52 (4): 947-966 (2022)Real-time Evaluation System for Top Taekwondo Athletes: Project Overview., , , , , and . BIODEVICES, page 209-216. SCITEPRESS, (2021)Turnover in Open-Source Projects: The Case of Core Developers., , and . SBES, page 447-456. ACM, (2020)