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Large Language Models as Automated Aligners for benchmarking Vision-Language Models., , , , , , and . CoRR, (2023)Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification., , , , , , , , , and 10 other author(s). NeurIPS (Competition and Demos), volume 176 of Proceedings of Machine Learning Research, page 80-96. PMLR, (2021)Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019, , , , , , , , , and 15 other author(s). HAL, (September 2020)LEGO-Prover: Neural Theorem Proving with Growing Libraries., , , , , , , , , and 4 other author(s). CoRR, (2023)Deep Statistical Solvers., , , , , and . NeurIPS, (2020)Advances in MetaDL: AAAI 2021 Challenge and Workshop., , , , , and . MetaDL@AAAI, volume 140 of Proceedings of Machine Learning Research, page 1-16. PMLR, (2021)FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving., , , , , , , and . CoRR, (2024)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.Learning to Prove Trigonometric Identities., , , , and . CoRR, (2022)Gaining Wisdom from Setbacks: Aligning Large Language Models via Mistake Analysis., , , , , , , , , and 4 other author(s). CoRR, (2023)