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Large Language Models as Automated Aligners for benchmarking Vision-Language Models., , , , , , и . ICLR, OpenReview.net, (2024)Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification., , , , , , , , , и 10 other автор(ы). NeurIPS (Competition and Demos), том 176 из Proceedings of Machine Learning Research, стр. 80-96. PMLR, (2021)Large Language Models as Automated Aligners for benchmarking Vision-Language Models., , , , , , и . CoRR, (2023)FIMO: A Challenge Formal Dataset for Automated Theorem Proving., , , , , , , , , и 2 other автор(ы). CoRR, (2023)A Survey of Reasoning with Foundation Models., , , , , , , , , и 24 other автор(ы). CoRR, (2023)Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019, , , , , , , , , и 15 other автор(ы). HAL, (сентября 2020)Deep Statistical Solvers., , , , , и . NeurIPS, (2020)Advances in MetaDL: AAAI 2021 Challenge and Workshop., , , , , и . MetaDL@AAAI, том 140 из Proceedings of Machine Learning Research, стр. 1-16. PMLR, (2021)LEGO-Prover: Neural Theorem Proving with Growing Libraries., , , , , , , , , и 4 other автор(ы). CoRR, (2023)FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving., , , , , , , и . CoRR, (2024)