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Long-term recurrent convolutional networks for visual recognition and description.

, , , , , , and . CVPR, page 2625-2634. IEEE Computer Society, (2015)

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Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data., , , , , and . CoRR, (2015)Women Also Snowboard: Overcoming Bias in Captioning Models., , , , and . ECCV (3), volume 11207 of Lecture Notes in Computer Science, page 793-811. Springer, (2018)An empirical analysis of compute-optimal large language model training., , , , , , , , , and 12 other author(s). NeurIPS, (2022)Women also Snowboard: Overcoming Bias in Captioning Models., , , and . CoRR, (2018)Scaling Language Models: Methods, Analysis & Insights from Training Gopher., , , , , , , , , and 70 other author(s). CoRR, (2021)Training Compute-Optimal Large Language Models., , , , , , , , , and 12 other author(s). CoRR, (2022)Long-Term Recurrent Convolutional Networks for Visual Recognition and Description., , , , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 39 (4): 677-691 (2017)Localizing Moments in Video with Temporal Language., , , , , and . EMNLP, page 1380-1390. Association for Computational Linguistics, (2018)Taxonomy of Risks posed by Language Models., , , , , , , , , and 13 other author(s). FAccT, page 214-229. ACM, (2022)Representation in AI Evaluations., , , , , , , , and . FAccT, page 519-533. ACM, (2023)