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Navigating Scaling Laws: Compute Optimality in Adaptive Model Training., , , and . ICML, OpenReview.net, (2024)Learning Associative Inference Using Fast Weight Memory., , and . CoRR, (2020)Solving Quantitative Reasoning Problems with Language Models., , , , , , , , , and 4 other author(s). NeurIPS, (2022)Block-Recurrent Transformers., , , , and . NeurIPS, (2022)Ancient Roman Coin Recognition in the Wild Using Deep Learning Based Recognition of Artistically Depicted Face Profiles., and . ICCV Workshops, page 2898-2906. IEEE Computer Society, (2017)A Modern Self-Referential Weight Matrix That Learns to Modify Itself., , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 9660-9677. PMLR, (2022)Linear Transformers Are Secretly Fast Weight Programmers., , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 9355-9366. PMLR, (2021)Language Imbalance Can Boost Cross-lingual Generalisation., , , , and . CoRR, (2024)Learning to Reason with Third Order Tensor Products., and . NeurIPS, page 10003-10014. (2018)Going Beyond Linear Transformers with Recurrent Fast Weight Programmers., , , and . NeurIPS, page 7703-7717. (2021)