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Revisiting Fundamentals of Experience Replay.

, , , , , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 3061-3071. PMLR, (2020)

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Deep Graph Infomax., , , , , and . ICLR (Poster), OpenReview.net, (2019)Revisiting Fundamentals of Experience Replay., , , , , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 3061-3071. PMLR, (2020)Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models., , , , , , , , , and 440 other author(s). Trans. Mach. Learn. Res., (2023)Emergent Abilities of Large Language Models., , , , , , , , , and 6 other author(s). Trans. Mach. Learn. Res., (2022)Language GANs Falling Short., , , , , and . CoRR, (2018)Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step., , , , , and . ICLR (Poster), OpenReview.net, (2018)Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity., , and . J. Mach. Learn. Res., (2022)Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?, , , , , , , , , and . EMNLP (Findings), page 12342-12364. Association for Computational Linguistics, (2023)MaskGAN: Better Text Generation via Filling in the _______., , and . ICLR (Poster), OpenReview.net, (2018)Recall Traces: Backtracking Models for Efficient Reinforcement Learning., , , , , , , and . ICLR (Poster), OpenReview.net, (2019)