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An Empirical Study on Google Research Football Multi-agent Scenarios.

, , , , , , , , and . Mach. Intell. Res., 21 (3): 549-570 (June 2024)

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Lipschitz Generative Adversarial Nets., , , , , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 7584-7593. PMLR, (2019)AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods., , , , , and . ICLR (Poster), OpenReview.net, (2019)Aggregating Crowd Wisdom with Side Information via a Clustering-based Label-aware Autoencoder., , , and . IJCAI, page 1542-1548. ijcai.org, (2020)Scheduled for July 2020, Yokohama, Japan, postponed due to the Corona pandemic..Improving Unsupervised Domain Adaptation with Variational Information Bottleneck., , , , , , , and . CoRR, (2019)Towards Efficient and Unbiased Implementation of Lipschitz Continuity in GANs., , , , and . CoRR, (2019)Generative Adversarial Nets for Information Retrieval: Fundamentals and Advances.. SIGIR, page 1375-1378. ACM, (2018)CoT: Cooperative Training for Generative Modeling., , , and . CoRR, (2018)MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence., , , , , and . CoRR, (2017)SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient., , , and . CoRR, (2016)On the Equilibrium of Query Reformulation and Document Retrieval., , , , and . ICTIR, page 43-50. ACM, (2018)