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Diffusion Augmented Agents, a new framework developed by researchers at Imperial College London and DeepMind, aims to address the data scarcity challenge in training embodied AI. This approach enables agents to learn tasks more efficiently by leveraging diffusion models for exploration and transfer learning, as highlighted by Di Palo et al. The framework is part of ongoing efforts to enhance artificial intelligence capabilities, particularly in real-world navigation and interaction.
[CL] Better Alignment with Instruction Back-and-Forth Translation T Nguyen, J Li, S Oh, L Schmidt, J Weston, L Zettlemoyer, X Li [University of Washington] (2024) https://t.co/DnUyJSiFXc - The paper proposes a new method called instruction back-and-forth translation to construct… https://t.co/T4LL8YebWK
Imperial College London, DeepMind introduce embodied agents that learn with less data: Diffusion Augmented Agent proposed by the Imperial College and DeepMind team, is designed to enable agents to learn tasks more efficiently. https://t.co/FgKniC44YE #AI #Business
[CL] Diffusion Guided Language Modeling J Lovelace, V Kishore, Y Chen, K Q. Weinberger [Cornell University] (2024) https://t.co/JrpNlBmJBn - Current language models demonstrate remarkable proficiency in text generation, but for many applications it is desirable to control… https://t.co/iNXYXL14gG
