Real World
Research on "real-world" applications focuses on bridging the gap between simulated and real-world environments, particularly for complex tasks like robotics, autonomous driving, and natural language processing. Current efforts utilize various model architectures, including large language models (LLMs), diffusion models, reinforcement learning (RL), and graph neural networks, to improve robustness, generalization, and efficiency in diverse real-world scenarios. This research is crucial for advancing AI capabilities beyond controlled settings and enabling practical applications in areas such as healthcare, manufacturing, and transportation, while also addressing challenges like data scarcity, safety, and bias.
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Papers - Page 22
January 29, 2023
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January 4, 2023
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December 5, 2022
December 4, 2022
Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidance
Vanshaj Khattar, Ming JinTime-Synchronized Full System State Estimation Considering Practical Implementation Challenges
Antos Cheeramban Varghese, Hritik Shah, Behrouz Azimian, Anamitra Pal, Evangelos Farantatos
December 2, 2022
Cloud-Device Collaborative Adaptation to Continual Changing Environments in the Real-world
Yulu Gan, Mingjie Pan, Rongyu Zhang, Zijian Ling, Lingran Zhao, Jiaming Liu, Shanghang ZhangNavigating to Objects in the Real World
Theophile Gervet, Soumith Chintala, Dhruv Batra, Jitendra Malik, Devendra Singh Chaplot
November 19, 2022
November 5, 2022