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.
Papers
Fully automatic extraction of morphological traits from the Web: utopia or reality?
Diego Marcos, Robert van de Vlasakker, Ioannis N. Athanasiadis, Pierre Bonnet, Hervé Goeau, Alexis Joly, W. Daniel Kissling, César Leblanc, André S.J. van Proosdij, Konstantinos P. Panousis
Reflecting Reality: Enabling Diffusion Models to Produce Faithful Mirror Reflections
Ankit Dhiman, Manan Shah, Rishubh Parihar, Yash Bhalgat, Lokesh R Boregowda, R Venkatesh Babu
A Survey on Diffusion Models for Recommender Systems
Jianghao Lin, Jiaqi Liu, Jiachen Zhu, Yunjia Xi, Chengkai Liu, Yangtian Zhang, Yong Yu, Weinan Zhang
Anomaly Detection for Real-World Cyber-Physical Security using Quantum Hybrid Support Vector Machines
Tyler Cultice, Md. Saif Hassan Onim, Annarita Giani, Himanshu Thapliyal