Full Potential
"Full potential" research explores maximizing the capabilities of various models and algorithms across diverse fields. Current efforts focus on improving model performance in tasks like program repair, multimodal search, and medical image segmentation, often leveraging large language models (LLMs), diffusion models, and graph neural networks. This research is significant because it aims to enhance the efficiency and accuracy of existing technologies, leading to advancements in areas such as software development, AI-assisted content creation, and healthcare diagnostics. The ultimate goal is to unlock the full capabilities of these models for practical applications and scientific discovery.
Papers
Assessing the Potential of PlanetScope Satellite Imagery to Estimate Particulate Matter Oxidative Potential
Ian Hough, Loïc Argentier, Ziyang Jiang, Tongshu Zheng, Mike Bergin, David Carlson, Jean-Luc Jaffrezo, Jocelyn Chanussot, Gaëlle Uzu
Investigating the potential of Sparse Mixtures-of-Experts for multi-domain neural machine translation
Nadezhda Chirkova, Vassilina Nikoulina, Jean-Luc Meunier, Alexandre Bérard
Unleashing the Potential of Open-set Noisy Samples Against Label Noise for Medical Image Classification
Zehui Liao, Shishuai Hu, Yong Xia
Unlocking the Potential of Early Epochs: Uncertainty-aware CT Metal Artifact Reduction
Xinquan Yang, Guanqun Zhou, Wei Sun, Youjian Zhang, Zhongya Wang, Jiahui He, Zhicheng Zhang
Unlocking the Potential of Metaverse in Innovative and Immersive Digital Health
Fatemeh Ebrahimzadeh, Ramin Safa
Teaching with Uncertainty: Unleashing the Potential of Knowledge Distillation in Object Detection
Junfei Yi, Jianxu Mao, Tengfei Liu, Mingjie Li, Hanyu Gu, Hui Zhang, Xiaojun Chang, Yaonan Wang
Predicting solvation free energies with an implicit solvent machine learning potential
Sebastien Röcken, Anton F. Burnet, Julija Zavadlav
Unveiling the Potential of AI for Nanomaterial Morphology Prediction
Ivan Dubrovsky, Andrei Dmitrenko, Aleksei Dmitrenko, Nikita Serov, Vladimir Vinogradov
Unleashing the Potential of Diffusion Models for Incomplete Data Imputation
Hengrui Zhang, Liancheng Fang, Philip S. Yu