Deep Model
Deep models, encompassing a broad range of neural network architectures, aim to learn complex patterns from data for various tasks like image classification, time series forecasting, and system identification. Current research emphasizes improving efficiency (e.g., through constant-time learning algorithms and layer caching), enhancing explainability (e.g., via gradient-free methods), and mitigating issues like bias and memorization. These advancements are significant because they improve the reliability, trustworthiness, and applicability of deep models across diverse scientific fields and real-world applications, including healthcare, finance, and autonomous systems.
Papers
Preparing Lessons for Progressive Training on Language Models
Yu Pan, Ye Yuan, Yichun Yin, Jiaxin Shi, Zenglin Xu, Ming Zhang, Lifeng Shang, Xin Jiang, Qun Liu
Hybrid deep learning and physics-based neural network for programmable illumination computational microscopy
Ruiqing Sun, Delong Yang, Shaohui Zhang, Qun Hao