Financial Application
Financial applications of artificial intelligence are rapidly expanding, driven by the need for efficient and accurate analysis of complex financial data. Current research focuses on developing and adapting various machine learning models, including large language models (LLMs), deep learning architectures (like YOLO and Swin-Unet), and optimization algorithms (e.g., those incorporating reinforcement learning and model predictive control), to handle diverse data types (text, images, time series) and tasks (prediction, classification, generation). This work is significant because it promises to improve decision-making, risk management, and resource allocation across various financial sectors, while also advancing the broader field of AI through the development of novel algorithms and model architectures tailored to specific financial challenges.
Papers
Integrated utilization of equations and small dataset in the Koopman operator: applications to forward and inverse Problems
FinAudio: A Benchmark for Audio Large Language Models in Financial Applications
UniEDU: A Unified Language and Vision Assistant for Education Applications
A Multilingual, Culture-First Approach to Addressing Misgendering in LLM Applications