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
The Sample Complexity of Approximate Rejection Sampling with Applications to Smoothed Online Learning
Adam Block, Yury Polyanskiy
New directions in the applications of rough path theory
Adeline Fermanian, Terry Lyons, James Morrill, Cristopher Salvi
Gaze-based intention estimation: principles, methodologies, and applications in HRI
Anna Belardinelli
3D reconstruction from spherical images: A review of techniques, applications, and prospects
San Jiang, Yaxin Li, Duojie Weng, Kan You, Wu Chen
Nonlinear Random Matrices and Applications to the Sum of Squares Hierarchy
Goutham Rajendran
Machine Learning Capability: A standardized metric using case difficulty with applications to individualized deployment of supervised machine learning
Adrienne Kline, Joon Lee
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy
Anastasia Koloskova, Ryan McKenna, Zachary Charles, Keith Rush, Brendan McMahan
Generalized Uncertainty of Deep Neural Networks: Taxonomy and Applications
Chengyu Dong
A Survey on Compositional Generalization in Applications
Baihan Lin, Djallel Bouneffouf, Irina Rish
Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery
Tao Tang, Simon Mak, David Dunson
Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications
Muhammad Arslan Manzoor, Sarah Albarri, Ziting Xian, Zaiqiao Meng, Preslav Nakov, Shangsong Liang