Hyperbolic Model

Hyperbolic models are gaining traction in machine learning due to their ability to effectively represent hierarchical and complex relationships within data, outperforming Euclidean models in certain applications. Current research focuses on applying hyperbolic embeddings within various architectures, including deep learning surrogates for complex systems like wildfire prediction, and hybrid models combining Euclidean and hyperbolic spaces for improved temporal knowledge graph reasoning and multimodal learning. This work is significant because it enhances the accuracy and efficiency of various machine learning tasks, from financial market analysis to federated learning and the modeling of physical phenomena like epidemic spread and fluid dynamics.