Hierarchical Latent

Hierarchical latent variable models aim to represent complex data by organizing information into nested layers of abstraction, capturing both fine-grained details and high-level concepts. Current research focuses on developing and applying these models across diverse domains, employing architectures like variational autoencoders, diffusion models, and recurrent neural networks, often within a Bayesian or probabilistic framework. This approach enhances interpretability, improves the handling of high-dimensional and noisy data, and facilitates tasks such as anomaly detection, image synthesis, and multi-agent trajectory prediction, with applications spanning various fields including computer vision, natural language processing, and time series analysis.