Generative Modeling

Generative modeling aims to create new data instances that resemble a given dataset, focusing on learning the underlying probability distribution. Current research emphasizes hybrid approaches combining the strengths of autoregressive models (for global context) and diffusion models (for high-quality local details), as well as advancements in flow-based models and score-based methods. These techniques are significantly impacting diverse fields, including image generation, 3D modeling, time series forecasting, and even scientific applications like molecular dynamics simulation and medical image synthesis, by enabling the creation of realistic and diverse synthetic data.