Learning Generative Model

Learning generative models focuses on creating algorithms that can produce new data instances similar to a training dataset, aiming to capture underlying data distributions. Current research emphasizes improving model efficiency and accuracy through techniques like multi-fidelity approaches (combining computationally expensive and inexpensive methods), and addressing challenges in long-form generation and high-throughput inference. These advancements are impacting diverse fields, from drug discovery (generating novel drug compounds) to speech enhancement and aircraft trajectory prediction, by enabling more realistic simulations and improved predictions.