PhD student, Korea Advanced Institute of Science & Technology
2 papers at NeurIPS 2025
We propose a scalable and sample-efficient framework for training diffusion samplers by integrating classical sampling methods, suitable for practical applications like molecular conformer generation.
Energy-based training of neural continuous-time Markov processes in general state space.
