News

• 1 paper accepted to CVPR 2024.

• 1 paper accepted to NeurIPS 2023.

• 1 paper accepted to ICCV 2023. 2 papers accepted to CVPR 2023.

Research Interests

• Recurrent Neural Network (RNN), State-Space Models (SSM), Linear RNNs

• Sequence Learning, Spatio-Temporal Learning

• Predictive Learning, Few-shot Learning, Lifelong Learning

Selected Projects

• J T.H. Smith, S De Mello, J Kautz, S W. Linderman, W Byeon, “Convolutional State Space Models for Long-Range Spatiotemporal Modeling”, NeurIPS, 2023

• J Su, W Byeon, F Huang, “Scaling-up Diverse Orthogonal Convolutional Networks with a Paraunitary Framework”, ICML, 2022

• B Wu*, O Hennigh, J Kautz, S Choudhry, W Byeon*, “Physics Informed RNN-DCT Networks for Time-Dependent Partial Differential Equations”, ICCS 2022 (*) equal contributions

  • Presented at NeurIPS’21 Workshop on ML and the Physical Science
  • Released as part of NVIDIA Mudulus