News
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2 papers accepted to CVPR 2022. 1 paper accepted to ICCS 2022.
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We are hiring research interns for 2022!
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We release a tensor learning library for PyTorch users (TensorLy-Torch). Check out the website to learn how this can help with your deep learning models. [website] [github]
Research Interests
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Recurrent Neural Network (RNN), Muiti-Dimensional LSTM
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Multi-Dimensional Sequence Learning, Spatio-Temporal Learning
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Predictive Learning, Few-shot Learning, Lifelong Learning
Selected Projects
- 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
- NeurIPS’21 Workshop on ML and the Physical Science
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J Su, W Byeon, F Huang, “Scaling-up Diverse Orthogonal Convolutional Networks with a Paraunitary Framework”, arXiv, 2021
- J Su*, W Byeon*, F Huang, J Kautz, A Anandkumar, “Convolutional Tensor-Train LSTM for Spatio-temporal Learning”, NeurIPS 2020 (*) equal contributions
- [Project page]
- ECCV’20 Tutorial on Accelerating Computer Vision with Mixed Precision.
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W Byeon, Q Wang, R K Srivastava, P Koumoutsakos, “ContextVP: Fully Context-Aware Video Prediction”, ECCV 2018 (oral)
- PR Vlachas, W Byeon, ZY Wan, TP Sapsis, P Koumoutsakos, “Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks”, Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences. 2018