| Sig‐Wasserstein GANs for conditional time series generation S Liao, H Ni, M Sabate‐Vidales, L Szpruch, M Wiese, B Xiao Mathematical Finance 34 (2), 622-670, 2024 | 161* | 2024 |
| Sig-Wasserstein GANs for Time Series Generation H Ni, L Szpruch, M Sabate-Vidales, B Xiao, M Wiese, S Liao The 2nd ACM International Conference on AI in Finance, 2021 | 73 | 2021 |
| Learning stochastic differential equations using RNN with log signature features S Liao, T Lyons, W Yang, H Ni arXiv preprint arXiv:1908.08286, 2019 | 48 | 2019 |
| Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition S Liao, T Lyons, W Yang, K Schlegel, H Ni The British Machine Vision Conference (BMVC) 2021, 2021 | 15 | 2021 |
| Conditional sig-wasserstein gans for time series generation. arXiv H Ni, L Szpruch, M Wiese, S Liao, B Xiao arXiv preprint arXiv:2006.05421, 2020 | 6 | 2020 |
| Conditional sig-wasserstein gans for time series generation. arXiv 2020 H Ni, L Szpruch, M Wiese, S Liao, B Xiao arXiv preprint arXiv:2006.05421, 0 | 6 | |
| Conditional sigwasserstein gans for time series generation (2020) H Ni, L Szpruch, M Wiese, S Liao, B Xiao arXiv preprint arXiv:2006.05421, 0 | 5 | |
| Forex Trading Volatility Prediction using Neural Network Models S Liao, J Chen, H Ni arXiv preprint arXiv:2112.01166, 2021 | 4 | 2021 |
| Log signatures in machine learning S Liao UCL (University College London), 2022 | 1 | 2022 |
