2 citations · 2 across the 4 of their papers we have counts for
5 papers
GIFT-Eval-CTX: A Benchmark For Contextual Forecasting Model Evaluation
Xu Liu, Hanshu Yan, Taha Aksu +6
Contextual forecasting seeks to improve time series forecasting by incorporating forward-looking information, such as planned events, external conditions, and expert expectations.…
MFSR: MeanFlow Distillation for One Step Real-World Image Super Resolution
Ruiqing Wang, Kai Zhang, Yuanzhi Zhu +3
Diffusion- and flow-based models have advanced Real-world Image Super-Resolution (Real-ISR), but their multi-step sampling makes inference slow and hard to deploy. One-step distill…
Moirai 2.0: When Less Is More for Time Series Forecasting
Chenghao Liu, Taha Aksu, Juncheng Liu +7
We introduce Moirai 2.0, a decoder-only time-series foundation model trained on a new corpus of 36M series. The model adopts quantile forecasting and multi-token prediction, improv…
Accelerating Video Diffusion Models via Distribution Matching
Yuanzhi Zhu, Hanshu Yan, Huan Yang +2
Generative models, particularly diffusion models, have made significant success in data synthesis across various modalities, including images, videos, and 3D assets. However, curre…
OFTSR: One-Step Flow for Image Super-Resolution with Tunable Fidelity-Realism Trade-offs
Yuanzhi Zhu, Ruiqing Wang, Shilin Lu +3
Recent advances in diffusion and flow-based generative models have demonstrated remarkable success in image restoration tasks, achieving superior perceptual quality compared to tra…