222 citations · 226 across the 3 of their papers we have counts for
5 papers
Backpropagating Verifier Rewards Through Continuous Flow Language Models
Xu Han, Jiajing Hu, Li-Ping Liu
Reinforcement learning (RL) has substantially improved the reasoning of autoregressive and discrete-diffusion language models. Language models that generate continuous token embedd…
Deterministic Prediction as Global Velocity: Flow Matching for Probabilistic Time-Series Forecast
Jiajing Hu, Xu Han, Li-Ping Liu
Accurate uncertainty quantification is critical for many time series forecasting applications, and recent studies have increasingly adopted generative models to capture predictive…
Self-Consistent Flow: Unifying Velocity and Endpoint Prediction for Rectified Flow Models
Xu Han, Jiajing Hu, Li-Ping Liu
In rectified-flow-based generative models, the neural network can be trained to predict two different targets, such as the instantaneous velocity or the data endpoint, to perform d…
DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science
Mufei Li, Jinjing Zhou, Jiajing Hu +4
Graph neural networks (GNNs) constitute a class of deep learning methods for graph data. They have wide applications in chemistry and biology, such as molecular property prediction…
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
Xiaohui Chen, Xu Han, Jiajing Hu +2
A graph generative model defines a distribution over graphs. One type of generative model is constructed by autoregressive neural networks, which sequentially add nodes and edges t…