7 citations · 12 across the 20 of their papers we have counts for
8 papers · 1 filter
Graph World Model
Tao Feng, Yexin Wu, Guanyu Lin +1
World models (WMs) demonstrate strong capabilities in prediction, generation, and planning tasks. Existing WMs primarily focus on unstructured data and cannot leverage the ubiquito…
FusionFactory: Fusing LLM Capabilities with Multi-LLM Log Data
Tao Feng, Haozhen Zhang, Zijie Lei +5
The rapid advancement of large language models (LLMs) has created a diverse landscape of models, each excelling at different tasks. This diversity drives researchers to employ mult…
Causality Enhanced Origin-Destination Flow Prediction in Data-Scarce Cities
Tao Feng, Yunke Zhang, Huandong Wang +1
Accurate origin-destination (OD) flow prediction is of great importance to developing cities, as it can contribute to optimize urban structures and layouts. However, with the commo…
EPR-GAIL: An EPR-Enhanced Hierarchical Imitation Learning Framework to Simulate Complex User Consumption Behaviors
Tao Feng, Yunke Zhang, Huandong Wang +1
User consumption behavior data, which records individuals' online spending history at various types of stores, has been widely used in various applications, such as store recommend…
GraphEval: A Lightweight Graph-Based LLM Framework for Idea Evaluation
Tao Feng, Yihang Sun, Jiaxuan You
The powerful capabilities of Large Language Models (LLMs) have led to their growing use in evaluating human-generated content, particularly in evaluating research ideas within acad…
RDBench: ML Benchmark for Relational Databases
Zizhao Zhang, Yi Yang, Lutong Zou +3
Benefiting from high-quality datasets and standardized evaluation metrics, machine learning (ML) has achieved sustained progress and widespread applications. However, while applyin…
Molecular De Novo Design through Transformer-based Reinforcement Learning
Pengcheng Xu, Tao Feng, Tianfan Fu +2
In this work, we introduce a method to fine-tune a Transformer-based generative model for molecular de novo design. Leveraging the superior sequence learning capacity of Transforme…
Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey
Zefang Zong, Jingwei Wang, Tao Feng +3
Recent technology development brings the boom of numerous new Demand-Driven Services (DDS) into urban lives, including ridesharing, on-demand delivery, express systems and warehous…