3 citations · 6 across the 5 of their papers we have counts for
6 papers
ChefFusion: Multimodal Foundation Model Integrating Recipe and Food Image Generation
Peiyu Li, Xiaobao Huang, Yijun Tian +1
Significant work has been conducted in the domain of food computing, yet these studies typically focus on single tasks such as t2t (instruction generation from food titles and ingr…
FakeEdge: Alleviate Dataset Shift in Link Prediction
Kaiwen Dong, Yijun Tian, Zhichun Guo +2
Link prediction is a crucial problem in graph-structured data. Due to the recent success of graph neural networks (GNNs), a variety of GNN-based models were proposed to tackle the…
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning
Chunhui Zhang, Chao Huang, Yijun Tian +5
Even pruned by the state-of-the-art network compression methods, Graph Neural Networks (GNNs) training upon non-Euclidean graph data often encounters relatively higher time costs,…
RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation
Yijun Tian, Chuxu Zhang, Zhichun Guo +3
Recipe recommendation systems play an essential role in helping people decide what to eat. Existing recipe recommendation systems typically focused on content-based or collaborativ…
Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks
Yijun Tian, Chuxu Zhang, Zhichun Guo +3
Learning effective recipe representations is essential in food studies. Unlike what has been developed for image-based recipe retrieval or learning structural text embeddings, the…
Quasi-experimental Designs for Assessing Response on Social Media to Policy Changes
Yijun Tian, Rumi Chunara
Regulation of tobacco products is rapidly evolving. Understanding public sentiment in response to changes is very important as authorities assess how to effectively protect populat…