1 citations · 3 across the 6 of their papers we have counts for
6 papers
Communicability-Inspired Positional Encoding (CIPE)
Yipeng Zhang, Zhongtian Sun, Pietro Liò +1
Positional encodings (PEs) are essential for Transformers. Yet designing effective PEs for non-Euclidean graphs remains challenging. Such encodings should ideally induce an Attenti…
Rhomboid Tiling for Geometric Graph Deep Learning
Yipeng Zhang, Longlong Li, Kelin Xia
Graph Neural Networks (GNNs) have proven effective for learning from graph-structured data through their neighborhood-based message passing framework. Many hierarchical graph clust…
Commutative algebra-enhanced topological data analysis
Chuanshen Hu, Yu Wang, Kelin Xia +2
Topological Data Analysis (TDA) combines computational topology and data science to extract and analyze intrinsic topological and geometric structures in data set in a metric space…
TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment
Bingqing Han, Yipeng Zhang, Longlong Li +2
Even with the significant advances of AlphaFold-Multimer (AF-Multimer) and AlphaFold3 (AF3) in protein complex structure prediction, their accuracy is still not comparable with mon…
Molecular topological deep learning for polymer property prediction
Cong Shen, Yipeng Zhang, Fei Han +1
Accurate and efficient prediction of polymer properties is of key importance for polymer design. Traditional experimental tools and density function theory (DFT)-based simulations…
KA-GNN: Kolmogorov-Arnold Graph Neural Networks for Molecular Property Prediction
Longlong Li, Yipeng Zhang, Guanghui Wang +1
As key models in geometric deep learning, graph neural networks have demonstrated enormous power in molecular data analysis. Recently, a specially-designed learning scheme, known a…