- Learning Long Range Dependencies on Graphs via Random Walks
Dexiong Chen, Till Hendrik Schulz, Karsten Borgwardt
Preprint, 2024
- Endowing Protein Language Models with Structural Knowledge
Dexiong Chen, Philip Hartout, Paolo Pellizzoni, Carlos Oliver, Karsten Borgwardt
Preprint, 2024
- Biomarker Identification by Interpretable Maximum Mean Discrepancy
Michael F Adamer, Sarah C Brüningk, Dexiong Chen, Karsten Borgwardt
ISMB, 2024
- SURE: SUrvey REcipes for building reliable and robust deep networks
Yuting Li, Yingyi Chen, Xuanlong Yu, Dexiong Chen, Xi Shen
CVPR, 2024
- ProteinShake: Building Datasets and Benchmarks for Deep Learning on Protein Structures
Tim Kucera, Carlos Oliver, Dexiong Chen, Karsten Borgwardt
NeurIPS (Datasets and Benchmarks), 2023
- Fisher Information Embedding for Node and Graph Learning
Dexiong Chen, Paolo Pellizzoni, Karsten Borgwardt
ICML, 2023
- Unsupervised Manifold Alignment with Joint Multidimensional Scaling
Dexiong Chen, Bowen Fan, Carlos Oliver, Karsten Borgwardt
ICLR, 2023
- Approximate Network Motif Mining via Graph Learning
Carlos Oliver, Dexiong Chen, Vincent Mallet, Pericles Philippopoulos, Karsten Borgwardt
Preprint, 2022
- Predicting in vitro single-neuron firing rates upon pharmacological perturbation using graph neural networks
Taehoon Kim, Dexiong Chen, Philipp Hornauer, Vishalini Emmenegger, Julian Bartram, Silvia Ronchi, Andreas R. Hierlemann, Manuel Schröter and Damian Roqueiro
Frontiers in Neuroinformatics, 2022
- Structure-Aware Transformer for Graph Representation Learning
Dexiong Chen, Leslie O’Bray, Karsten Borgwardt
ICML, 2022
- GraphiT: Encoding Graph Structure in Transformers
Grégoire Mialon, Dexiong Chen, Margot Selosse, Julien Mairal
Preprint, 2021
- Structured Data Modeling with Deep Kernel Machines and Applications in Computational Biology
Dexiong Chen
PhD thesis, Université Grenoble Alpes, 2020
- A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention
Grégoire Mialon, Dexiong Chen, Alexandre d’Aspremont, Julien Mairal
ICLR, 2021
- Convolutional Kernel Networks for Graph-Structured Data
Dexiong Chen, Laurent Jacob, Julien Mairal
In ICML, 2020
- Recurrent Kernel Networks
Dexiong Chen, Laurent Jacob, Julien Mairal
NeurIPS, 2019 and accepted as an oral presentation in MLCB, 2019
- A Kernel Perspective for Regularizing Deep Neural Networks
Alberto Bietti, Grégoire Mialon, Dexiong Chen, Julien Mairal
ICML, 2019
- Biological Sequence Modeling with Convolutional Kernel Networks
Dexiong Chen, Laurent Jacob, Julien Mairal
Research in Computational Molecular Biology (RECOMB), 2019 and Bioinformatics, 2019