Publications

Publications du projet :

  • Tsiry Mayet, Simon Bernard, Clément Chatelain, Romain Hérault: Multiple Noises in Diffusion Model for Semi-Supervised Multi-Domain Translation (submitted) CoRR abs/2309.14394 (2023)
  • L. Yang, C. Chatelain, and S. Adam, « Dynamic graph representation learning with neural networks: a survey, » IEEE Access, vol. 12, pp. 43460-43484, 2024 (https://ieeexplore.ieee.org/document/10473053)
  • L. Yang, C. Chatelain, and S. Adam, « Inductive anomaly detection in dynamic graphs with accumulative causal walk alignment, » in Machine Learning on Graph @ ECML, 2024.
  • D. Jain and ‪R. Modzelewski and R. Herault and C. Chatelain and S. Thureau, Multi-Modal U-net for Segmenting Gross Tumor Volume in Lungs during Radiotherapy, submitted (2023)
  • Tsiry Mayet, Simon Bernard, Clément Chatelain, Romain Hérault: Domain Translation via Latent Space Mapping. IJCNN 2023: 1-10 (https://arxiv.org/abs/2212.03361)
  • L. Yang, C. Chatelain, and S. Adam, « DspGNN: Bringing Spectral Design to Discrete Time Dynamic Graph Neural Networks for Edge Regression, » in Temporal Graph Learning Workshop@NeurIPS, 2023

Articles présentés en groupe de lecture :

Articles recommandés :

  • Lu,K., Grover,A., Abbeel, P., Mordatch,I. (2021) Pretrained Transformers as Universal Computation Engines. (https://arxiv.org/abs/2103.05247)
  • Jiang,D., Lei,X., Wubo Li,W., Luo,N., Hu,Y., Zou,W., Li,X. (2019) Improving Transformer-based Speech Recognition Using Unsupervised Pre-training.(https://arxiv.org/abs/1910.09932)
  • Zoph,B., Ghiasi,G., Lin,T., Cui,Y., Liu,H., Cubuk,E.D., Le, Q.V. (2020) Rethinking Pre-training and Self-training » : (https://arxiv.org/abs/2006.06882)
  • Guo, S., Huang,W., Zhang,H., Zhuang,C., Dong,D., Scott,M.R. , Huang,D. (2018) CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images (https://arxiv.org/abs/1808.01097)