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) (https://arxiv.org/abs/2309.14394)
- 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
- T. Mayet, P. Shamsolmoali, S. Bernard, E. Granger, R. Herault, and C. Chatelain, « TD-Paint: Faster Diffusion Inpainting Through Time-aware Pixel Conditioning, » in ICLR, 2025. (https://arxiv.org/abs/2410.09306)
- D. Jain, T. Mayet, R. Herault, and R. Modzelewski, « One-Class SVM-guided Negative Sampling for Enhanced Contrastive Learning », Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL), PMLR 265, 2025
Articles présentés en groupe de lecture :
- Chen,T., Kornblith,S., Norouzi, M., Hinton, G. (2020) A Simple Framework for Contrastive Learning of Visual Representations. (https://arxiv.org/abs/2002.05709)
- Haibo,J., Shengcai,L., Ling,S. (2020). Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild. (https://arxiv.org/abs/2003.03771 )
- Xu, D., Ruan, C., Korpeoglu, E., Kumar, S., & Achan, K. (2020). Inductive representation learning on temporal graphs. (https://arxiv.org/abs/2002.07962)
- Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models.(https://arxiv.org/abs/2006.11239)
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)
- Jain, Dhruv & Modzelewski, Romain & Hérault, Romain & Chatelain, Clement & Thureau, Sebastien. (2023). Multi-Modal U-net for Segmenting Gross Tumor Volume in Lungs during Radiotherapy. 10.20944/preprints202304.0129.v1.
