{"id":58,"date":"2024-11-05T08:38:03","date_gmt":"2024-11-05T07:38:03","guid":{"rendered":"https:\/\/projets.litislab.fr\/llisa\/?page_id=58"},"modified":"2025-05-27T09:41:14","modified_gmt":"2025-05-27T07:41:14","slug":"publications","status":"publish","type":"page","link":"https:\/\/projets.litislab.fr\/llisa\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n<p class=\"has-text-color has-link-color has-medium-font-size wp-elements-abfca8e0a2b462a191b8b9ac65072171 wp-block-paragraph\" style=\"color:#f80404\">Publications du projet :<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Tsiry Mayet, Simon Bernard, Cl\u00e9ment Chatelain, Romain H\u00e9rault: Multiple Noises in Diffusion Model for Semi-Supervised Multi-Domain Translation (submitted) CoRR abs\/2309.14394 (2023) (<a href=\"https:\/\/arxiv.org\/abs\/2309.14394\">https:\/\/arxiv.org\/abs\/2309.14394<\/a>)<\/li>\n\n\n\n<li>L. Yang,&nbsp;C. Chatelain, and S. Adam, \u00ab&nbsp;Dynamic graph representation learning with neural networks: a survey,&nbsp;\u00bb&nbsp;<em>IEEE Access<\/em>, vol. 12, pp. 43460-43484, 2024 (<a href=\"https:\/\/ieeexplore.ieee.org\/document\/10473053\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/ieeexplore.ieee.org\/document\/10473053<\/a>)<\/li>\n\n\n\n<li>L. Yang,&nbsp;C. Chatelain, and S. Adam, \u00ab&nbsp;Inductive anomaly detection in dynamic graphs with accumulative causal walk alignment,&nbsp;\u00bb in&nbsp;<em>Machine Learning on Graph @ ECML<\/em>, 2024.<\/li>\n\n\n\n<li>D. Jain and \u202aR. 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)<\/li>\n\n\n\n<li>Tsiry Mayet, Simon Bernard, Cl\u00e9ment Chatelain, Romain H\u00e9rault: Domain Translation via Latent Space Mapping. IJCNN 2023: 1-10 (<a href=\"https:\/\/arxiv.org\/abs\/2212.03361\">https:\/\/arxiv.org\/abs\/2212.03361<\/a>)<\/li>\n\n\n\n<li>L. Yang, C. Chatelain, and S. Adam, \u00ab DspGNN: Bringing Spectral Design to Discrete Time Dynamic Graph Neural Networks for Edge Regression, \u00bb in Temporal Graph Learning Workshop@NeurIPS, 2023<\/li>\n\n\n\n<li>T. Mayet, P. Shamsolmoali, S. Bernard, E. Granger, R. Herault, and C. Chatelain, \u00ab TD-Paint: Faster Diffusion Inpainting Through Time-aware Pixel Conditioning, \u00bb in ICLR, 2025. (<a href=\"https:\/\/arxiv.org\/abs\/2410.09306\">https:\/\/arxiv.org\/abs\/2410.09306<\/a>)<\/li>\n\n\n\n<li>D. Jain, T. Mayet, R. Herault, and R. Modzelewski, \u00ab\u00a0One-Class SVM-guided Negative Sampling for Enhanced Contrastive Learning\u00a0\u00bb, Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL), PMLR 265, 2025<\/li>\n<\/ul>\n\n\n\n<p class=\"has-text-color has-link-color has-medium-font-size wp-elements-2cd43f6496d36a7ebad886f9f6ba3a0d wp-block-paragraph\" style=\"color:#f80404\">Articles pr\u00e9sent\u00e9s en groupe de lecture :<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Chen,T., &nbsp;Kornblith,S., &nbsp;Norouzi,&nbsp;M., Hinton<strong>, <\/strong>G. (2020) A Simple Framework for Contrastive Learning of Visual Representations. <em>(<\/em><a href=\"https:\/\/arxiv.org\/abs\/2002.05709\">https:\/\/arxiv.org\/abs\/2002.05709<\/a><em>)<\/em><\/li>\n\n\n\n<li>Haibo,J.,&nbsp;Shengcai,L.,&nbsp;Ling,S. (2020). Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild. (<a href=\"https:\/\/arxiv.org\/abs\/2003.03771\">https:\/\/arxiv.org\/abs\/2003.03771<\/a>&nbsp;)<\/li>\n\n\n\n<li>Xu, D., Ruan, C., Korpeoglu, E., Kumar, S., &amp; Achan, K. (2020). Inductive representation learning on temporal graphs.&nbsp;(<a href=\"https:\/\/arxiv.org\/abs\/2002.07962\">https:\/\/arxiv.org\/abs\/2002.07962<\/a>)<\/li>\n\n\n\n<li>Ho, J., Jain, A., &amp; Abbeel, P. (2020). Denoising diffusion probabilistic models.(<a href=\"https:\/\/arxiv.org\/abs\/2006.11239\">https:\/\/arxiv.org\/abs\/2006.11239<\/a>)<\/li>\n<\/ul>\n\n\n\n<p class=\"has-text-color has-link-color has-medium-font-size wp-elements-751f5ea47c1254097cc42d1caffbd61b wp-block-paragraph\" style=\"color:#f80404\">Articles recommand\u00e9s :<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Lu,K.,&nbsp;Grover,A.,&nbsp;Abbeel, P.,&nbsp;Mordatch,I. (2021) Pretrained Transformers as Universal Computation Engines. (<a href=\"https:\/\/arxiv.org\/abs\/2103.05247\">https:\/\/arxiv.org\/abs\/2103.05247<\/a>)<\/li>\n\n\n\n<li>Jiang,D., Lei,X.,&nbsp;Wubo Li,W.,&nbsp;Luo,N.,&nbsp;Hu,Y.,&nbsp;Zou,W.,&nbsp;Li,X. (2019) Improving Transformer-based Speech Recognition Using Unsupervised Pre-training.(<a href=\"https:\/\/arxiv.org\/abs\/1910.09932\">https:\/\/arxiv.org\/abs\/1910.09932<\/a>)<\/li>\n\n\n\n<li>Zoph,B., Ghiasi,G.,&nbsp;Lin,T.,&nbsp;Cui,Y.,&nbsp;Liu,H.,&nbsp;Cubuk,E.D., Le, Q.V. (2020) Rethinking Pre-training and Self-training\u00a0\u00bb : (<a href=\"https:\/\/arxiv.org\/abs\/2006.06882\">https:\/\/arxiv.org\/abs\/2006.06882<\/a>)<\/li>\n\n\n\n<li>Guo, S.,&nbsp;Huang,W.,&nbsp;Zhang,H.,&nbsp;Zhuang,C.,&nbsp;Dong,D., Scott,M.R. ,&nbsp;Huang,D. (2018) CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images (<a href=\"https:\/\/arxiv.org\/abs\/1808.01097\">https:\/\/arxiv.org\/abs\/1808.01097<\/a>)<\/li>\n\n\n\n<li>Jain, Dhruv &amp; Modzelewski, Romain &amp; H\u00e9rault, Romain &amp; Chatelain, Clement &amp; Thureau, Sebastien. (2023). Multi-Modal U-net for Segmenting Gross Tumor Volume in Lungs during Radiotherapy. 10.20944\/preprints202304.0129.v1.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Publications du projet : Articles pr\u00e9sent\u00e9s en groupe de lecture : Articles recommand\u00e9s :<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"onecolumn-page.php","meta":{"footnotes":""},"class_list":["post-58","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/projets.litislab.fr\/llisa\/wp-json\/wp\/v2\/pages\/58","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/projets.litislab.fr\/llisa\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/projets.litislab.fr\/llisa\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/projets.litislab.fr\/llisa\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/projets.litislab.fr\/llisa\/wp-json\/wp\/v2\/comments?post=58"}],"version-history":[{"count":4,"href":"https:\/\/projets.litislab.fr\/llisa\/wp-json\/wp\/v2\/pages\/58\/revisions"}],"predecessor-version":[{"id":111,"href":"https:\/\/projets.litislab.fr\/llisa\/wp-json\/wp\/v2\/pages\/58\/revisions\/111"}],"wp:attachment":[{"href":"https:\/\/projets.litislab.fr\/llisa\/wp-json\/wp\/v2\/media?parent=58"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}