===== Transfer Learning & Semantic Embedding Reading Group =====

Reading group meets every Friday at 4:15pm.

[[meeting_2020spring:policy|How to Participate]] (Read this first!)

==== 2020 Spring schedule ====
^Date	^Presenter^Topic ^ Reading ^ Slides ^
|Feb 21,2020| Tan Yang |transfer learning| W. Zhang and D. Wu, “Discriminative Joint Probability Maximum Mean Discrepancy ( DJP-MMD ) for Domain Adaptation.”  | {{ :meeting_2020spring:paperreading_djpmmd_20200220.pdf |slide}}|
|Week 2|Li Mingyang|self-supervised learning | A Simple Framework for Contrastive Learning of Visual Representations | {{ :meeting_2020spring:a_simple_framework_for_contrastive_learning_of_visual_representations.pptx |slide}}|
|Week 3|Shen Ruhui|transfer learning| Massimiliano Patacchiola et al. Deep Kernel Transfer in Gaussian Process for Few-Shot Learning | {{ :meeting_2020spring:deep_kernel_transfer_in_gaussian_processes_for_few-shot_learning.pptx |slide}}|
|Week 4|Fan Jiashuo | network embedding|{{ career_network:Link Prediction Algorithms for Social Networks Based on Machine Learning and HARP.pdf| Link Prediction Algorithms for Social Networks based on Machine Learning and HARP}}|{{ :meeting_2020spring:a_simple_framework_for_contrastive_learning_of_visual_representations.pptx |slide}}|
|Week 5|Yang Jingyun| medical image|{{ :meeting_2020spring:retina_unet.pdf}} | {{:meeting_2020spring:retina_unet.pdf| slide}}   | 
|Week 6|Wang Jingge|transfer learning|[[http://openaccess.thecvf.com/content_CVPR_2019/papers/You_Universal_Domain_Adaptation_CVPR_2019_paper.pdf|Universal Domain Adaptation]] |  {{:meeting_2020spring:universal_domain_adaptation.pdf| slide}}   | 
|Week 7|Calvin|network embedding | {{:meeting_2020spring:learning_author_representations_by_combining_content_and_link_information.pdf|Author2Vec: Learning Author Representations by Combining Content and Link Information}} |{{:meeting_2020spring::author2vec.pptx| slide}}|
|Week 8|Yicong Li|Transfer Learning|{{geyer19a.pdf|Transfer Learning by Adaptive Merging of Multiple Models}}|{{transfer_learning_by_adaptive_merging_of_multiple_models.pptx|slide}}|
 

^Date	^Presenter^Topic ^ Reading ^ Slides ^
|Week9| Yang Tan |transfer learning| {{ :meeting_2020spring:geometric_dataset_distances_via_optimal_transport.pdf |}} |{{ :meeting_2020spring:paperreading_datasetdistance_20200415.pdf | slides}} |
|Week10| Mingyang Li|representation learning |{{ :meeting_2020spring:generative_cross_modal_retrieval.pdf}}|{{:meeting_2020spring:look_imagine_and_match-_improving_textual-visual_cross-modal_retrieval_with_generative_models.ppt }}|
|Week11| Ruhui Shen   |transfer learning |{{ :meeting_2020spring:active_learning_framework_of_informative_p53_cancer_rescue_mutants.pdf}}| |
|Week12| Jingyun Yang |   |   | |
|Week13| Jingge Wang  |Transfer Learning   | [[http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_Deep_Cocktail_Network_CVPR_2018_paper.pdf| Deep cocktail network]]  |{{ :meeting_2020spring:dctn_deep_cocktail_network.pdf |slide}} |
|Week14| Calvin       |   |   | |
|Week15| Yicong Li  |Noisy Labels|{{:meeting_2020spring:1804.06872.pdf}}|{{:meeting_2020spring:co-teaching.pdf|slide}}|
|Week16| Jiashuo Fan    |   |   | |



==== Paper Suggestions (feel free to add!)====

^Paper Name^     Notes     ^ Added by^
|R. Chen, T. Chen, X. Hui, H. Wu, G. Li, and L. Lin, “Knowledge Graph Transfer Network for Few-Shot Recognition,” 2019.| few-shot learning, transfer learning, knowledge graph        | yang|
|B. Tan, Y. Song, E. Zhong, and Q. Yang, “Transitive Transfer Learning Categories and Subject Descriptors,” Kdd, pp. 1155–1164, 2015| transitivity in transfer learning (1/2) |yang|
| B. Tan, Y. Zhang, S. J. Pan, and Q. Yang, “Distant domain transfer learning,” 31st AAAI Conf. Artif. Intell. AAAI 2017, pp. 2604–2610, 2017| transitivity in transfer learning (2/2) |yang|
|I. Redko, E. Morvant, A. Habrard, M. Sebban, and Y. Bennani, Advances in Domain Adaptation Theory. Elsevier Science, 2019. (Textbook) | domain adaptation, theory (can use multiple meetings) |yang|
| B. B. Damodaran, B. Kellenberger, R. D. Tuia, and N. Courty, “DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11208 LNCS, pp. 467–483, 2018.| transfer learning, domain adaptation |yang|
| Y. Song et al., “Improving Unsupervised Domain Adaptation with Variational Information Bottleneck,” 2019.| domain adaptation, information theory, |yang|