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--- trans_learn:reading_group_2023 [2023/09/18 17:48] – xianggyuchen
+++ trans_learn:reading_group_2023 [2023/10/23 10:47] (current) – wuyr
@@ Line 2: / Line 2: @@
 A biweekly reading group to discuss recent or classical papers on transfer learning methodologies and their applications on medical tasks. Other interesting theory topics are also welcomed.
-**Time:  Wednesday 8:30 p.m. (GMT+8)**
+**Time:  Monday 10:30 a.m. (GMT+8)**
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@@ Line 67: / Line 67: @@
 ===09/18/2023===
   * Presenter 1: Xiangyu Chen
-  * Paper: Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical Image Segmentation (Xiangyu Chen, ICML 2023)
+  * Paper: Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure (Sato, ICML 2023)
-  * Slides: {{ :trans_learn: rd.pdf |}}
+  * Slides: {{ :trans_learn: graph_recover.pptx |}}
   * Presenter 2: Yanru Wu
-  * Paper:
+  * Paper: MATE: Plugging in Model Awareness to Task Embedding for Meta Learning (Chen and Wang, NeurIPS 2020)
-  * Slides:
+  * Slides: {{ :trans_learn: MATE.pptx |}}
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+===10/11/2023===
+  * Presenter 1: Hanbing Liu
+  * Paper: Active Gradual Domain Adaptation: Dataset and Approach (Zhou, IEEE Transactions on Multimedia 2022)
+  * Slides: {{ :trans_learn: o |}}
+  * Presenter 2: Jiahao Lai
+  * Paper: Hypergraph Neural Networks (Feng, AAAI 2019)
+  * Slides: {{ :trans_learn: hypergnn.pdf |}}
+===10/23/2023===
+  * Presenter: Haohua Wang
+  * Paper: Fine-Tuning Language Models with Advantage-Induced Policy Alignment (Zhu, arXiv:2306.02231)
+  * Slides: {{ :trans_learn: fine-tuning_language_models_with_advantage-induced_policy_alignment_2_.pptx |}}
+-----------------------------