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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 Document Domain Randomization for Deep Learning Document Layout Extraction Ling, Meng; Chen, Jian; Möller, Torsten... In: Proceedings of the 16th International Conference on Document Analysis and Recognition (ICDAR, September 5--10, Lausanne, Switzerland) ; https://hal.inria.fr/hal-03336444 ; Proceedings of the 16th International Conference on Document Analysis and Recognition (ICDAR, September 5--10, Lausanne, Switzerland), Sep 2021, Lausanne, Switzerland. pp.497-513, ⟨10.1007/978-3-030-86549-8_32⟩ (2021) BASE Show details 2 Document Domain Randomization for Deep Learning Document Layout Extraction ... Ling, Meng; Chen, Jian; Möller, Torsten. - : arXiv, 2021 BASE Show details 3 A Neural Network-Based Linguistic Similarity Measure for Entrainment in Conversations ... Yu, Mingzhi; Litman, Diane; Ma, Shuang. - : arXiv, 2021 BASE Show details 4 Extractive Research Slide Generation Using Windowed Labeling Ranking ... Sefid, Athar; Wu, Jian; Mitra, Prasenjit. - : arXiv, 2021 BASE Show details 5 Additional file 3 of Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models ... Dejun Jiang; Zhenxing Wu; Hsieh, Chang-Yu; Guangyong Chen; Liao, Ben; Wang, Zhe; Shen, Chao; Dongsheng Cao; Wu, Jian; Hou, Tingjun. - : figshare, 2021 Abstract: Additional file 3: Table S1. The performance of the top three runs and the worst three runs among the 50 times independent runs given by the XGBoost model for the 11 datasets. Table S2. The performance comparison (MAE metric) of the four descriptor-based and four graph-based models on the three regression datasets. Table S3. The performance comparison (R2 metric) of the four descriptor-based and four graph-based models on the three regression datasets. Table S4: The performance comparison (average RMSE) of the 50 times independent runs on three regression datasets including ESOL, FreeSolv, and Lipop before/after removing the top three related descriptors given by the four descriptor-based models (SVM, XGBoost, RF and DNN). All the models named with suffix ‘1’ refer to the models developed based on the remaining descriptors. Table S5. The detailed information for the 11 washed datasets. Table S6. The performance comparison of the 50 times independent runs on four datasets including BBBP, Tox21, ToxCast, and ... Keyword: Artificial Intelligence and Image Processing; FOS Computer and information sciences URL: https://dx.doi.org/10.6084/m9.figshare.14053380 https://springernature.figshare.com/articles/journal_contribution/Additional_file_3_of_Could_graph_neural_networks_learn_better_molecular_representation_for_drug_discovery_A_comparison_study_of_descriptor-based_and_graph-based_models/14053380 BASE Hide details 6 Additional file 3 of Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models ... Dejun Jiang; Zhenxing Wu; Hsieh, Chang-Yu. - : figshare, 2021 BASE Show details

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