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The "Fat Face" illusion: A robust adaptation for processing pairs of faces
In: ISSN: 0042-6989 ; EISSN: 0042-6989 ; Vision Research ; https://hal.archives-ouvertes.fr/hal-03579276 ; Vision Research, Elsevier, 2022, 195, pp.108015. ⟨10.1016/j.visres.2022.108015⟩ (2022)
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2
Speaking clearly improves speech segmentation by statistical learning under optimal listening conditions ...
Guo, Zhe-chen. - : Open Science Framework, 2022
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3
SLOGAN: Handwriting Style Synthesis for Arbitrary-Length and Out-of-Vocabulary Text ...
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4
Fostering student engagement with feedback: an integrated approach
Zhang, Zhe (Victor); Hyland, Ken. - 2022
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O desenho de uma aplicação de MAVL em PLE destinado a aprendentes chineses
In: Entrepalavras; v. 11, n. 11esp (11): Dicionário, léxico e ensino de línguas; 313-339 (2022)
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Making Better Use of Bilingual Information for Cross-Lingual AMR Parsing ...
Cai, Yitao; Lin, Zhe; Wan, Xiaojun. - : arXiv, 2021
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7
Speaking clearly improves speech segmentation by statistical learning under optimal listening conditions
In: Laboratory Phonology: Journal of the Association for Laboratory Phonology; Vol 12, No 1 (2021); 14 ; 1868-6354 (2021)
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8
Accountable Error Characterization ...
Misra, Amita; Liu, Zhe; Mahmud, Jalal. - : arXiv, 2021
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9
Pushing Paraphrase Away from Original Sentence: A Multi-Round Paraphrase Generation Approach ...
Lin, Zhe; Wan, Xiaojun. - : arXiv, 2021
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10
EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets ...
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11
Modeling Endorsement for Multi-Document Abstractive Summarization ...
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12
Context-Aware Interaction Network for Question Matching ...
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13
Borealestes serendipitus WALDMAN & SAVAGE 1972 ...
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14
Borealestes serendipitus WALDMAN & SAVAGE 1972 ...
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15
Weakly Supervised Named Entity Tagging with Learnable Logical Rules ...
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16
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
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17
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 ...
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://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/14053380/1
https://dx.doi.org/10.6084/m9.figshare.14053380.v1
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Weakly Supervised Named Entity Tagging with Learnable Logical Rules ...
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19
Injecting Semantic Concepts into End-to-End Image Captioning ...
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20
Overcoming Barriers to Cross-cultural Cooperation in AI Ethics and Governance
ÓhÉigeartaigh, Seán S.; Whittlestone, Jess; Liu, Yang. - : Springer Netherlands, 2021. : Philosophy & Technology, 2021
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