Research topics:

• Machine Learning and Statistical Inference
• Learning with Unlabeled Data
• Meta and Ensemble learning
• Ontology for Knowledge Representation

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281 to 290 of 888 Results
train_7.pt
May 27, 2022 - HHAR Processed Data
Unknown - 5.1 MB - MD5: e1306b7c777212cebe3c6e11e851baf5
train_8.pt
May 27, 2022 - HHAR Processed Data
Unknown - 5.5 MB - MD5: a86e3874d6e4e892668cc464c5f3ba8a
ADATIME(Nanyang Technological University)
May 27, 2022
Processed version of C-MAPSS dataset
May 12, 2022
Ragab, Mohamed, 2022, "Processed version of C-MAPSS dataset", https://doi.org/10.21979/N9/FMUP9M, DR-NTU (Data), V1
C-MAPSS is a simulated turbofan engine dataset that consists of four data subsets with different operating conditions and fault modes denoted as FD001, FD002, FD003, and FD004.
cmapps_train_test_cross_domain.pt
May 12, 2022 - Processed version of C-MAPSS dataset
Unknown - 660.0 MB - MD5: 22db2917b7a3a4a1253cfdbb90d4e3f2
Related data for GLIMG: Global and local item graphs for top-N recommender systems
Apr 23, 2022 - Lin Zhuoyi
Lin, Zhuoyi, 2022, "Related data for GLIMG: Global and local item graphs for top-N recommender systems", https://doi.org/10.21979/N9/NRRVDW, DR-NTU (Data), V2
GLIMG: Global and local item graphs for top-N recommender systems
labelml1m.txt
Apr 23, 2022 - Related data for GLIMG: Global and local item graphs for top-N recommender systems
Plain Text - 11.8 KB - MD5: 07701e260ae2c69d9d3c9f226c35cabc
labelyelp.txt
Apr 23, 2022 - Related data for GLIMG: Global and local item graphs for top-N recommender systems
Plain Text - 11.1 KB - MD5: 2bd81066874f84dcf69bf7e646cfad46
main.py
Apr 23, 2022 - Related data for GLIMG: Global and local item graphs for top-N recommender systems
Python Source Code - 8.0 KB - MD5: fef17ebb815014628196c42300145816
Code
Related Data for COMET: Convolutional Dimension Interaction for Collaborative Filtering
Apr 23, 2022 - Lin Zhuoyi
Lin, Zhuoyi, 2022, "Related Data for COMET: Convolutional Dimension Interaction for Collaborative Filtering", https://doi.org/10.21979/N9/TO2HBX, DR-NTU (Data), V2
Latent factor models play a dominant role among recommendation techniques. However, most of the existing latent factor models assume both historical interactions and embedding dimensions are independent of each other, and thus regrettably ignore the high-order interaction informa...
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