Titlebook: Databases Theory and Applications; 31st Australasian Da Renata Borovica-Gajic,Jianzhong Qi,Weiqing Wang Conference proceedings 2020 Springe

BET

Query-Oriented Temporal Active Intimate Community Searchdensely-connected as well as actively participate and have active temporal interactions among them with respect to the given query consisting of a set of query nodes (users) and a set of attributes. Experiments on real datasets demonstrate the effectiveness of our proposed approach.

量被毀壞

0302-9743 and data analytics between researchers and practitioners from around the globe, particularly Australia, New Zealand and in the World..978-3-030-39468-4978-3-030-39469-1Series ISSN 0302-9743 Series E-ISSN 1611-3349

CONE

Elena Lokhman,Srijana Rai,William Matthewsver deep neural networks. In particular, our proposed function interpolation models exhibit memory footprint two orders of magnitude smaller compared to neural network models, and 30–40% accuracy improvement over neural networks trained with the same amount of time, while keeping query time generally on-par with neural network models.

酷熱

Coronaviruses and their Diseasesthree representative methods from different categories to reveal how matching model affects the performance. Besides, the experiments are conducted on multiple real datasets with different settings to demonstrate the influence of other factors in map-matching problem, like the trajectory quality, data compression and matching latency.

其他

Function Interpolation for Learned Index Structuresver deep neural networks. In particular, our proposed function interpolation models exhibit memory footprint two orders of magnitude smaller compared to neural network models, and 30–40% accuracy improvement over neural networks trained with the same amount of time, while keeping query time generally on-par with neural network models.

尖牙

Onerous

regale

多產(chǎn)子

Mariette F. Ducatez,Jean-Luc GuérinC can efficiently exploit the parallel computation advantages of GPU hardware for training, and further facilitate the gradient propagation. Extensive experiments on MS-COCO demonstrate that the proposed PAIC significantly reduces the training time, while achieving competitive performance compared to conventional RNN-based models.