Metric learningReviewMulti-task learning, referring to the joint training of multiple problems, can usually lead to better performance by exploiting the shared information across all the problems. On the other hand, metric learning, an important research topic, is however often studied in the ...
A Brief Review on Multi-Task Learning. Multimedia Tools and Applications 2018 paper bib Kimhan Thung, Chong Yaw Wee A Survey on Multi-Task Learning. arXiv 2017 paper bib Yu Zhang, Qiang Yang A Survey on Multi-view Learning. Computer ence 2013 paper bib Chang Xu, Dacheng Tao, Chao Xu An...
A Brief Review on Multi-Task Learning. Multimedia Tools and Applications 2018 paper bib Kimhan Thung, Chong Yaw Wee A Survey on Multi-Task Learning. arXiv 2017 paper bib Yu Zhang, Qiang Yang A Survey on Multi-view Learning. Computer ence 2013 paper bib Chang Xu, Dacheng Tao, Chao Xu An...
The --cache argument tells AutoML to load all data into memory if possible (on) or not (off) or to automatically determine what to do. Figure 4 AutoML Command Summary Expand table Argument Alias Values Default Value --task -T multiclass classification, binary classification, regression --...
A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance. Remote Sens. 2019, 11, 2144. [Google Scholar] [CrossRef] [Green Version] Hussain, T.; Hussain, D.; Hussain, I.; AlSalman, H.; Hussain, S.; Ullah, S.S.; Al-Hadhrami, S. Internet...
Additionally, while distillation provides a potential solution to multi-task learning, it requires a reservoir of persistent data for each learned task. Jung, Ju, Jung, and Kim (2018) proposed to regularize the l2 distance between the final hidden activations, preserving the previously learned ...
Although diabetes mellitus is a complex and pervasive disease, most studies to date have focused on individual features, rather than considering the complexities of multivariate, multi-instance, and time-series data. In this study, we developed a novel diabetes prediction model that incorporates these...
Table 1 Results of GD metric of different multi-objective algorithms on ZDT, DTLZ and constraint benchmark problems. Full size table Analysis using the IGD metric Table 2 presents the final solution distributions when comparing MOEDO algorithm against MOMPA, NSGA-II, MOAOA, MOEA/D and MOGNDO ...
NAS methods use machine learning algorithms to search through a large space of possible architectures and find the one that performs best on a given task. We provide a summary of the performance achieved by representative NAS algorithms on the CIFAR-10, CIFAR-100, ImageNet and well-known bench...
[DEEP LEARNING] BanditLib - A simple Multi-armed Bandit library. [Deprecated] Caffe - A deep learning framework developed with cleanliness, readability, and speed in mind. [DEEP LEARNING] CatBoost - General purpose gradient boosting on decision trees library with categorical features support out of...