Extensive experiments on public benchmark datasets have verified the effectiveness of the two proposed works in retaining excellent recommendation performance.
To ensure DNNs effectively retain past knowledge while accommodating future tasks, we explore CL techniques from the viewpoint of augmenting and modularizing the memorization of DNNs.
In this work, we use MetaMap to map the original dataset, which lacks ground truth, with knowledge from drug-drug interaction databases, thereby deriving the ground truth dataset.
This presentation investigates the role of modularity in advancing neuroevolution algorithms and proposes novel techniques that exploit this property to evolve efficient neural networks.
This talk will discuss a flexible survival analysis method that simultaneously accommodates dependent censoring and eliminates the requirement for specifying the copula.
With the massive development in Information Communication Technology (ICT) in the past decades, people nowadays are used to fast-paced and real-time information exchange and public opinion deliberation.
