… the learner vehicles, where the knowledge on network can be transferred from the expert …
access networks available to vehicles, we propose a joint access network selection and power …

… ing an effective and fast algorithm for network selection with contextual evolution. … network
selection model that takes the diverse and asymmetric downlink-uplink features of network …

… or impede knowledge transfer. Stimulating future research on knowledge transfer across
social … In addition, the article aimed to identity the conditions under which knowledge transfer …

… networks, as it supports a high data rate and uses an unlicensed spectrum. In hybrid WiFi-VLC
networks, … an intelligent network selection that is capable of reducing network complexity …

… in the transfer learning on modular neural networks are optimized by a tournament selection
… A modular neural network contains a layer of multiple modules (small layers that may be …

… problem without retraining, we use graph neural networks (Graph-NNs) to compute the
embeddings of OOKB entities, exploiting the limited auxiliary knowledge provided at test time.The …

… Thus, the objective of this study is to uncover the major knowledge clusters in supplier
selection through the papers' co-citation network on the basis of more rigorous and objective …

… can facilitate knowledge transfer in university–… knowledge transfer provides a valuable
perspective. We started our review with identifying barriers and facilitators of knowledge transfer. …

… After knowledge transfer, the resulting neural network is denoted MS→T and is thus used
for service-level metric prediction in DT . In this paper we specifically study the challenging …

… for applying answer selection models to a new … Knowledge-aware Attentive Network (KAN),
a transfer learning framework for crossdomain answer selection, which uses the knowledge …