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Shared Latent Space by Both Languages in Non-Autoregressive Neural Machine Translation
Latent variable modeling in non-autoregressive neural machine translation (NAT) is a promising approach to mitigate the multimodality problem.
Advanced Scaling Methods for VNF deployment with Reinforcement Learning
Network function virtualization (NFV) and software-defined network (SDN) have become emerging network paradigms, allowing virtualized network function (VNF) deployment at a low cost.
End-to-End Training for Back-Translation with Categorical Reparameterization Trick
Back-translation is an effective semi-supervised learning framework in neural machine translation (NMT).
Sequential Deep Learning Architectures for Anomaly Detection in Virtual Network Function Chains
Therefore, we propose several sequential deep learning models to learn time-series patterns and sequential patterns of the virtual network functions (VNFs) in the chain with variable lengths.
Adversarial Training with Contrastive Learning in NLP
In this work, we propose adversarial training with contrastive learning (ATCL) to adversarially train a language processing task using the benefits of contrastive learning.
Reinforcement Learning of Graph Neural Networks for Service Function Chaining
In the management of computer network systems, the service function chaining (SFC) modules play an important role by generating efficient paths for network traffic through physical servers with virtualized network functions (VNF).
Graph Neural Network based Service Function Chaining for Automatic Network Control
Moreover, the GNN based model can be applied to a new network topology without re-designing and re-training.
