Paper on self-supervised learning for telecom networks accepted
This paper shows that self-supervised pretraining on unlabeled data enables accurate prediction even when labeled data is scarce, which is the normal situation in live telecom networks. It evaluates how one pretrained model can be efficiently adapted to multiple downstream tasks that share similar feature structure, as new applications are introduced. This modular reuse across … Continue reading “Paper on self-supervised learning for telecom networks accepted”