In this approach, it is common to use bilevel optimization where one optimizes the model weights over the training data (lower-level problem) and various hyperparameters such as the configuration of the architecture over the validation data (upper-level problem).
In parallel, recent developments in self-supervised and semi-supervised learning (S4L) provide powerful techniques, based on data-augmentation, contrastive learning, and self-training, that enable superior utilization of unlabeled data which led to a significant reduction in required labeling in the standard machine learning benchmarks.
Short text is becoming more and more popular on the web, such as Chat Message, SMS and Product Reviews.
By using them, we found that these word networks have low accuracy and coverage, and cannot completely portray the semantic network of PWN.
We show that over the information space learning is fast and one can quickly train a model with zero training loss that can also generalize well.
In particular, we prove that: (i) In the first few iterations where the updates are still in the vicinity of the initialization gradient descent only fits to the correct labels essentially ignoring the noisy labels.
Community detection is of great importance for online social network analysis.
Social and Information Networks Cryptography and Security