However, considering the ever-evolving paradigms in deep learning, employees with ulterior motivesmay fabricate reasons such asthe requirements ofdata augmentation [6]orthe purpose of multimodal learning [3] to apply for relevant and irrelevant private datasets, which is common in social engineering [4].

Large machine learning (ML) datasets become critical assets for AI corporations [2, 8].

Despite achieving remarkable performance, deep graph learning models, such as node classification and network embedding, suffer from harassment caused by small adversarial perturbations.

On the other hand, recent finding on neural tangent kernel enables us to approximate a wide neural network with a linear model of the network's random features. In this paper, we theoretically analyze the knowledge distillation of a wide neural network. First we provide a transfer risk bound for the linearized model of the network. Then we propose a metric of the task's training difficulty, called data inefficiency.

The substitution effect happens when learning the stronger features insome neurons can substitute for learning these features in other neurons through updating the predictionhead.