Lagrangian approach, which involves adding a regularization term to the loss function.

We first define the restricted Cheeger constant in the link prediction task. Then, according to Proposition 2.1, we have: Then, we can draw the same conclusion with Eq.12, and the Thus, Eq.16 can be simplified to: "sites" Based on the Eq.15 and Eq.17, we can rewrite L The inequality holds due to the assumption. Knowledge discovery: In the 5 random experiments, we add 500 pseudo links in each iteration. The metadata information of the nodes are all strongly relevant to "Linux" Both papers focus on the "malware"/"phishing" under the topic "Computer security". The detailed result of the case study is shown in Table 6.

Pseudo labeling (PL) is a wide-applied strategy to enlarge the labeled dataset by self-annotating the potential samples during the training process.

Diffusion models have gained traction as powerful algorithms for synthesizing high-quality images. Central to these algorithms is the diffusion process, a set of equations which maps data to noise in a way that can significantly affect performance. In this paper, we explore whether the diffusion process can be learned from data.

We consider the problem of variational Bayesian inference in a latent variable model where a (possibly complex) observed stochastic process is governed by the solution of a latent stochastic differential equation (SDE).

This bound guides the design of an optimal exploration policy attaining minimal sample complexity. However, this lower bound involves solving a hard non-convex optimization problem.