Then we introduce a semantic mapper that produces the proxy masks from various input conditions including semantic masks.

AML Trends in 2022: What Y ou Need to Know .

However, calculating the Laplacian spectrum can be expensive in a matrix base.

The "black-box" nature of representations, combined with the known susceptibility of networks to learn spurious correlations [

Reinforcement Learning agent and a configurator that can modify some environmental parameters to improve the agent's performance.

Machine learning (ML) models have been widely used to make predictions.

Functional constrained optimization (FCO) has emerged as a powerful tool for solving various machine learning problems. However, with the rapid increase in applications of neural networks in recent years, it has become apparent that both the objective and constraints often involve nonconvex functions, which poses significant challenges in obtaining high-quality solutions. In this work, we focus on a class of nonconvex FCO problems with nonconvex constraints, where the two optimization variables are nonlinearly coupled in the inequality constraint. Leveraging the primal-dual optimization framework, we propose a smoothed first-order Lagrangian method (SLM) for solving this class of problems. We establish the theoretical convergence guarantees of SLM to the Karush-Kuhn-Tucker (KKT) solutions through quantifying dual error bounds. By establishing connections between this structured FCO and equilibrium-constrained nonconvex problems (also known as bilevel optimization), we apply the proposed SLM to tackle bilevel optimization oriented problems where the lower-level problem is nonconvex. Numerical results obtained from both toy examples and hyper-data cleaning problems demonstrate the superiority of SLM compared to benchmark methods.