Monotone Neural Network
Monotone neural networks are neural network architectures designed to learn functions with specific monotonicity properties, ensuring that the output increases (or decreases) monotonically with respect to the input. Current research focuses on developing novel architectures like monotone gradient networks (mGradNets) and algorithms that enforce monotonicity, often employing techniques like specialized loss functions or iterative methods such as the Forward-Backward-Forward algorithm. This area is significant because monotone functions are crucial in various applications, including solving inverse problems, optimization, and generative modeling, offering improved robustness, convergence guarantees, and memory efficiency compared to traditional methods. The resulting models find use in diverse fields like image processing, MRI acceleration, and stochastic optimization.