ReLU Neural Network

ReLU neural networks, characterized by their rectified linear unit activation function, are a cornerstone of modern machine learning, with research focusing on understanding their theoretical properties and improving their practical performance. Current efforts investigate aspects like the computational complexity of determining network properties (injectivity, surjectivity), the development of efficient algorithms for various tasks (e.g., stochastic bandits), and the exploration of novel architectures and training methods to enhance expressivity, robustness, and generalization. This research is crucial for advancing both the theoretical foundations of deep learning and the reliable deployment of neural networks in safety-critical applications.