The field of deep neural networks is moving towards more adaptive and robust models. Researchers are exploring new activation functions that can dynamically adjust to input statistics, such as VeLU, which integrates ArcTan-Sin transformations and Wasserstein-2 regularization. Additionally, there is a growing interest in sensitivity analysis methods to study neural network models and interpret their underlying mechanisms. Another area of focus is the development of more efficient and effective neural architecture search methods, such as MedNNS, which jointly optimizes architecture selection and weight initialization. Furthermore, researchers are working on improving the training process of deep neural networks, including the use of smooth activation functions to overcome skip dominance and discretization discrepancy challenges. Noteworthy papers include: MedNNS, which introduces a supernet-based neural network search framework for medical imaging applications, and SA-DARTS, which leverages a smooth activation function on architecture weights to mitigate skip dominance and improve the performance of differentiable architecture search. OUI is also a significant contribution, providing a novel tool for monitoring training dynamics and identifying optimal regularization hyperparameters.