Neural Network Baseline

Neural network baselines serve as crucial points of comparison in evaluating the performance of novel machine learning models across diverse applications. Research currently focuses on developing and improving these baselines, encompassing various architectures like convolutional neural networks (CNNs), transformers, and spiking neural networks (SNNs), often tailored to specific data types (e.g., images, time series, graphs). The establishment of robust and efficient baselines is essential for rigorous model evaluation, facilitating advancements in fields ranging from medical diagnosis and industrial process monitoring to natural language processing and computer vision. Improved baselines also help to identify limitations in existing approaches and guide the development of more effective and interpretable models.