Fair Comparison

Fair comparison in machine learning research focuses on establishing objective and reliable benchmarks for evaluating different models and algorithms, addressing biases introduced by varying experimental setups. Current research emphasizes controlling factors like model size, training data, and initialization methods to enable more accurate performance comparisons across diverse architectures, including Multilayer Perceptrons (MLPs), Kolmogorov-Arnold Networks (KANs), and Transformers, and across various applications such as natural language processing, computer vision, and cybersecurity. This rigorous approach is crucial for advancing the field by ensuring that reported improvements reflect genuine advancements rather than artifacts of experimental design, ultimately leading to more robust and reliable machine learning systems.