Inter Sample

Inter-sample analysis focuses on leveraging relationships between data points, moving beyond the traditional assumption of independent and identically distributed (i.i.d.) samples. Current research explores this by incorporating sample affinities and relationships into machine learning models, using techniques like graph neural networks and attention mechanisms to improve performance, particularly in high-dimensional or imbalanced datasets. This approach is proving valuable in various applications, including improving the accuracy of deep learning models, addressing bias in speech recognition systems, and enhancing clustering algorithms. The ultimate goal is to build more robust and accurate models by exploiting the inherent structure and dependencies within datasets.