The key idea behind low-rank factorization is to replace high-dimensional tensors with lower-dimensional tensors. One type of low-rank factorization is compact convolutional filters, where the over-parameterized (having too many parameters) convolution filters are replaced with compactblocks to both reduce the number of parameters and increase speed.
A perturbation test is a method used to evaluate a model’s robustness and stability. In machine learning, this test helps determine how sensitive the model’s predictions are to small changes (perturbations) in the input data. If a model is stable, small changes in the input should lead to minimal changes in the output. This method…
Calibration curves are specifically used for classification models. The primary goal of a calibration curve is to evaluate the reliability of the predicted probabilities in a classification task. A calibration curve checks how well predicted probabilities align with the actual observed frequencies (e.g., when a model predicts 70% probability of being positive, we expect about…
