3 accepted papers
Modern neural networks are expected to simultaneously satisfy a host of desirable properties: accurate fitting to training data, generalization to unseen inputs, parameter and computational efficiency, and robustness to adversarial perturbations. While compressibility and robustness have each been s…
Cross-validation is commonly used to estimate machine learning model performance on new samples. However, using it for both hyperparameter selection and error estimation can lead to overestimating model performance, especially with extensive hyperparameter searches that overly tailor models to valid…
The convolutional neural network (CNN) remains an essential tool in solving computer vision problems. Standard convolutional architectures consist of stacked layers of operations that progressively downscale the image. Aliasing is a well-known side-effect of downsampling that may take place: it caus…