When taking photos under an environment with insufficient light, the exposure time and the sensor gain usually require to be carefully chosen to obtain images with satisfying visual quality.

On the one hand, as we attain better spatial robustness via equivariant models and larger training augmentation, the adversarial robustness worsens.

Nevertheless, a theoretical analysis of criteria for learning good hash codes remains largely unexploited.

Parallel hardware devices (e.g., graphics processor units) have limited high-bandwidth memory capacity.

Our main contributions are threefold: 1. We show that for any regularizer, there is an SCO problem for which Regularized Empirical Risk Minimzation fails to learn.

Sentence summarization aims at compressing a long sentence into a short one that conveys the main idea of the input.