3 accepted papers
In the process of training Neural Networks, pre-training is an unsupervised training process that uses automatically generated labels for real end-goal task inputs. It usually precedes a supervised training stage, can improve neural network performance, and can reduce training loss. In this work, we…
Point cloud data collected in real-world applications are often incomplete. This is because they are observed from partial viewpoints, which capture only a specific perspective or angle, or due to occlusion and low resolution. Existing completion approaches rely on datasets of specific predefined ob…
In this paper, we consider the problem of aligning data sets of short temporal signals without any a-priori known correspondence. We present a method combining Koopman operator theory and the Riemannian geometry of symmetric positive-definite (SPD) matrices. First, by taking a Koopman operator theor…