3 accepted papers
Low-rank tensor decompositions (TDs) provide an effective framework for multiway data analysis. Traditional TD methods rely on predefined structural assumptions, such as CP or Tucker decompositions. From a probabilistic perspective, these methods effectively model the relationships between latent fa
The inherent scale ambiguity issue greatly limits the performance of monocular visual odometry. In recent years, a variety of methods have been proposed for self-supervised learning of ego-motion and depth estimation, incorporating specifically designed scale-consistency constraints that utilize est…
Offline reinforcement learning aims to maximize the expected cumulative rewards with a fixed collection of data. The basic principle of current offline reinforcement learning methods is to restrict the policy to the offline dataset action space. However, they ignore the case where the dataset's traj…