5 accepted papers
Deep learning models can suffer from severe performance degradation when relying on spurious correlations between input features and labels, making the models perform well on training data but have poor prediction accuracy for minority groups. This problem arises especially when training data are li…
The high rigidity of the low-melting-point alloy (LMPA) renders it suitable for soft robots with heavy loads and extensive workspace requirements. However, its two-stage stiffness adjustment and prolonged response time have limited further application. This research proposes a novel approach to achi
Graph classification is an important learning task for graph-structured data. Graph neural networks (GNNs) have recently gained growing attention in graph learning and shown significant improvements on many important graph problems. Despite their state-of-the-art performances, existing GNNs only use…
3D Multi-Object Tracking (MOT) plays a crucial role in efficient and safe operation of automatic driving, especially in scenarios of occlusion or poor visibility. Most 3D MOT methods leverage only positional distance, which is insufficient for scenes with high density of objects or drastic changes i…
Small data has been a barrier for many machine learning tasks, especially when applied in scientific domains. Fortunately, we can utilize domain knowledge to make up the lack of data. Hence, in this paper, we propose a hybrid model KRL that treats domain knowledge model as a weak learner and uses an…