Abstract Decision Trees (DTs) are predictive models in supervised learning, known not only
for their unquestionable utility in a wide range of applications but also for their interpretability …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Rotated object detection aims to identify and locate objects in images with arbitrary
orientation. In this scenario, the oriented directions of objects vary considerably across …

We propose a novel high-performance and interpretable canonical deep tabular data
learning architecture, TabNet. TabNet uses sequential attention to choose which features to …

Prototype-based methods use interpretable representations to address the black-box nature
of deep learning models, in contrast to post-hoc explanation methods that only approximate …

The application of closed-loop approaches in systems neuroscience and therapeutic
stimulation holds great promise for revolutionizing our understanding of the brain and for …

Vision transformers (ViTs), which have demonstrated a state-of-the-art performance in image
classification, can also visualize global interpretations through attention-based contributions …

Fine-grained visual categorization (FGVC) is an important but challenging task due to high
intra-class variances and low inter-class variances caused by deformation, occlusion …

An adaptive neural decision tree is investigated to recognize electroencephalogram (EEG)
emotion signal with ability of intelligently selecting network structure. Firstly, to overcome …

An important step in Reinforcement Learning (RL) research is to create mechanisms that
give higher level insights into the black-box policy models used nowadays and provide …