4 accepted papers
Quantifying feature importance with valid statistical uncertainty is central to interpretable machine learning, yet classical model-agnostic methods often fail under feature correlation, producing unreliable attributions and compromising inference. Statistical approaches that address correlation thr…
Deep Neuro-Fuzzy Inference Systems (DNFIS) seamlessly fuse neural networks with the fuzzy inference system enabling intricate decision-making and knowledge representation, while upholding a commendable degree of adaptability and interpretability. However, the challenge of privacy-preserving inferenc…
Multiple instance learning (MIL) has been increasingly applied to the classification of histopathology whole slide images (WSI). While many classic attention-based MIL algorithms have achieved good classification performance, they do not account for the relationships between instances. Using transfo…
The appearance of container technology has profoundly changed the development and deployment of multi-tier distributed applications. However, the imperfect system resource isolation features and the kernel-sharing mechanism will introduce significant security risks to the container-based cloud. In t…