With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …

Visual representation learning is ubiquitous in various real-world applications, including
visual comprehension, video understanding, multi-modal analysis, human-computer …

The last decade of machine learning has seen drastic increases in scale and capabilities.
Deep neural networks (DNNs) are increasingly being deployed in the real world. However …

Objective. Low-dose computed tomography (LDCT) denoising is an important problem in CT
research. Compared to the normal dose CT, LDCT images are subjected to severe noise …

We propose CX-ToM, short for counterfactual explanations with theory-of-mind, a new
explainable AI (XAI) framework for explaining decisions made by a deep convolutional …

Explainable AI aims to overcome the black-box nature of complex ML models like neural
networks by generating explanations for their predictions. Explanations often take the form of …

Through extensive experience developing and explaining machine learning (ML)
applications for real-world domains, we have learned that ML models are only as …

神经网络模型性能日益强大, 被广泛应用于解决各类计算机相关任务, 并表现出非常优秀的能力,
但人类对神经网络模型的运行机制却并不完全理解. 针对神经网络可解释性的研究进行了梳理和 …

Zero-shot learning (ZSL) aims to recognize objects from unseen classes only based on
labeled images from seen classes. Most existing ZSL methods focus on optimizing feature …

Neurons are the fundamental units of neural networks. In this paper, we propose a method
for explaining neural networks by visualizing the learning process of neurons. For a trained …