Explainable Knowledge Tracing Models for Big Data: Is Ensembling an Answer?

Constrained self-supervised method with temporal ensembling for fiber bundle detection on anatomic tracing data

Anatomic tracing data provides detailed information on brain circuitry essential for addressing some of the common errors in diffusion MRI tractography. However, automated detection of fiber bundles on tracing data is challenging due to sectioning distortions, presence of noise and artifacts and intensity/contrast variations. In this work, we propose a deep learning method with a self-supervised loss function that takes anatomy-based constraints into account for accurate segmentation of fiber bundles on the tracer sections from macaque brains. Also, given the limited availability of manual lab…

Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory

Deep learning based knowledge tracing model has been shown to outperform traditional knowledge tracing model without the need for human-engineered features, yet its parameters and representations have long been criticized for not being explainable. In this paper, we propose Deep-IRT which is a synthesis of the item response theory (IRT) model and a knowledge tracing model that is based on the deep neural network architecture called dynamic key-value memory network (DKVMN) to make deep learning based knowledge tracing explainable. Specifically, we use the DKVMN model to process the student's le…

Explainable Link Prediction for Privacy-Preserving Contact Tracing

Contact Tracing has been used to identify people who were in close proximity to those infected with SARS-Cov2 coronavirus. A number of digital contract tracing applications have been introduced to facilitate or complement physical contact tracing. However, there are a number of privacy issues in the implementation of contract tracing applications, which make people reluctant to install or update their infection status on these applications. In this concept paper, we present ideas from Graph Neural Networks and explainability, that could improve trust in these applications, and encourage adopti…

NormEnsembleXAI：揭示可解释人工智能集成技术的优势与不足

NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble Techniques

该研究对XAI集成方法做全面对比，提出结合极值、均值与归一化的NormEnsembleXAI新方法，剖析这类方法的优劣，并推出配套库推动可解释深度学习模型落地。

CIKT：一种基于大语言模型的协作迭代知识追踪框架

CIKT: A Collaborative and Iterative Knowledge Tracing Framework with Large Language Models

提出CIKT框架，利用大语言模型通过分析师与预测器的协同迭代优化，提升知识追踪的预测准确性、可解释性和可扩展性。

xCG：用于非小细胞肺癌生存预测的可解释细胞图

xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer

提出一种可解释细胞图（xCG）方法，利用图神经网络对肿瘤微环境进行空间建模，结合成像质谱流式数据预测非小细胞肺癌患者生存期，并通过逐层相关性传播方法在细胞水平提供风险归因解释。