The “black box” nature of many AI models causes hesitation in trusting their predictions, especially in sensitive fields like medicine and finance, where interpretability and accountability are essential. Overcoming this challenge has become a central focus in AI development. Time series classification (TSC) is a critical task with numerous real-world […]
Machine Learning
4 posts
We have been fortunate to have 3 papers accepted at AAAI’25. Hung and colleagues will present their work on explainability of time series classification. InteDisUX aims to create explanations that are accessible and meaningful to users (real people) by identifying subsequences of the time series that provide positive or negative […]
Scientific corpora, such as papers and patents, are great source of information. Incorporating this information into scientific discovery pipelines is a great challenge that could reduce the discovery costs and speed-up the process. Motivating by this fact and leveraging the recent advances of the Natural Language Processing (NLP) domain, we […]