Search Results for author:
Found 5 papers, 1 papers with code
XAI for All: Can Large Language Models Simplify Explainable AI?
The field of Explainable Artificial Intelligence (XAI) often focuses on users with a strong technical background, making it challenging for non-experts to understand XAI methods.
Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection
This paper introduces MarketSenseAI, an innovative framework leveraging GPT-4's advanced reasoning for selecting stocks in financial markets.
Enhancing Explainability in Mobility Data Science through a combination of methods
In the domain of Mobility Data Science, the intricate task of interpreting models trained on trajectory data, and elucidating the spatio-temporal movement of entities, has persistently posed significant challenges.
XAI for time-series classification leveraging image highlight methods
Although much work has been done on explainability in the computer vision and natural language processing (NLP) fields, there is still much work to be done to explain methods applied to time series as time series by nature can not be understood at first sight.
Transforming Sentiment Analysis in the Financial Domain with ChatGPT
Financial sentiment analysis plays a crucial role in decoding market trends and guiding strategic trading decisions.
