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A Comprehensive Review on Financial Explainable AI
The success of artificial intelligence (AI), and deep learning models in particular, has led to their widespread adoption across various industries due to their ability to process huge amounts of data and learn complex patterns.
FinXABSA: Explainable Finance through Aspect-Based Sentiment Analysis
This paper presents a novel approach for explainability in financial analysis by deriving financially-explainable statistical relationships through aspect-based sentiment analysis, Pearson correlation, Granger causality & uncertainty coefficient.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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InterpretTime: a new approach for the systematic evaluation of neural-network interpretability in time series classification
We present a novel approach to evaluate the performance of interpretability methods for time series classification, and propose a new strategy to assess the similarity between domain experts and machine data interpretation.
Nektar++: enhancing the capability and application of high-fidelity spectral/$hp$ element methods
Nektar++ is an open-source framework that provides a flexible, high-performance and scalable platform for the development of solvers for partial differential equations using the high-order spectral/$hp$ element method.
Mathematical Software Numerical Analysis Numerical Analysis Fluid Dynamics
