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Found 5 papers, 3 papers with code
Revolutionizing Mobile Interaction: Enabling a 3 Billion Parameter GPT LLM on Mobile
The field of Artificial Intelligence has witnessed remarkable progress in recent years, especially with the emergence of powerful large language models (LLMs) based on the transformer architecture.
Black Box Model Explanations and the Human Interpretability Expectations -- An Analysis in the Context of Homicide Prediction
Intending to shed light on the explanations generated by XAI measures and their interpretabilities, this research addresses a real-world classification problem related to homicide prediction, duly endorsed by the scientific community, replicated its proposed black box model and used 6 different XAI measures to generate explanations and 6 different human experts to generate what this research referred to as Interpretability Expectations - IE.
Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust Perspective
In recent years, XAI researchers have been formalizing proposals and developing new methods to explain black box models, with no general consensus in the community on which method to use to explain these models, with this choice being almost directly linked to the popularity of a specific method.
Binary Classification
Explainable artificial intelligence
+1
Does Dataset Complexity Matters for Model Explainers?
Seeking to answer questions such as "Are the explanations generated by the different measures the same, similar or different?"
Prediction of Homicides in Urban Centers: A Machine Learning Approach
This research presents a machine learning model to predict homicide crimes, using a dataset that uses generic data (without study location dependencies) based on incident report records for 34 different types of crimes, along with time and space data from crime reports.
