0 bookmark(s) - Sort by: Date ↓ / Title / - Bookmarks from other users for this tag
Researchers from the University of California San Diego have developed a mathematical formula that explains how neural networks learn and detect relevant patterns in data, providing insight into the mechanisms behind neural network learning and enabling improvements in machine learning efficiency.
MIT researchers developed a system that uses large language models to convert AI explanations into narrative text that can be more easily understood by users, aiming to help with better decision-making about model trustworthiness.
The system, called EXPLINGO, leverages large language models (LLMs) to convert machine-learning explanations, such as SHAP plots, into easily comprehensible narrative text. The system consists of two parts: NARRATOR, which generates natural language explanations based on user preferences, and GRADER, which evaluates the quality of these narratives. This approach aims to help users understand and trust machine learning predictions more effectively by providing clear and concise explanations.
The researchers hope to further develop the system to enable interactive follow-up questions from users to the AI model.
An article detailing how to build a flexible, explainable, and algorithm-agnostic ML pipeline with MLflow, focusing on preprocessing, model training, and SHAP-based explanations.
This article provides a non-technical guide to interpreting SHAP analyses, useful for explaining machine learning models to non-technical stakeholders, with a focus on both local and global interpretability using various visualization methods.
The article discusses an interactive machine learning tool that enables analysts to interrogate modern forecasting models for time series data, promoting human-machine teaming to improve model management in telecoms maintenance.
This article introduces interpretable clustering, a field that aims to provide insights into the characteristics of clusters formed by clustering algorithms. It discusses the limitations of traditional clustering methods and highlights the benefits of interpretable clustering in understanding data patterns.
Gemma Scope is an open-source, multi-scale, high-throughput microscope system that combines brightfield, fluorescence, and confocal microscopy, designed for imaging large samples like brain tissue.
An article discussing the importance of explainability in machine learning and the challenges posed by neural networks. It highlights the difficulties in understanding the decision-making process of complex models and the need for more transparency in AI development.
This article explains the concept and use of Friedman's H-statistic for finding interactions in machine learning models.
First / Previous / Next / Last
/ Page 1 of 0