Edge 261: Local Model-Agnostic Interpretability Methods: LIME
Was this email forwarded to you? Sign up here Edge 261: Local Model-Agnostic Interpretability Methods: LIMELIME, Meta AI research on interpretable neurons and the Alibi Explain framework.In this issue:
💡 ML Concept of the Day: Local Model-Agnostic Interpretability Methods: LIMEContinuing our series about ML interpretability, today we would like to cover one of the most popular local model-agnostic methods. Local interpretable model-agnostic explanations (LIME) is omnipresent in all literature and frameworks related to ML interpretability. Just like other local methods, LIME does not try to understand the complete behavior of a model but rather the changes in individual predictions. What sets LIME apart, are the specific mechanics used to derive the interpretations. LIME introduces perturbations in the input features and observes the resulting impact in the output. Using a more technical nomenclature, given an example x, LIME tries to fit a local interpretable model that matches the output of the original model with features in close proximity to f(x). Once a local model is chosen, LIME tries to fit it to a new training set formed by adding perturbations to the original feature space... Subscribe to TheSequence to read the rest.Become a paying subscriber of TheSequence to get access to this post and other subscriber-only content. A subscription gets you:
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Edge 262: NVIDIA’s Get3D is a Generative AI Model for 3D Shapes
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Edge 259: Local Model-Agnostic Interpretability Methods: SHAP
Tuesday, January 10, 2023
SHAP method, MIT taxonomy for ML interpretability and BAIR's iModels framework.
NVIDIA Latest Push in Generative AI the Metaverse
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Edge 258: Inside OpenAI's Point-E: The New Foundation Model Able to Generate 3D Representations from Language
Thursday, January 5, 2023
The new model combines GLIDE with image-to-3D generation models is a very clever and efficient architecture.
Edge 257: Local Model-Agnostic Interpretability Methods
Tuesday, January 3, 2023
Local model-agnostic interpretability, IBMs ProfWeight research and the InterpretML framework.
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