A game theoretic approach to explain the output of any machine learning model.
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A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Fit interpretable models. Explain blackbox machine learning.
A curated list of awesome responsible machine learning resources.
StellarGraph - Machine Learning on Graphs
Algorithms for explaining machine learning models
Steering-first neuron explorer for Qwen3.5-4B: push every MLP neuron, keep every rollout, browse the results
Interpretability and explainability of data and machine learning models
A collection of research materials on explainable AI/ML
moDel Agnostic Language for Exploration and eXplanation
An open source DevOps tool from the CNCF for packaging and versioning AI/ML models, datasets, code, and configuration into an OCI Artifact.
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
A computer, compiled into the weights of a language model. An exact CPU inside Qwen3.5-4B, with Gated DeltaNet state as RAM.
Build AI text detectors whose decisions are mechanistically grounded and human-auditable (EMNLP 2026)
A curated list of awesome Fairness in AI resources
⬛ Python Individual Conditional Expectation Plot Toolbox
Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University