Results for "interpretability"

Mechanistic Interpretability Visualizations

Advanced mathematical AI interpretability: Category Theory, Homotopy Type Theory, Spectral Analysis, Causal Inference, Quantum Topology, and Sheaf Cohomology for WebAssembly. Implements functorial retrieval, univalence axioms, Cheeger bounds, do-calculus,

Mathematical AI interpretability plugin providing sheaf cohomology, spectral analysis, causal inference, and quantum topology for coherence validation, consensus verification, and hallucination prevention.

Neural coordination plugin for multi-agent swarm intelligence using SONA, GNN, and attention mechanisms

MCP Server implementing the Occam's Razor Thinking Tool for guided LLM reasoning.

A custom n8n node to clean data using Recursive Feature Elimination (RFE) powered by Python.

Black Box Precision: Unlocking High-Stakes Performance with Explainable AI

A JavaScript implementation of a basic Language Model (LM) and utilities designed to facilitate natural language processing tasks, inspired by advanced models like xAI's Grok. (not affiliated with xAI)

Interactive playground for ExplainAI demos

This project aims to implement a deep learning model for generating captions for images. Leveraging convolutional neural networks (CNNs) for feature extraction and recurrent neural networks (RNNs) for sequence generation, the model learns to understand th

Explain any ML models anywhere

Permutation feature importance

A library for analyzing LLM internals in web browsers.

Explain any ML models anywhere

Neuronpedia (Official)

React visualization components for ExplainAI

Core explainability algorithms and model interfaces for ExplainAI

Complete ExplainAI package - Interpret machine learning models visually and interactively, directly in JavaScript

Node.js CLI tools for ExplainAI