OpenXAI : Towards a Transparent Evaluation of Model Explanations
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Updated
Aug 17, 2024 - JavaScript
OpenXAI : Towards a Transparent Evaluation of Model Explanations
Love2D LSP (VS Code / Neovim / Zed / etc.) extension for live coding and live variable tracking
[ICLR 2026] DecAlign: Hierarchical Cross-Modal Alignment for Decoupled Multimodal Representation Learning
Editing machine learning models to reflect human knowledge and values
🏔️ Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations
A user interface to interpret machine learning models.
Designing a Dashboard for Transparency and Control of Conversational AI, https://arxiv.org/abs/2406.07882
Visually compare fill-in-the-blank LLM prompts to uncover learned biases and associations!
A Python Toolkit for Explainable IR methods
Online exploration of memory reduction strategies of a DRL agent trained to solve a navigation task on ViZDoom
ir_explain: a Python Library of Explainable IR Methods
Explainable AI & NLP interpretability dashboard analyzing musical semantics, emotional vectors, and lyrical themes using YouTube API.
Visual analytics approach presented in the paper "Visual Analytics Tool for the Interpretation of Hidden States in Recurrent Neural Networks" (VCIBA, 2021).
Web based interpretability tool for LLMs using Huggingface and Inseq
A web user interface for the OncoText Pathology System (https://github.com/yala/OncoText)
A curated reading list on feature superposition in neural networks, plus a daily arXiv feed on superposition and mechanistic interpretability.
Build explainable machine learning products and services
Parameter Space Inspector: ask questions about how a language model works and settle them by measuring a GGUF model on your own computer.
Make inference visible. A source-grounded 3D observatory for DeepSeek V4.1 Flash, with CPU-only synthetic execution, token traces, and English/Chinese UI.
[AAAI-24 Paper] Using Stratified Sampling to improve LIME image explanations
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