Interpretability Beyond Feature Attribution

Interpretability Beyond Feature Attribution

Share:

Similar Tracks

NL- Use Cases at Uber MLconf

AAAI 2021 Tutorial on Explaining Machine Learning Predictions UCI NLP

Kilian Weinberger, "Interpretable Machine Learning" Emergence of Intelligent Machines

Quantitative Testing with Concept Activation Vectors (TCAV) -- Been Kim (Google) - 2018 Center for Language & Speech Processing(CLSP), JHU

A Roadmap for the Rigorous Science of Interpretability | Finale Doshi-Velez | Talks at Google Talks at Google

ICLR 2022 Keynote: Been Kim Been Kim

Explainable AI for Science and Medicine Microsoft Research

Interpretable Neural Networks for Computer Vision: Clinical Decisions that are Aided, not Automated Cynthia Rudin

Been Kim wants interpretability for everyone OATML research group

Computer Scientist Explains Machine Learning in 5 Levels of Difficulty | WIRED WIRED

Introduction to Explainable AI (ML Tech Talks) TensorFlow

Practical Tips for Interpreting Machine Learning Models - Patrick Hall, H2O.ai H2O.ai

Brain Matters documentary | Early Childhood Development Brain Matters

But what is a neural network? | Deep learning chapter 1 3Blue1Brown

Explainable AI - Making ML and DL Models More Interpretable Aditya Bhattacharya

MLconf SF 2022: Semantic Label Representation and Multimodal Categorization, Binwei Yang @Walmart MLconf

PR-167: Interpretability Beyond Feature Attribution: Testing with Concept Activation Vector (TCAV) Jinsung Yoon

Open the Black Box: an Introduction to Model Interpretability with LIME and SHAP - Kevin Lemagnen PyData

11. Introduction to Machine Learning MIT OpenCourseWare