Applied Deep Learning 2023 - Lecture 12 - Explainable AI

About this lesson

Complete Playlist: https://www.youtube.com/playlist?list=PLNsFwZQ_pkE87JO3T_mvedVTlw0sjUzKh == Literature == 1. Molnar, Interpretable Machine Learning. 2019 2. Arrieta et al., Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI. 2019 3. Petsiuk et al., RISE: Randomized Input Sampling for Explaination of Black-box Models. 2018 4. Bau et al., GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. 2018 5. Koul et al., Learning Finite State Representations of Recurrent Policy Networks. 2018 6. Ribeiro et al. “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. 2016 7. Sarkar. Model Interpretation Strategies. 2018. 8. Lundberg et al. A Unified Approach to Interpreting Model Predictions. 2017 9. Tjoa et al. A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI. 2019 10. Liu et al. Towards Visually Explaining Variational Autoencoders. 2019 11. Mundhenk et al. Efficient Saliency Maps for Explainable AI. 2019 12. Angelov et al. Towards Explainable Deep Neural Networks (xDNN). 2019 13. Fan et al. On Interpretability of Artificial Neural Networks. 2020 14. Lundberg et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. 2018 15. Schreiber. Saliency Maps for Deep Learning. 2019 16. Simonyan et al. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. 2014 17. Yau et al. What Did You Think Would Happen? Explaining Agent Behaviour through Intended Outcomes. 2020

Original Description