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1
Intro
2
Explaining Predictions
3
Explanation Methods
4
Perturbation
5
Layer-wise Relevance Propagation
6
Approach 2: (Simple) Taylor Expansions
7
Simple Taylor Decomposition
8
Deep Taylor Decomposition
9
Best Practice for LRP
10
Evaluating Explanations
11
LRP Applied to Different Problems
12
Understanding Prediction Strategies
13
Understanding the Model
14
Understanding the Data
15
Meta-Explanations
16
Spectral Relevance Analysis (Spray)
17
Summary
Description:
Explore methods, applications, and recent developments in Explainable AI with Dr. Wojciech Samek in this 43-minute conference talk from ODSC Europe 2019. Discover the effectiveness of explanation techniques like Layer-wise Relevance Propagation (LRP) across various data types and neural architectures. Learn how LRP provides insights into individual predictions through heatmaps visualizing pixel relevance in decision-making processes. Gain understanding of perturbation methods, Taylor expansions, and best practices for LRP implementation. Examine LRP applications in diverse problem domains, strategies for interpreting prediction strategies, model comprehension, and data analysis. Explore meta-explanations and Spectral Relevance Analysis (Spray) before concluding with a discussion on challenges and open questions in the field of explainable AI.

Explainable AI - Methods, Applications & Recent Developments

Open Data Science
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