Search Results for author:
Found 4 papers, 3 papers with code
Improving Explainability of Disentangled Representations using Multipath-Attribution Mappings
We propose a framework that utilizes interpretable disentangled representations for downstream-task prediction.
Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment
Thus, today's AL literature presents an inconsistent and contradictory landscape, leaving practitioners uncertain about whether and how to use AL in their tasks.
A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification
To demonstrate the relevance of this unified perspective, we present a large-scale empirical study for the first time enabling benchmarking confidence scoring functions w. r. t all relevant methods and failure sources.
From Correlation to Causation: Formalizing Interpretable Machine Learning as a Statistical Process
Based on these similarities, we present a formalization of IML along the lines of a statistical process.
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
+1
