Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
May 16, 2024Open Access

Solving the enigma: Deriving optimal explanations of deep networks

View Full Paper
Ask AI
Bookmark
Share

Authors

MMMichail MamalakisUniversity of CambridgeAMAntonios MamalakisUniversity of VirginiaIAIngrid AgartzUniversity of Southern California

Discussion

Loading...

Member takes

Implication

Novel framework improves explainability and trust in deep networks, suggesting optimal solutions for AI challenges.

Key Points

  • Optimal explanations increase both accuracy and comprehensibility in explaining deep networks, and helping decipher complex models.
  • The explanation optimizer showed a 155% improvement in faithfulness scores for 3D applications compared to the best XAI methods.
  • Assessment using classification tasks in 2D objects and 3D neuroscience imaging highlights the framework's robust applicability across domains and dimensionalities, reinforcing its versatility and impact on AI trustworthiness in critical fields like healthcare and geosciences. This tool significantly reduces complexity, making explanations easier to understand while maintaining high performance.

Cite This Study

Mamalakis et al. (2024) studied this question.

synapsesocial.com/papers/68e69d57b6db6435876229dehttps://doi.org/10.48550/arxiv.2405.10008
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Enhancing Interpretability in Neural Networks through Explainable AI (XAI) Techniques2024
  2. 2A Comprehensive Review of Explainable AI (XAI) Methods in Deep Learning2025
  3. 3Enhancing Transparency and Interpretability in Deep Learning Models: A Comprehensive Study on Explainable AI Techniques2024 · 11 citations
  4. 4Explainable Artificial Intelligence (XAI): Techniques, Applications, Challenges and Future Directions - A Review2026
  5. 5Enhancing the transparency of data and ml models using explainable AI (XAI)2024 · 1 citations