Instance-wise Linearization of Neural Network for Model Interpretation
Quick summary
arXiv:2310.16295v2 Announce Type: replace-cross Abstract: Neural network have achieved remarkable successes in many scientific fields. However, the interpretability of the neural network model is still a major bottlenecks to deploy such technique into our daily life. The challenge can dive into the non-linear behavior of the neural network, which rises a critical question that how a model use input feature to make a decision. The classical approach to address this challenge is feature attribution, which assigns an important score to each input feature and reveal its importance of current predi
Key takeaways
- arXiv:2310.16295v2 Announce Type: replace-cross Abstract: Neural network have achieved remarkable successes in many scientific fields.
- However, the interpretability of the neural network model is still a major bottlenecks to deploy such technique into our daily life.
- The challenge can dive into the non-linear behavior of the neural network, which rises a critical question that how a model use input feature to make a decision.
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
Member comments