Visualizing the Function Computed by a Feedforward Neural Network

By Tony A. Plate, P. Band, J. Bert, J. Grace. 1997

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. A. Goldstein
2. Josua Krause
3. Adam Kapelner
4. E. Pitkin
5. Marcus R. Gallagher
6. Justin Bleich
7. Adam Perer
8. T. Downs

Citations in the corpus

• Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation (2013, methodology): “Plate et al. (2000) plots neural network predictions in a scatterplot for each variable by sampling points from covari-2 ate space.”
• Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models (2016, background): “[36] and Olden [31] show how input features influence the outcome of neural network classifications.”
• Visualization of learning in multilayer perceptron networks using principal component analysis (2003, methodology): “For examples of visualization techniques developed to assist with such understanding, see [ 25 ] and [27].”
• ACCURACY VERSUS INTERPRETABILITY IN FLEXIBLE MODELING : IMPLEMENTING A TRADEOFF USING GAUSSIAN PROCESS MODELS (1999, methodology): “[12] describe how additive plots [8] can be adapted for visualizing non-additive models.”