280 citations · 387 across the 9 of their papers we have counts for
Showing 2017 · cs.LGShow all
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cs.LG2017★ 280 cited
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients
Andrew Slavin Ross, Finale Doshi-Velez
Deep neural networks have proven remarkably effective at solving many classification problems, but have been criticized recently for two major weaknesses: the reasons behind their…
cs.LG2017
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez
Neural networks are among the most accurate supervised learning methods in use today, but their opacity makes them difficult to trust in critical applications, especially when cond…