most citedImproving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients

280 citations · 327 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG2019

Ensembles of Locally Independent Prediction Models

Andrew Slavin Ross, Weiwei Pan, Leo Anthony Celi +1

Ensembles depend on diversity for improved performance. Many ensemble training methods, therefore, attempt to optimize for diversity, which they almost always define in terms of di…

cs.DB20192 cited

Learning Key-Value Store Design

Stratos Idreos, Niv Dayan, Wilson Qin +8

We introduce the concept of design continuums for the data layout of key-value stores. A design continuum unifies major distinct data structure designs under the same model. The cr…

cs.CY2019

Tackling Climate Change with Machine Learning

David Rolnick, Priya L. Donti, Lynn H. Kaack +19

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a po…

cs.LG201945 cited

Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning

Xuefeng Peng, Yi Ding, David Wihl +6

Sepsis is the leading cause of mortality in the ICU. It is challenging to manage because individual patients respond differently to treatment. Thus, tailoring treatment to the indi…

cs.LG2017280 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…