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Found 7 papers, 4 papers with code
Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory
For comparison, Sch\"utt et al. (2019) spent 626 hours creating a dataset on which they trained their NN for 160h, for a total of 786h; our method achieves comparable performance within 31h.
On Quantitative Evaluations of Counterfactuals
As counterfactual examples become increasingly popular for explaining decisions of deep learning models, it is essential to understand what properties quantitative evaluation metrics do capture and equally important what they do not capture.
ECINN: Efficient Counterfactuals from Invertible Neural Networks
Counterfactual examples identify how inputs can be altered to change the predicted class of a classifier, thus opening up the black-box nature of, e. g., deep neural networks.
Backpropagating through Fréchet Inception Distance
Using FID as an additional loss for Generative Adversarial Networks improves their FID.
What if Neural Networks had SVDs?
We present an algorithm that is fast enough to speed up several matrix operations.
MeLIME: Meaningful Local Explanation for Machine Learning Models
Additionally, we introduce modifications to standard training algorithms of local interpretable models fostering more robust explanations, even allowing the production of counterfactual examples.
