10 citations · 26 across the 5 of their papers we have counts for
6 papers
On Quantitative Evaluations of Counterfactuals
Frederik Hvilshøj, Alexandros Iosifidis, Ira Assent
As counterfactual examples become increasingly popular for explaining decisions of deep learning models, it is essential to understand what properties quantitative evaluation metri…
ECINN: Efficient Counterfactuals from Invertible Neural Networks
Frederik Hvilshøj, Alexandros Iosifidis, Ira Assent
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. We pr…
One Reflection Suffice
Alexander Mathiasen, Frederik Hvilshøj
Orthogonal weight matrices are used in many areas of deep learning. Much previous work attempt to alleviate the additional computational resources it requires to constrain weight m…
What if Neural Networks had SVDs?
Alexander Mathiasen, Frederik Hvilshøj, Jakob Rødsgaard Jørgensen +2
Various Neural Networks employ time-consuming matrix operations like matrix inversion. Many such matrix operations are faster to compute given the Singular Value Decomposition (SVD…
MeLIME: Meaningful Local Explanation for Machine Learning Models
Tiago Botari, Frederik Hvilshøj, Rafael Izbicki +1
Most state-of-the-art machine learning algorithms induce black-box models, preventing their application in many sensitive domains. Hence, many methodologies for explaining machine…
Backpropagating through Fréchet Inception Distance
Alexander Mathiasen, Frederik Hvilshøj
The Fréchet Inception Distance (FID) has been used to evaluate hundreds of generative models. We introduce FastFID, which can efficiently train generative models with FID as a loss…