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Found 10 papers, 6 papers with code
A Rigorous Study Of The Deep Taylor Decomposition
Here, we investigate the DTD theory to better understand this perplexing behavior and found that the Deep Taylor Decomposition is equivalent to the basic gradient$\times$input method when the Taylor root points (an important parameter of the algorithm chosen by the user) are locally constant.
DNNR: Differential Nearest Neighbors Regression
K-nearest neighbors (KNN) is one of the earliest and most established algorithms in machine learning.
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset
In a study, we assess if participants can identify the relevant set of attributes compared to the ground-truth.
Analyzing a Caching Model
Machine Learning has been successfully applied in systems applications such as memory prefetching and caching, where learned models have been shown to outperform heuristics.
Two4Two: Evaluating Interpretable Machine Learning - A Synthetic Dataset For Controlled Experiments
However, few of these approaches are subjected to human-subject evaluations, partly because it is challenging to design controlled experiments with natural image datasets, as they leave essential factors out of the researcher's control.
Interpretability Through Invertibility: A Deep Convolutional Network With Ideal Counterfactuals And Isosurfaces
Using PCA on the classifier’s input, we can also create “isofactuals”– image interpolations with the same outcome but visually meaningful different features.
Restricting the Flow: Information Bottlenecks for Attribution
Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks.
When Explanations Lie: Why Many Modified BP Attributions Fail
The paper provides a framework to assess the faithfulness of new and existing modified BP methods theoretically and empirically.
Automatic localization and decoding of honeybee markers using deep convolutional neural networks
Tracking and identifying all bees in the colony over their lifetimes therefore may likely shed light on the interplay of individual differences and colony behavior.
RenderGAN: Generating Realistic Labeled Data
Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable performance on many computer vision tasks.
