Gradient-weighted class activation mapping (Grad-CAM) is an explainability technique that can be used to help understand the predictions made by a deep neural network [3]. Grad-CAM, a generalization of the CAM
This has traditionally required humans to manually(opens in a new window) inspect neurons(opens in a new window) to figure out what features of the data they represent. This process doesn’t scale well: it’s hard to apply it to neural networks with tens or hundreds of ...
When training simple models (like, for example, a logistic regression model), answering such questions can be trivial. But when a more performant model is necessary, like with a neural network, XAI techniques can give approximate answers for both the whole model and single predictions. KNIME can...
We applied three machine learning algorithms: decision tree (DT), support vector machine (SVM) and artificial neural network (ANN). For each algorithm we automatically extract diagnosis rules. For formalising expert knowledge, we relied on the normative dataset [13]. For arguing be- tween agents...
Tensorflow Slim Grad-Cam to Explain Neural Network Predictions with Heatmap or Shading - hiveml/tensorflow-grad-cam
SelfExplainML/PiML-ToolboxPublic NotificationsYou must be signed in to change notification settings Fork123 Star1.2k main BranchesTags Code README Apache-2.0 license An integrated Python toolbox for interpretable machine learning pip install PiML ...
Deep neural networks (DNNs) models have the potential to provide new insights in the study of cognitive processes, such as human decision making, due to their high capacity and data-driven design. While these models may be able to go beyond theory-driven models in predicting human behaviour, ...
(see below). Induction of NEIs was not observed when “bald” viruses were applied (Supplementary Fig.3b, see below). Indirectly, these data suggest that EVs (≈2.2 × 108particles/ml) present in both the “bald” and VSV-G-pseudotyped virus preparations did not contribute to the ...
One example of a black-box machine learning model is a simple neural network model with one or two hidden layers. Even though you can write out the equations that link every input in the model to every output, you might not be able to grasp the meaning of the connections simply by ...
We present an integrative model of disease mechanisms in the Chronic Fatigue Syndrome (CFS), unifying empirical findings from different research traditions. Based upon the Cognitive activation theory of stress (CATS), we argue that new data on cardiovascular and thermoregulatory regulation indicate a st...