The network model was created to represent complex data relationships more effectively when compared to hierarchical models, to improve database performance and standards. It has entities which are organized in
The locally interpretable model-agnostic explanations (LIME) technique is an explainability technique used to explain the decisions made by a deep neural network. Given the decision of deep network for a piece of input data, the LIME technique calculates the importance of each feature of the input...
To address these needs, we construct fine-grained dynamic mobility networks from mobile-phone geolocation data, and use these networks to model the spread of SARS-CoV-2 within 10 of the largest metropolitan statistical areas (hereafter referred to as metro areas) in the USA. These networks map ...
This paper presents FireXplainNet, a Convolutional Neural Network (CNN) base model, designed specifically to address these limitations through enhanced efficiency and precision in wildfire detection. We optimized data input via specialized preprocessing techniques, significantly improving detection accuracy on...
Visualize how parts of the image affects neural network's confidence by occluding parts iteratively from tf_explain.callbacks.occlusion_sensitivity import OcclusionSensitivityCallback model = [...] callbacks = [ OcclusionSensitivityCallback( validation_data=(x_val, y_val), class_index=0, patch_...
data demonstrated that for time-lags (model order) greater than the actual values, the linear model’s coefficients are very close to 0, and the network efficiently detects that the signal samples at those time-lags are of no informative value. The same model order consideration has also been...
background data sample and the current input to be explained, and we assume the input features are independent then expected gradients will compute approximate SHAP values. In the example below we have explained how the 7th intermediate layer of the VGG16 ImageNet model impacts the output ...
In spite of the simplicity of its architecture, the attractor neural network might be considered to mimic human behavior in the meaning of semantic memory organization and its disorder. Although this model could explain various phenomenon in cognitive neuropsychology, it might become obvious that this...
Changing the architecture of the explained model. Training models with different activation functions improved explanation scores. We are open-sourcing our datasets and visualization tools for GPT‑4-written explanations of all 307,200 neurons in GPT‑2, as well as code for explanation and scoring...
In this paper we argue that major economic downturns should be seen as collapses in the client and supplier relations between production entities, i.e., collapses in the trade network. In a model where individual units like firms and workers produce more if they have a larger network, but wh...