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Search results for jupyter notebook interpretable deep learning
interpretable-deep-learning
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jupyter-notebook
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13 search results found
Understanding Nn
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222
Tensorflow tutorial for various Deep Neural Network visualization techniques
Path_explain
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134
A repository for explaining feature attributions and feature interactions in deep neural networks.
Visual Attribution
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117
Pytorch Implementation of recent visual attribution methods for model interpretability
Gradcam_plus_plus Pytorch
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102
A Simple pytorch implementation of GradCAM and GradCAM++
Attributionpriors
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75
Tools for training explainable models using attribution priors.
Deep Explanation Penalization
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74
Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584
Nn_interpretability
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37
Pytorch implementation of various neural network interpretability methods
Cross Modal Transformer
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32
Official repository of cross-modal transformer for interpretable automatic sleep stage classification. https://arxiv.org/abs/2208.06991
Fastism
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14
In-silico Saturation Mutagenesis implementation with 10x or more speedup for certain architectures.
Iseeu
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14
ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU
Malaria Detection Using Deep Learning Techniques
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9
Malaria Parasite Detection using Efficient Neural Ensembles. Malaria, a life threatening disease caused by the bite of the Anopheles mosquito infected with the parasite, has been a major burden towards healthcare for years leading to approximately 400,000 deaths globally every year. This study aims to build an efficient system by applying ensemble techniques based on deep learning to automate the detection of the parasite using whole slide images of thin blood smears.
Interpret Lm Knowledge
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8
Extracting knowledge graphs from language models as a diagnostic benchmark of model performance.
Visualizations Of The Interpretations Of Cnns
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7
Using Sensitivity Heatmap, Deconvolution, Guided Backpropagation, Class Activation Mapping, Grad-CAM and Guided Grad-CAM methods to interpret the predictions of different CNN models
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1-13 of 13 search results
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