SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations). ...
Learning to Detect Salient Objects With Image-Level Supervision CVPR code 25 DeepCD: Learning Deep Complementary Descriptors for Patch Representations ICCV code 25 Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon NIPS code 25 State-Frequency Memory Recurrent Neural Networks ICM...
light sensors, humidity and air pressure sensors could be introduced and these are considered FIPS 140-2 Level 4 specifications (highest of the FIPS 140-2) if the key mats are properly provisioned according to FIPS 140-2 Level 1-3 as well. ...