senjed
senjed
How can the integrated gradient explainer be used for a model with multiple outputs (e.g. a multi-task classification model where each output can have few possible values)? Can you please...
I am trying to build the same matrix such as 21_food_inspection_violation_matrix_nums.csv for the more recent inspections by parsing the Violations column. My assumption is that if violation v_i is mentioned...
I noticed that the generate_input_dart.py only used 3 references for evaluation. However, some examples have many more references. I was wondering if you could provide more details about the results...
I tried to run the example you provided with iris dataset with keras. But I am getting AttributeError: 'int' object has no attribute 'op' error. My TensorFlow version is 2.2....
Thank you so much for the great work! I was wondering for multi-task models how can the attributions from two separate tasks be combined to understand the importance of features...