Comments (2)
Hi Han,
Sorry for the late reply. These two approaches answer different questions: the first is when you want to see which training points are influential (i.e., what would happen if you removed them?) and the second is when you want to see how the model would respond if the training points were slightly different (e.g., if you were interested in what would happen if there was additive noise).
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Thanks for the explanation!
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Related Issues (20)
- Pytorch implementation HOT 26
- Perturbation to find influential features in training data HOT 6
- Computing influence on a CNN HOT 2
- Need help: what does `set_params_op` do? HOT 2
- How can we implement this method on LSTM HOT 4
- Why the hessian vector product is calculated by mini-batch? HOT 3
- Maybe a bug: retrain without re-initialization? HOT 5
- Error in influence calculation for spam experiment HOT 2
- Mismatch between IHVP computation in code and paper? HOT 1
- why does inverse_hvp / scale after the iteration in get_inverse_hvp_lissa HOT 1
- Minor error in documentation HOT 3
- Typo in paper HOT 1
- Local issue running Hospital Readmission notebook HOT 3
- Adapting this approach to regressions tasks not just classification HOT 1
- Influence value for RNN models HOT 1
- Can't understand what function "get_inverse_hvp_lissa" do HOT 1
- How much system memory is required in this source ? HOT 2
- How is the damping term determined? HOT 2
- How to Computing Influence Function HOT 5
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