Comments (3)
You can compute the accurate hessian vector product, but it turns out that it's just not necessary and would slow down the computation drastically. A mini-batch sample of the HVP within each iteration is sufficient to get good results. I encourage you to do some experiments with varying mini-batch sizes, let us know what you find out!
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@markus-beuckelmann Thank you for your kind reply. I am confused about why is mini-batch way faster? If we compute the whole hessian, the loss costs O(n), the hessian costs O(p^2), the hvp costs O(p^2). The code divides the data set into k groups, the lost costs O(n/k), the other stay the same for an iteration. I don't think it is even faster.
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In this case, IIRC, it's just to make sure it fits within the GPU memory.
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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
- Maybe a bug: retrain without re-initialization? HOT 5
- Error in influence calculation for spam experiment HOT 2
- Upweighting a training point vs Perturbing a training input 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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