Comments (1)
Thanks for pointing this out and the repro! Yes structured derivatives don't have structure annotation / jacobian implementation for scatter/gather primitives, and would be very inefficient currently (so I recommend using methods 1/2); will take a look and see if it can be improved.
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Related Issues (20)
- The analytical output of GP can not fit the result of NNGP generated by the nt.predict.gp_inference HOT 1
- Question: Relu Kernel Computation HOT 3
- Question: Connection MLE "parametrized" GP in infinite Width Limit vs minimizing MSE "parametrized" Kernel in infinite Width HOT 4
- Question regarding OOM issues HOT 3
- Question regarding lr in Neural Tangents Cookbook
- eNTK implementation uses deprecated xla attribute HOT 2
- Colab notebooks issue HOT 2
- How to obtain aleatoric uncertainty? HOT 2
- How to compute the empirical after kernel? HOT 1
- pip install issues HOT 2
- Erf function goes beyond [-1,1] HOT 2
- using stax.Cos(a=1.0, b=1.0, c=0.0) to get kernel from conv layer gives error HOT 2
- NTK is not PD
- stax.serial PSDness HOT 1
- How to use batch to gradient_descent_mse_ensemble ? HOT 1
- NTK/NNGP behavior in the infinite regime when weights are drawn from Gaussians with high standard deviation HOT 7
- NKT_mean output Nan, when the number of training sample is increased HOT 3
- Question regarding the cookbook
- Calling the empirical kernel function with different parameters returns same result
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