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Optimizing control variates for black-box gradient estimation
surrogate_cond, grad_surrogate_cond = value_and_grad(surrogate_cond)(params):
value_and_grad only applies to real scalar-output functions. Try jacobian, elementwise_grad or holomorphic_grad.
Dear authors,
Thanks for sharing your code. I noticed a difference between the code and paper in computing \tilde{z}. Specifically, in the code 1-\theta is performed, but in Appendix B of the paper when computing v', \theta is directly used. Is the latter a typo, or am I missing something?
Thanks
I found that in the Appendix B of paper, v' can still differentiate with respect to \theta but the code (https://github.com/duvenaud/relax/blob/master/pytorch_toy.py#L105) omits it. Why? Because the effect of it is small?
Hi, can anyone help me? It got the following issue?
Traceback (most recent call last):
File "mnist_vae.py", line 100, in <module>
gradvars = inf_gradvars + gen_gradvars + rebar_optimizer.variance_gradvars
TypeError: unsupported operand type(s) for +: 'zip' and 'list'
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