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@yannikschaelte wrote via mail:
> We may want to adopt a more flexible way of defining the objective function, gradient and Hessian, similar to pypesto.
I thought about this, but was hesitant fo…
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Does MyGrad support higher order derivatives like Hessians?
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**Python: 3.8
TFQ: 0.4.0**
Hi,
I am trying to get some Hessians from the one of my parameterized quantum circuits. This trainstep works as intended:
```python
def build_train_step(circuit: cirq…
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TODO: check if https://github.com/JuliaDiff/AbstractDifferentiation.jl can be used for specifying differentiation backend (also relevant to Manopt.jl).
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Hey there Avik!
As you may know, I have been busy developing [DifferentiationInterface.jl](https://github.com/gdalle/DifferentiationInterface.jl), and it's really starting to take shape.
I was won…
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Hi,
I have a question about how gradients are combined in multi-target models using built in objectives like reg:squarederror? are they summed up? and in which file can find this gradients combinatio…
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Hi Shangyu,
Thanks a lot for sharing PyTorch code for applying LOBS on various ImageNet CNNs.
I could run the code perfectly after a couple of minor error/syntax corrections required due to Py…
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Hello, this repository is very interesting. I am studying for a PhD in normalizing flow, and when reviewing your code, I could not see where the Jacobian matrix is calculated. Is it a repository for b…
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How much work would it be to add something like `valhessian!(H, schain, x, p)`? Where `H` is the Hessian of a simple chain with loss.
jbrea updated
7 months ago
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### 🐛 Describe the bug
Hi !
I'm trying to backward my model along multiple directions, so I'm using `torch.autograd.grad` with `is_grads_batched=True`. I had no problem using it on a MLP, but wh…