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(Feature request) Since the synaptic operations are differentiable with respect to the coefficients in their difference equations, they can also be optimized via backpropagation.
After learning, t…
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consider the a + b system, this has different outcomes. We could spec it as:
0, a -> a when a integer
a, 0 -> a when a integer
pos_integer, pos_integer -> pos_integer
neg_integer, neg_integer ->…
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As discussed on Slack, this is just to track the fix for the performance issue using `EnsembleDistributed`. There are performance gains on the forward solve when using `EnsembleDistributed`, but there…
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## 🚀 Feature
By using DataParallel, the engine creates a thread for each GPU. It should do this for backpropagation too, including within python-implemented autograd.Functions.
## Motivation
We'r…
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Hello,
I am currently using your code for surface normal estimation. I was wondering what is the benefit to calculate gradient(df) in loss function and do output.backward(gradient=df) in training pro…
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While fine-tuning LoRA using PyTorch Lightning, I consistently encounter an `assert param.grad is not None` error during backpropagation. Increasing the gradient accumulation steps delays the issue bu…
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Up to now, we've been using our 'func' graph to represent the neural network. This is good for educational purposes but we should now figure out how backpropagation (i.e., calculating gradients) can …
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Line 20 of algorithm 11 should be outside of the for loop that starts in line 8. Otherwise, the weights will be updated before all the gradients are computed.
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**This book is the best deep learning book I have ever seen. With your help, I also wrote a framework with personal characteristics.**
At the end of the study of the whole book, I have one more tec…
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https://ericzhang1412.github.io/2023/11/29/SpikingNN-on-trial/?
refs Error-backpropagation in temporally encoded networks of spiking neurons, 10.1016/S0925-2312(01)00658-0 Spatio-Temporal Backpropa…