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Why would you want to do that? Just curious
by blovescoffee 4y ago
Why would you want to do that? Just curious
- deleted 4y ago[deleted]
- pizza 4y agoOne thing I've thought about is a static typed compiled neural network framework. To do backprop generally you create a computation graph and find the gradients from e.g. reverse-mode automatic differentiation. Just tracing along operations along paths in a network - which seems a little similar to the DSP graph signal processing in this project. But one nice property of this project's graph processing framework is that the graphs are typed and compiled - which reminded me of the things I think nn frameworks generally lack. But even without introducing the whole baggage of an entire neural network framework, you could in theory port an e.g. audio processing neural net from e.g. PyTorch into the equivalent chain of operations in this framework. Perhaps it could exploit the gradient to do some finetuning of the audio processing operation at runtime, etc.