This is where I keep my research/productive work.
For more information about me see my website
Tinkering, partially done projects and non-professional work can be found under my private account
Code for the paper "Combining Gradients and Probabilities for Heterogeneours Approximation of Neural Networks"
Home Page: https://etrommer.github.io/agn-approx/
License: GNU General Public License v3.0
This is where I keep my research/productive work.
For more information about me see my website
Tinkering, partially done projects and non-professional work can be found under my private account
TorchApprox has been extended to use PyTorch's internal affine quantization: https://github.com/etrommer/torch-approx/tree/feature/torchquant
It needs to be benchmarked in order to assess whether this improves inference accuracy.
Experimental setups:
Current changes have broken the existing documentation. The documentation should be updated to provide a minimal working example on the current head of master branch.
Sphinx documentation currently needs to be built locally due to non-public dependencies. Move to CI as soon as torchapprox
is published.
Evaluate accuracy of HTP models on several architecture:
Models to evaluate (all evaluation with behavioral simulation):
MLFlow logging is currently disabled in the trainer instance if the log_mlflow
parameter is not set. The logging instance however still creates an experiment. Import and usage of MLFlow could be made fully dependent on an environment variable instead.
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