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Dear Authors,
I am amazed that the performance of POET outperforms POFO so much. Since PyTorch does not have a traced backward graph, I am curious about which framework you are using.
The text was updated successfully, but these errors were encountered:
POET uses Gurobi for solving the MILP formulation. Gurobi can be replaced by cvxpy/coin-or or any open-source ILP solvers. We generate the graph and the corresponding dependencies ourselves. The network is defined using Pytorch-compatible methods.
To further highlight the benefit, POFO supports chain (linear) model graphs while POET is more generic and supports any arbitrary model graphs such as ResNets/BERTs etc.
Dear Authors,
I am amazed that the performance of POET outperforms POFO so much. Since PyTorch does not have a traced backward graph, I am curious about which framework you are using.
The text was updated successfully, but these errors were encountered: