2 citations · 2 across the 2 of their papers we have counts for
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Optimization over Trained Neural Networks: Going Large with Gradient-Based Algorithms
Jiatai Tong, Yilin Zhu, Thiago Serra +1
When optimizing a nonlinear objective, one can employ a neural network as a surrogate for the nonlinear function. However, the resulting optimization model can be time-consuming to…
Optimization over Trained (and Sparse) Neural Networks: A Surrogate within a Surrogate
Hung Pham, Aiden Ren, Ibrahim Tahir +2
In constraint learning, we use a neural network as a surrogate for part of the constraints or of the objective function of an optimization model. However, the tractability of the r…
Computational Tradeoffs of Optimization-Based Bound Tightening in ReLU Networks
Fabian Badilla, Marcos Goycoolea, Gonzalo Muñoz +1
The use of Mixed-Integer Linear Programming (MILP) models to represent neural networks with Rectified Linear Unit (ReLU) activations has become increasingly widespread in the last…
Optimization Over Trained Neural Networks: Taking a Relaxing Walk
Jiatai Tong, Junyang Cai, Thiago Serra
Besides training, mathematical optimization is also used in deep learning to model and solve formulations over trained neural networks for purposes such as verification, compressio…