Optimal training of integer-valued neural networks with mixed integer programming

• URL: http://arxiv.org/abs/2009.03825v5
• Date: Fri, 31 Mar 2023 17:15:54 GMT
• Title: Optimal training of integer-valued neural networks with mixed integer programming
• Authors: T\'omas Thorbjarnarson and Neil Yorke-Smith
• Abstract summary: We develop new MIP models which improve training efficiency and which can train the important class of integer-valued neural networks (INNs) We provide a batch training method that dramatically increases the amount of data that MIP solvers can use to train. Experimental results on two real-world data-limited datasets demonstrate that our approach strongly outperforms the previous state of the art in training NNs with MIP.
• Score: 2.528056693920671
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent work has shown potential in using Mixed Integer Programming (MIP) solvers to optimize certain aspects of neural networks (NNs). However the intriguing approach of training NNs with MIP solvers is under-explored. State-of-the-art-methods to train NNs are typically gradient-based and require significant data, computation on GPUs, and extensive hyper-parameter tuning. In contrast, training with MIP solvers does not require GPUs or heavy hyper-parameter tuning, but currently cannot handle anything but small amounts of data. This article builds on recent advances that train binarized NNs using MIP solvers. We go beyond current work by formulating new MIP models which improve training efficiency and which can train the important class of integer-valued neural networks (INNs). We provide two novel methods to further the potential significance of using MIP to train NNs. The first method optimizes the number of neurons in the NN while training. This reduces the need for deciding on network architecture before training. The second method addresses the amount of training data which MIP can feasibly handle: we provide a batch training method that dramatically increases the amount of data that MIP solvers can use to train. We thus provide a promising step towards using much more data than before when training NNs using MIP models. Experimental results on two real-world data-limited datasets demonstrate that our approach strongly outperforms the previous state of the art in training NN with MIP, in terms of accuracy, training time and amount of data. Our methodology is proficient at training NNs when minimal training data is available, and at training with minimal memory requirements -- which is potentially valuable for deploying to low-memory devices.

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