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Found 8 papers, 3 papers with code
Solving Mixed Integer Programs Using Neural Networks
Our approach constructs two corresponding neural network-based components, Neural Diving and Neural Branching, to use in a base MIP solver such as SCIP.
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing
We develop a framework for value-function-based deep reinforcement learning with a combinatorial action space, in which the action selection problem is explicitly formulated as a mixed-integer optimization problem.
The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification
We improve the effectiveness of propagation- and linear-optimization-based neural network verification algorithms with a new tightened convex relaxation for ReLU neurons.
CAQL: Continuous Action Q-Learning
Value-based reinforcement learning (RL) methods like Q-learning have shown success in a variety of domains.
Strong mixed-integer programming formulations for trained neural networks
We present an ideal mixed-integer programming (MIP) formulation for a rectified linear unit (ReLU) appearing in a trained neural network.
Strong convex relaxations and mixed-integer programming formulations for trained neural networks
We present strong convex relaxations for high-dimensional piecewise linear functions that correspond to trained neural networks.
Optimization and Control 90C11
How Could Polyhedral Theory Harness Deep Learning?
The holy grail of deep learning is to come up with an automatic method to design optimal architectures for different applications.
Bounding and Counting Linear Regions of Deep Neural Networks
We investigate the complexity of deep neural networks (DNN) that represent piecewise linear (PWL) functions.
