12 citations · 12 across the 4 of their papers we have counts for
5 papers
Training Experimentally Robust and Interpretable Binarized Regression Models Using Mixed-Integer Programming
Sanjana Tule, Nhi Ha Lan Le, Buser Say
In this paper, we explore model-based approach to training robust and interpretable binarized regression models for multiclass classification tasks using Mixed-Integer Programming…
Planning with Learned Binarized Neural Networks Benchmarks for MaxSAT Evaluation 2021
Buser Say, Scott Sanner, Jo Devriendt +2
This document provides a brief introduction to learned automated planning problem where the state transition function is in the form of a binarized neural network (BNN), presents a…
Reward Potentials for Planning with Learned Neural Network Transition Models
Buser Say, Scott Sanner, Sylvie Thiébaux
Optimal planning with respect to learned neural network (NN) models in continuous action and state spaces using mixed-integer linear programming (MILP) is a challenging task for br…
Scalable Planning with Deep Neural Network Learned Transition Models
Ga Wu, Buser Say, Scott Sanner
In many real-world planning problems with factored, mixed discrete and continuous state and action spaces such as Reservoir Control, Heating Ventilation, and Air Conditioning, and…
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains
Ga Wu, Buser Say, Scott Sanner
Given recent deep learning results that demonstrate the ability to effectively optimize high-dimensional non-convex functions with gradient descent optimization on GPUs, we ask in…