apricot implements submodular optimization for the purpose of selecting subsets of massive data sets to train machine learning models quickly. See the documentation page: https://apricot-select.readthedocs.io/en/latest/index.html

Summarize Massive Datasets using Submodular Optimization

NAACL 2019: Submodular optimization-based diverse paraphrasing and its effectiveness in data augmentation

visual-inertial odometry (VINS-Mono) with motion-aware feature selection

(IJCAI 2019) Submodular Batch Selection for Training Deep Neural Networks

A collection of optimization algorithms for maximizing unconstrained submodular set functions.

Code for the paper: Combining Graph Degeneracy and Submodularity for Unsupervised Extractive Summarization

Memory Efficient Max Flow

Recommending movies based on a utility function of movie ratings

Greedy and Lazy Greedy Sub Modular Optimisation

Efficient greedy-based methods for constrained submodular optimization.

Some tools for submodular function minimization in Python

A GPU accelerated Submodular Optimization toolkit.

Probabilistic Gas Leak Rate Estimation using Submodular Function Maximization with Routing Constraints

Routines for submodular set function minimization

Submodular Subset Selection for Long-Document Question Answering

Implementation of Serial and Parallel Local Lazy Greedy for submodular b-matching

[AAAI 2023] Implementation of Submodular-Maximization-under-the-Intersection-of-Matroid-and-Knapsack-Constraints

delta-submodular work codes