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Found 7 papers, 0 papers with code
Transform then Explore: a Simple and Effective Technique for Exploratory Combinatorial Optimization with Reinforcement Learning
The technique is originated from physics, but is very effective in enabling RL agents to explore to continuously improve the solutions during test.
A new nature inspired modularity function adapted for unsupervised learning involving spatially embedded networks: A comparative analysis
Unsupervised machine learning methods can be of great help in many traditional engineering disciplines, where huge amount of labeled data is not readily available or is extremely difficult or costly to generate.
Unsupervised Community Detection with a Potts Model Hamiltonian, an Efficient Algorithmic Solution, and Applications in Digital Pathology
We propose a fast statistical down-sampling of input image pixels based on the respective color features, and a new iterative method to minimize the Potts model energy considering pixel to segment relationship.
Visual Machine Learning: Insight through Eigenvectors, Chladni patterns and community detection in 2D particulate structures
In the current work, we aim to better understand the generic intuition underlying unsupervised ML with a focus on physical systems.
The Stochastic Replica Approach to Machine Learning: Stability and Parameter Optimization
We introduce a statistical physics inspired supervised machine learning algorithm for classification and regression problems.
An interacting replica approach applied to the traveling salesman problem
The {\it ensemble} of replicas evolves as to maximize the inter-replica correlation while simultaneously minimize the local intra-replica cost function (e. g., the total path length in the Traveling Salesman Problem within each replica).
Automatic Segmentation of Fluorescence Lifetime Microscopy Images of Cells Using Multi-Resolution Community Detection
The proposed MCD method outperformed a popular spectral clustering based method in performing FLIM image segmentation.
