DeepAI AI Chat
Log In Sign Up

A Neural Network for Semigroups

by   Edouard Balzin, et al.

Tasks like image reconstruction in computer vision, matrix completion in recommender systems and link prediction in graph theory, are well studied in machine learning literature. In this work, we apply a denoising autoencoder-based neural network architecture to the task of completing partial multiplication (Cayley) tables of finite semigroups. We suggest a novel loss function for that task based on the algebraic nature of the semigroup data. We also provide a software package for conducting experiments similar to those carried out in this work. Our experiments showed that with only about 10 the available data, it is possible to build a model capable of reconstructing a full Cayley from only half of it in about 80


page 1

page 2

page 3

page 4


Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning

Autoencoders are popular among neural-network-based matrix completion mo...

Extendable Neural Matrix Completion

Matrix completion is one of the key problems in signal processing and ma...

Graph Convolutional Matrix Completion

We consider matrix completion for recommender systems from the point of ...

Image Reconstruction using Enhanced Vision Transformer

Removing noise from images is a challenging and fundamental problem in t...

Mixture Matrix Completion

Completing a data matrix X has become an ubiquitous problem in modern da...

Log-Normal Matrix Completion for Large Scale Link Prediction

The ubiquitous proliferation of online social networks has led to the wi...