GitHub

A comprehensive implementation of self-supervised reinforcement learning algorithms including contrastive learning, predictive models, and representation learning for RL tasks.

Self-supervised reinforcement learning is a technique where agents learn useful representations and skills from the environment through self-generated tasks, such as predicting next states or learning ...
Search Engine Land

Google algorithm updates 2025 in review: 3 core updates and 1 spam update

Google launched four official and confirmed algorithmic updates in 2025, three core updates and one spam update. This is in comparison to last year, in 2024, where we had seven confirmed updates, then ...
IEEE

Multi-Modal Self-Supervised Learning Algorithm-Based Product Recommendation

Abstract: With the rise of e-commerce, personalized recommendation algorithms have received much attention in recent years. Meanwhile, multimodal recommendation algorithms have become the next ...
Scientific Research Publishing

Sen, P.C., Hajra, M. and Ghosh, M. (2019) Supervised Classification Algorithms in Machine Learning: A Survey and Review. In: Mandal, J.K. and Bhattacharya, D., Eds., Emerging ...

ABSTRACT: Accurate prediction of malaria incidence is indispensable in helping policy makers and decision makers intervene before the onset of an outbreak and potentially save lives. Various ...
Seeking Alpha

MicroAlgo Inc. Announces a Quantum Entanglement-Based Novel Training Algorithm — Entanglement-Assisted Training Algorithm for Supervised Quantum Classifiers

shenzhen, May 16, 2025 (GLOBE NEWSWIRE) -- Shenzhen, May. 16, 2025––MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced the development of a novel quantum entanglement-based ...
Nasdaq

MicroAlgo Inc. Announces a Quantum Entanglement-Based Novel Training Algorithm — Entanglement-Assisted Training Algorithm for Supervised Quantum Classifiers

This press release contains statements that may constitute "forward-looking statements." Forward-looking statements are subject to numerous conditions, many of which are beyond the control of ...
Scientific Research Publishing

Colins, M. (2017) Machine Learning: An Introduction to Supervised and Un-Supervised Learning Algorithms. CreateSpace.

ABSTRACT: Clustering is an unsupervised machine learning technique used to organize unlabeled data into groups based on similarity. This paper applies the K-means and Fuzzy C-means clustering ...
IEEE

HEX: Hierarchical Emergence Exploitation in Self-Supervised Algorithms

Abstract: In this paper, we propose an algorithm that can be used on top of a wide variety of self-supervised (SSL) approaches to take advantage of hierarchical structures that emerge during training.