# Learnergy: Energy-based Machine Learners

arXiv: https://arxiv.org/abs/2003.07443 · HTML: https://mlsanity.com/paper/2003.07443/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [Energy-based Dropout in Restricted Boltzmann Machines: Why not go random](https://mlsanity.com/paper/2101.06741/) (2021)
- [Development and Application of Self-Supervised Machine Learning for Smoke Plume and Active Fire Identification from the FIREX-AQ Datasets](https://mlsanity.com/paper/2501.15343/) (2025)
- [Fast Ensemble Learning Using Adversarially-Generated Restricted Boltzmann Machines](https://mlsanity.com/paper/2101.01042/) (2021)
- [Fusing Multi- and Hyperspectral Satellite Data for Harmful Algal Bloom Monitoring with Self-Supervised and Hierarchical Deep Learning](https://mlsanity.com/paper/2510.02763/) (2025)

## Abstract (arXiv)

Throughout the last years, machine learning techniques have been broadly encouraged in the context of deep learning architectures. An exciting algorithm denoted as Restricted Boltzmann Machine relies on energy- and probabilistic-based nature to tackle the most diverse applications, such as classification, reconstruction, and generation of images and signals. Nevertheless, one can see they are not adequately renowned compared to other well-known deep learning techniques, e.g., Convolutional Neural Networks. Such behavior promotes the lack of researches and implementations around the literature, coping with the challenge of sufficiently comprehending these energy-based systems. Therefore, in this paper, we propose a Python-inspired framework in the context of energy-based architectures, denoted as Learnergy. Essentially, Learnergy is built upon PyTorch to provide a more friendly environment and a faster prototyping workspace and possibly the usage of CUDA computations, speeding up their computational time.