Learnergy: Energy-based Machine Learners

TL;DR

Learnergy is a Python-based framework built on PyTorch that facilitates the development and prototyping of energy-based machine learning models like Restricted Boltzmann Machines, aiming to boost research and application in this area.

Contribution

The paper introduces Learnergy, a new framework that simplifies building energy-based models and enhances computational efficiency using CUDA, addressing the lack of accessible tools in this domain.

Findings

Provides a user-friendly environment for energy-based models

Enables faster prototyping with CUDA support

Aims to increase research and application in energy-based machine learning

Abstract

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.