Learnergy: Energy-based Machine Learners
This work provides a tool for researchers and practitioners to more easily experiment with energy-based models, which are less explored compared to other deep learning techniques, but it is incremental as it focuses on framework development rather than novel algorithmic advances.
The paper introduces Learnergy, a Python framework built on PyTorch for energy-based machine learning, specifically targeting Restricted Boltzmann Machines, to address the lack of research and implementations in this area, resulting in a more user-friendly environment and faster prototyping with CUDA support.
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.