20103

Learnergy: Energy-based Machine Learners

Mateus Roder, Gustavo Henrique de Rosa, João Paulo Papa
Department of Computing, São Paulo State University, Bauru, São Paulo, Brazil
arXiv:2003.07443 [cs.LG], (16 Mar 2020)

@misc{roder2020learnergy,

   title={Learnergy: Energy-based Machine Learners},

   author={Mateus Roder and Gustavo Henrique de Rosa and João Paulo Papa},

   year={2020},

   eprint={2003.07443},

   archivePrefix={arXiv},

   primaryClass={cs.LG}

}

Throughout the last years, machine learning techniques have been broadly encouraged in the context of deep learning architectures. An interesting algorithm denoted as Restricted Boltzmann Machine relies on energy- and probabilistic-based nature to tackle with the most diverse applications, such as classification, reconstruction, and generation of images and signals. Nevertheless, one can see they are not adequately renowned when 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 for providing a more friendly environment and a faster prototyping workspace, as well as, possibility the usage of CUDA computations, speeding up their computational time.
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