Energy-based learning (EBL) is a general framework to describe supervised and unsupervised training methods for probabilistic and non-probabilistic factor graphs. An energy-based model associates a scalar energy to configurations of inputs, outputs, and latent variables. Learning machines can be constructed by assembling modules and loss functions. Gradient-based learning procedures are easily implemented through semi-automatic differentiation of complex models constructed by assembling predefined modules. We introduce an open-source and cross-platform C++ library called EBLearn1 to enable the construction of energy-based learning models. EBLearn is composed of two major components, libidx: an efficient and flexible multi-dimensional tensor library, and libeblearn: an object-oriented library of trainable modules and learning algorithms. The latter has facilities for such models as convolutional networks, as well as for image processing. It also provides graphical display functions. © 2009 IEEE.
CITATION STYLE
Sermanet, P., Kavukcuoglu, K., & LeCun, Y. (2009). EBLearn: Open-source energy-based learning in C++. In Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI (pp. 693–697). https://doi.org/10.1109/ICTAI.2009.28
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