TY - GEN
T1 - EBLearn
T2 - 21st IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2009
AU - Sermanet, Pierre
AU - Kavukcuoglu, Koray
AU - LeCun, Yann
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77949515497&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77949515497&partnerID=8YFLogxK
U2 - 10.1109/ICTAI.2009.28
DO - 10.1109/ICTAI.2009.28
M3 - Conference contribution
AN - SCOPUS:77949515497
SN - 9781424456192
T3 - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
SP - 693
EP - 697
BT - ICTAI 2009 - 21st IEEE International Conference on Tools with Artificial Intelligence
Y2 - 2 November 2009 through 5 November 2009
ER -