Eblearn is an object-oriented C++ library that implements various machine learning models, including energy-based learning, gradient-based learning for machine composed of multiple heterogeneous modules. In particular, the library provides a complete set of tools for building, training, and running convolutional networks.
If you use EBLearn for your work, please cite:
Pierre Sermanet, Koray Kavukcuoglu and Yann LeCun: EBLearn: Open-Source Energy-Based Learning in C++, Proc. International Conference on Tools with Artificial Intelligence, IEEE, 2009.
EBLearn allows you to quickly build complex classifiers and regressors without writing a single line of code. Convenient tools are provided to package datasets, train your system and do a real-time test of your system using cameras and kinects.
EBLearn is self-contained and does not depend on external libraries for its core functionalities.
EBLearn includes several CPU optimizations including Intel IPP, SSE(experimental) and OpenMP(experimental) support as well as OpenMPI cluster support for training and detection.
To quickly get started with eblearn, please look at the Getting started Section. Here, we provide Tutorials and instructions for Download and Installation of EBLearn.
The Eblearn project is currently under development at the Computational and Biological Learning Laboratory, New York University's machine learning lab, led by Dr.Yann Lecun.
EBLearn is divided into three main sections, namely libidx, libeblearn and tools.