mloss.org Eblearnhttp://mloss.orgUpdates and additions to EblearnenFri, 10 Oct 2008 22:20:23 -0000Eblearn pre-releasehttp://mloss.org/software/view/155/<html><p>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.
</p>
<p>In Eblearn, a learning machine is constructed by assembling modules.
Each module can be a functional module of a factor. Each module has at
least two methods: fprop, which computes the output(s) from the
input(s), and bprop, which computes the gradient of a loss function
with respect to the input(s) and internal parameters given the
gradient of the loss function with respect to the output(s).
Functional modules implement simple deterministic dependencies between
inputs and outputs. Factor modules implement non-deterministic
dependencies between inputs: the takes one or several input objects
and output a scalar energy, which can be interpreted as a negative
log-likelihood. Factor modules also have an infer method that produces
the combination of unknown inputs with the lowest energy. This design
allows semi-automatic differentiation of complex architectures for
gradient-based learning, as well as efficient MAP inference algorithms
for factor graphs. Eblearn uses a similar model and API as the machine
learning library distributed with the Lush language. All the trainable
parameters are collected in a single vector, which facilitates the
implementation of fancy optimization algorithms independently of the
structure of the learning machine.
</p>
<p>Eblearn implements convolutional networks for invariant recognition of
images and temporal sequences. It implements all the known tricks to
make gradient-based learning fast, including the stochastic diagonal
Levenberg-Marquardt method.
</p>
<p>Eblearn also provides utility functions to preprocess images and
access and manipulate datasets. It comes with a portable GUI toolkit
built on top of Qt, which enables the graphic visualization of
internal variables and other data. The library has been used
successfully to train face detectors, and object recognizers.
</p></html>Yann LeCun, Pierre Sermanet, Koray Kavukcuoglu, Cyril Poulet, Fu Jie HuangFri, 10 Oct 2008 22:20:23 -0000http://mloss.org/software/rss/comments/155http://mloss.org/software/view/155/machine learningconvolutional neural networksenergy based models