Difference between revisions of "Object Recognition with Deep Boltzmann Machines"
CarloDEramo (Talk | contribs)
m (FrancescoVisin moved page Object Recognition with Deep Belief Networks to Object Recognition with Deep Boltzmann Machines without leaving a redirect)
Latest revision as of 17:09, 3 July 2014
This project aims to exploit the ability of Deep Boltzmann machines (DBM) to classify (and generate) multiclass objects. Initially, they will be used to classify road signs with the purpose to help the navigation of an hypotetical autonomous car. Since the initial results on the classification tasks of road signs using DBM are very good, we are pretty confident about the quality of the project. After that, we will consider the possibility to extend the task to other domains.
Object recognition with DBMs
|Short Description:||Deep Boltzmann machines for classification tasks|
|Tutor:||MatteoMatteucci (firstname.lastname@example.org), FrancescoVisin (email@example.com)|
|Research Topic:||Machine Learning|
Deep learning and Boltzmann machines
Deep learning is a set of algorithms in machine learning that attempt to model high-level abstractions in data by using architectures composed of multiple non-linear transformations. We will work on Boltzmann machines, an energy-based model which has been proven to perform very well in image and speech recognition tasks. A Boltzmann machines is composed of visible units representing the inputs of the network provided by the dataset and hidden units representing the feature detectors of the net. In a general fully connected Boltzmann machine each pair of unit is connected with a symmetric connection with a certain weights. We will focus on DBMs that are a particular type of Boltzmann machines built by stacking several layers of RBM on top of each other in a proper way. A RBM is known as restricted Boltzmann machines, a Boltzmann machines with no intra-layer connections. We'll study also deep belief network (DBN), a hybrid model between a neural network with discriminative and generative connections and a DBM, which has only symmetric connections. We will use Pylearn2 framework which offers training procedures of deep neural networks. During the project we will go deep in the analysis of this kind of networks studying their potentiality and flexibility; we'll try different configuration of networks in order to obtain the good performances we're looking for.