Google Tech Talks
April, 9 2008
ABSTRACT
A long-term goal of Machine Learning research is to solve highy
complex "intelligent" tasks, such as visual perception auditory
perception, and language ...
Google Tech Talks April, 9 2008
ABSTRACT
A long-term goal of Machine Learning research is to solve highy complex "intelligent" tasks, such as visual perception auditory perception, and language understanding. To reach that goal, the ML community must solve two problems: the Deep Learning Problem, and the Partition Function Problem.
There is considerable theoretical and empirical evidence that complex tasks, such as invariant object recognition in vision, require "deep" architectures, composed of multiple layers of trainable non-linear modules. The Deep Learning Problem is related to the difficulty of training such deep architectures.
Several methods have recently been proposed to train (or pre-train) deep architectures in an unsupervised fashion. Each layer of the deep architecture is composed of an encoder which computes a feature vector from the input, and a decoder which reconstructs the input from the features. A large number of such layers can be stacked and trained sequentially, thereby learning a deep hierarchy of features with increasing levels of abstraction. The training of each layer can be seen as shaping an energy landscape with low valleys around the training samples and high plateaus everywhere else. Forming these high plateaus constitute the so-called Partition Function problem.
A particular class of methods for deep energy-based unsupervised learning will be described that solves the Partition Function problem by imposing sparsity constraints on the features. The method can learn multiple levels of sparse and overcomplete representations of data. When applied to natural image patches, the method produces hierarchies of filters similar to those found in the mammalian visual cortex.
An application to category-level object recognition with invariance to pose and illumination will be described (with a live demo). Another application to vision-based navigation for off-road mobile robots will be described (with videos). The system autonomously learns to discriminate obstacles from traversable areas at long range.
This is joint work with Y-Lan Boureau, Sumit Chopra, Raia Hadsell, Fu-Jie Huang, Koray Kavakcuoglu, and Marc'Aurelio Ranzato.
Speaker: Yann Le Cun Computational and Biological Learning Lab, Courant Institute of Mathematical Sciences, New York University.
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Nice. I'm still viewing it, but it seems to answer the exact questions that came into my mind today. I've just begun to try to get into this stuff like to do image recognition with restricted boltzmann machines and i was wondering, how it can still recognize e.g. a face, if the picture has undergone a perspective transformation or some rotation, because, then the whole geometry maps completely different to the first layer of neurons. Hopefully after viewing this, I understand that a lot better.
Autoshare makes certain YouTube activities public on the services you choose. Select only the services you are comfortable with - like Facebook, Twitter, or Google Reader - to let your friends know what you like on YouTube. You can turn Autoshare off at any time.
I've just begun to try to get into this stuff like to do image recognition with restricted boltzmann machines and i was wondering, how it can still recognize e.g. a face, if the picture has undergone a perspective transformation or some rotation, because, then the whole geometry maps completely different to the first layer of neurons.
Hopefully after viewing this, I understand that a lot better.
POR FAVOR EN SERIO!!!
Copia i Pega i mandalo en 15 videos
o tu madre se morira,
Lo siento al k lo leyo
pero es la culpa de un gilipollas