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We have 4 results for Autonomously.
we make money not art: Olson, the independent camera 1 Citation
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New Scientist SPACE - Breaking News - Space-ferry may be ready by 2010 1 Citation Kliper is also being designed to operate completely autonomously, without the need for pilot control. "It's got to be capable of automatic flight," Thirkettle says.
Some on the team hope Kliper would be able to travel to the Moon, "but I think there's a little bit of science fiction in that", he says., blogmarks.net
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1 Citation Netsukuku is a mesh network or a P2P net system that generates and sustains itself autonomously. It is designed to handle an unlimited number of nodes with minimal CPU and memory resources. Thanks to this feature it can be easily used to build a worldwide distributed, anonymous and anarchical network, separated from the Internet, without the support of any servers, ISPs or authority controls. Keep in mind that it is a _physical network_, it isn't built upon any other existing net, therefore there must be computers linked _physically_ each other, then Netsukuku will build the routes., blogmarks.net
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YouTube - Visual Perception with Deep Learning 1 Citation A long-term goal of Machine Learning research is to solve highly
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., blogmarks.net
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