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投稿者 googletechtalks 投稿日時 08/04/10 18:11 表示回数 8590 再生時間 57:25 お気に入りリストに追加 - YouTubeプレイヤーで表示 - YouTube.comで直接表示(高画質版) |
Deepに関連してそうなまとめサイトとか、関連してないけど最新のまとめとか： たけのこの里＜＜＜＜＜＜＜＜＜＜きのこの山（笑）(カナ速) ヘアワックスは何で選ぶ？ “香り”が7割(【2ch】ニュー速クオリティ) ＤＱＮとオタクの差(アルファルファモザイク) 天ぷらの中で至高なのは？(カナ速) 姉ちゃんの独り言ＩＮトイレ(アルファルファモザイク) オヌヌメの電波ソング教えて(カナ速) 仕事が出来ない人(【2ch】ニュー速クオリティ) |
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関連タグ： google techtalks techtalk engedu talk talks googletechtalks education 
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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. Deepの新着動画 |
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平均: 4.70 点
投稿者:googletechtalks 投稿日時: 08/04/10 18:11 表示回数: 8547 再生時間: 57:25 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 percepti... |
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