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1
Intro
2
Motivations
3
Google Image Search
4
Language Processing
5
Analogy Learning
6
Computational bottleneck
7
Sampling methods
8
Object recognition
9
MIT Technology Review 2013
10
Speech Recognition
11
Computer Vision
12
Unsupervised Learning
13
Free Trading Trick
14
What is a good representation
15
Priors
16
Improvised Learning
17
Fundamental Problems
18
First Problem
19
Experiments
20
Dependency Nets
21
Default Machine
22
Default Machine Results
23
Noise
Description:
Explore the cutting-edge field of deep learning and generative models in this comprehensive lecture by renowned AI researcher Yoshua Bengio. Delve into key concepts such as unsupervised learning, object recognition, and language processing. Discover how these techniques are revolutionizing areas like computer vision, speech recognition, and image search. Examine computational bottlenecks, sampling methods, and the importance of good representations in AI systems. Learn about innovative approaches like analogy learning, the free trading trick, and dependency nets. Investigate fundamental problems in the field and their potential solutions, including experiments with default machines. Gain insights into the future of AI and its applications across various domains in this informative 71-minute talk from the Center for Language & Speech Processing at Johns Hopkins University.

Deep Learning of Generative Models - 2014

Center for Language & Speech Processing(CLSP), JHU
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