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1
Intro
2
Semantic Map Filling
3
Problem Definition
4
Architecture Overview
5
Wear Module
6
Neural Network
7
Mode Collapse
8
Auto Encoder
9
Real Images
10
Animation
11
Neural Network Results
12
Copy and Paste
13
Understanding complex images
14
Images as a graph
15
Good enough graph
16
Structure prediction
17
Architecture
18
Graph permutation invariant
19
Synthetic example
20
Visual Genome
21
Visual Genome Example
22
Summary
Description:
Explore cutting-edge applications of deep learning in speech recognition and collaborative filtering systems in this session from the NVIDIA AI Tech Workshop at NeurIPS Expo 2018. Delve into the intricacies of building speech recognition models using synthetic speech and optimizing neural collaborative filtering systems. Gain insights into semantic map filling, problem definition, and architecture overview. Examine various modules including wear module, neural networks, and auto encoders. Discover techniques for handling mode collapse and processing real images. Learn about complex image understanding, graph-based image representation, and structure prediction. Investigate graph permutation invariant architectures and their applications in synthetic examples and Visual Genome datasets. Enhance your knowledge of applied deep learning techniques presented by NVIDIA Research experts in this comprehensive 39-minute session.

Applied Deep Learning in Speech Recognition and Collaborative Filtering - Session 5

Nvidia
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