The document discusses how Google uses graph-powered machine learning. It describes using graphs to combine labeled and unlabeled data using semi-supervised learning. The graphs represent relationships between objects/concepts as nodes connected by edges. This allows combining different data types like images, text, and messages into a single representation. The approach is used in Google products for applications such as image recognition, reminders, messaging, and more.

1. Graph-powered Machine
Learning
@ Google Research Blog
Mindos

2. image recognition system
in Google Photos
Reminders
in inbox
Smart Messaging
in Allo

3. Semi-supervised Learning
Training with labeled & unlabeled data at the
same time.
Easily combined multiple types of signals into
single graph representation.

4. Graph
Node : objects or concepts.
edges : connecting concepts that share
similarities.

5. Node
labeled data : node with label on it.
unlabeled data : node without label on it.

6. Advantage
We have observed that it is often beneficial to
learn over multi-graphs that combine
information from multiple types of data
representations (e.g., image pixels, object
categories and chat response messages for
PhotoReply in Allo)

7. Toy Example
predict a color (“red” or “blue”)

8. Examples
predict a color (“red” or “blue”)

9. Examples
predict a color (“red” or “blue”)

10. Real Practice
Complicated update function

11. Real Challenge
billions of nodes
trillions of edges
for complex tasks involving billions of
different label types.

12. Examples for Complex Problem
natural language understanding
machine perception
user modeling
joint multimodal learning for tasks involving
multiple modalities such as text, image and
video inputs

13. Language Graphs
for Learning Humor
emotion labeling
a language understanding task in Smart Reply
for Inbox
Goal
to label words occurring in natural language
text with their fine-grained emotion categories.

14. Word Embedding Learned
from A Neural Network Model

15. to build a sparse graph
Nodes correspond to words.
Edges represent semantic relationship
between them.
Edge strength is computed using
similarity between embedding vectors
(low similarity edges are ignored)

16. Seeding & Iteration

17. How to deal with large dataset or
dense representation?
by leveraging approximate, linear-time graph
construction algorithms.

18. Graph-based Machine Intelligence
in Action
on massive graphs (containing billions of nodes and
trillions of edges)
to recognize and understand concepts in natural
language, images, videos, and queries.
powering Google products for applications like
reminders, question answering, language translation,
visual object recognition, dialogue understanding, and
more.

19. Typical Product