Final project for Machine Learning course @NSYSU @Taiwan

1. Anomoly Detection
Using Hierarchical Temporal Memory and Grid Cells

2. The Problem
• The circle on the right is
revolving in a circle.
• Detect any anomaly in the path
the circle takes

3. Hierarchical Temporal Memory
• For our purpose.
• An algorithm learns to predict xt+1 from xt
• Accepts sparse binary tensors as input
• a.k.a Sparse Distributed Representation (SDR)

4. SDR and Encoders
• So, a SDR looks like this

5. SDR and Encoders
• So, a SDR looks like this
• How do we construct one

6. SDR and Encoders
• So, a SDR looks like this
• How do we convert
integer/numbers into one

7. ScalarEncoder
• The simplest encoder
• Example: Encode scalars between 0 and 100
with 400 bits.
• SDR on the right represents 54

8. ScalarEncoder
• The simplest encoder
• Example: Encode scalars between 0 and 100
with 400 bits.
• SDR on the right represents 54
• One SDR can represent may values
• What happens if > 100 or < 0
• Encode as 0 or 100

9. ScalarEncoder
• We are NOT using it here

10. Grid Cells
• State of the art (for real)
• ScalarEncoder suc*s
• GeoSpatialEncoder is old

11. Grid Cells
• A grid cell fires in a grid like pattern
• With multiple grid cell units. A unique location can be represented

12. Grid Cells
• Implementation:
• ~50 lines of C++
• And 2D vector math
• Won't board you with the details
• Encoding a 2D coord using such
algorithm

13. Grid Cells

14. Demo
• The circle is the target
• Location encoded with grid cells
• Shows anomaly signal when moved
out of ordinary path