Using my BSnet deep learnin network, each neuron is designed not to overfit. It achieves this by concatenating the positive and negative inputs so that it becomes more separable in high dimension space. This allows it to be used for general purpose classification problems such as MNIST dataset to recognize handwriting number digits. BSnet is based on the principles of Boolean algebra and monotone circuit. Using the same principles, I also design BSautonet autoencoder, that can be used to denoise image, learn embeddings and unsupervised learning.

1. A neuron that never overfits
An intuition of how my BSnet
works
Tan Sing Kuang

2. Background
● BSnet stands for Boolean Structured Deep Learning Network
○ Aka BullShit net, LOL
● It uses the principles of Boolean algebra and monotone circuit to design the
network
● The design is fully connected, but can also be applied to convolutional
network

3. Ordinary deep learning network example

4. In a normal scenario, a neuron of an
ordinary deep learning network acts like
a normal linear classifier.
The separation hyperplane (the line on
the left) classify the datapoints (the
circles on the left) if it lies on the green
side as green class, and likewise
classify the datapoints on the red side
as red class.
During the training process, the goal is
to find the optimal position and
orientation for the separation
hyperplane

5. The position and orientation of the
hyperplane is adjusted during gradient
descent.

6. Until the optimal position and
orientation of the hyperplane is found.

7. BSnet example

8. As for my BSnet neuron, it can classify
more complex scenario like this.

9. During gradient descent, the
hyperplane of the neuron is adjusted.

10. As the gradient descent progress, the
hyperplane will suddenly flip over to
separate the bigger cluster of green
datapoints against the red datapoints,
achieved a better global optimal.
This behavior prevents overfitting by
classifying most of the points correctly,
leaving the 2 green datapoints
classified wrongly due to noise.

11. This is due to the additional negated
dimensions that are input to the neuron,
making the classification problem more
separable.
This is due to the negated
operations in each layer.

12. A similar example is SVM
kernel that can separate
datapoints linearly by projecting
it into high dimensions space

13. My BSnet papers
BSnet
https://vixra.org/abs/2212.0193
https://github.com/singkuangtan/BSnet
BSautonet
https://vixra.org/abs/2212.0208
https://github.com/singkuangtan/BSautonet

14. About Me
● My job uses Machine Learning to solve problems
○ singkuangtan@gmail.com
○ Like my posts or slides in LinkedIn, Twitter or Slideshare
○ Follow me on LinkedIn
■ https://www.linkedin.com/in/sing-kuang-tan-b189279/
○ Follow me on Twitter
■ https://twitter.com/Tan_Sing_Kuang
■ https://mstdn.social/@singkuangtan
○ Send me comments through these links
● Look at my Slideshare slides
○ https://www.slideshare.net/SingKuangTan
■ Kung Fu Computer Science, Clique Problem: Step by Step
■ Beyond Shannon, Sipser and Razborov; Solve Clique Problem like an Electronic Engineer
■ A weird Soviet method to partially solve the Perebor Problems
■ 8 trends in Hang Seng Index
■ 4 types of Mathematical Proofs
■ How I prove NP vs P
○ Follow me on Slideshare

15. Share my links
● I am a Small Person with Big Dreams
○ Please help me to repost my links to other platforms so that I can spread my ideas to the rest of the world
● 我人小，但因梦想而伟大。
○ 请帮我的文件链接传发到其他平台，让我的思想能传遍天下。
● Comments? Send to singkuangtan@gmail.com
● Link to my paper NP vs P paper
○ https://www.slideshare.net/SingKuangTan/brief-np-vspexplain-249524831
○ Prove Np not equal P using Markov Random Field and Boolean Algebra Simplification
○ https://vixra.org/abs/2105.0181
○ https://vixra.org/author/sing_kuang_tan
○ Other link
■ https://www.slideshare.net/SingKuangTan