Obsidian/4 Qualifying Exam/2 Writing/3. QE Research Approach.md

title	allDay	date	completed	type	endDate
Research Approach	true	2024-10-10		single	2024-10-22

5 Pages TARGET: 2800-3000 words

Outline

1. What are unperturbed perturbations and why are they important?
2. What is a generative diffusion model?
3. Where have generative diffusion models been used?
4. How will generative diffusion models generate new plants?
5. How can we know if these new plants are in the set of 'controllable' plants?
6. How are we going to get training data?

Something to justify, why diffusion model as opposed to other generative AI

First Draft

Story

1. Generating unstructured perturbations is pretty hard
2. They need to be 'random'
3. diffusion model is good at creating novel examples (cite!)
4. we can use diffusion models to solve this problem of generating examples
5. But how does a diffusion model work?
6. Diffusion model uses two processes
7. a forward process that introduces noise into an 'input'
8. this forward process does this using several small steps of noise
9. These are called timesteps
10. This noise is a gaussian noise
11. over time this forward process degrades the input until it is unrecognizable
12. This takes several several iterations, but is dependent on the amount of noise in each step
13. The markov chain that this creates is also gaussian at every step, until the noise at the end of the day is some gaussian distribution. Usually mean 0 std. dev beta
14. A reverse process that tries to remove the noise
15. But if we destroy the input how can we do this?
16. Well we train a neural network as a denoiser.
17. Because the diffusion model forward steps are small and gaussian, we can know the reverse step is also a gaussian distribution.
18. So for our neural network, what we're trying to learn is the mean and standard deviation of the reverse steps for a given timestep.
19. What does this mean for

Writin some stuff

The purpose of this proposal is to suggest that using a generative network to create unstructured perturbations can be a viable way to advance the state of the art. But to do this, the current state of diffusion models and their place must be introduced. The generative diffusion model is a recent breakthrough in generative models. Diffusion models