# Lecture - Theories of Deep Learning MT25, XVI, Ingredients for a successful mini-project report

> Source: https://ollybritton.com/notes/uni/part-c/mt25/theories-of-deep-learning/lectures/report/ · Updated: 2025-12-10 · Tags: uni, lecture

- [Course - Theories of Deep Learning MT25](https://ollybritton.com/notes/uni/part-c/mt25/theories-of-deep-learning/)
> 
> - Report should include:
> 	- A discussion of some theoretical portion of deep learning along
> 	- Numerical simulations
> - Don't write about an application (e.g. this method gets 2% better), this project is about the theory of deep learning
> 	- But you can highlight how an application raises questions about deep nets, and how the theory of deep learning could be used to overcome these problems
> - One approach:
> 	- Pick two papers that came out at the same time and then compare them
> 	- Combine the two approaches
> - You're not expected to do conference-level research but there should be some aspect of originality. The examiners should hear your voice
> 	- E.g. if you're comparing two papers, your report shouldn't be useless if the examiner has read the two papers you have compared
> 
> - Pick a topic that you are excited about
> 	- It should read like a 20 page report that's been compressed into a 5 page report
> - Adapt your code from others 
> 	- Pick papers that have code already to build on
> - Don't just focus on one paper
> 	- Compare different aspects of multiple papers
> 	- Look at papers that came out at the same time and haven't been directly compared
> - Work out what you don't like when reading a paper, and don't do that
> - Clearly state what is new, be upfront and don't present other results as your own
> 	- It can be jarring to say "I did X" but it makes the examiner's life much easier
> - Don't pick papers that are very close to papers presented in the lectures
> - You can pick older papers but don't go back more than about 10 years
> - You can copy figures from other papers but don't make all your figures other people's
> 
> - Examples
> 	- Robustness and accuracy: are we trying to have our cake and eat it too?
> 		- Nice bibliography
> 		- A nice mathematical tone
> 		- Novel experiment
> 		- Didn't have a complete answer in the end
> 	- On manifold mixup for deep learning
> 		- Had a really good summary of the topic and the bibliography
> 		- Lots of originality, above what is expected
> 		- The examiner couldn't find the new content anywhere else, it was genuinely new
> 	- Backpropagation and predictive coding: an experimental comparison
> 		- Contrasting
> 		- Ex
> 	- What were these papers missing to be exceptional?
> 		- Sometimes the lecturer gets 
> 		- Great literature review
> 		- Really good experiments
> 		- With more page length it seems like it could be amazing
> 
> - Start with an outline
> - Fill things in
> - Go over the page length
> - Then condense by selecting the most essential parts of the discussion
> - Re-read and improve your report
> 
> - A literature review (done well) would get you a 60
> - Putting some of yourself into the report would improve your score
> - The more originality, the higher your score (roughly)
> 
> - Don't generally recommend e.g. modifying a proof and doing lots of math, better to do numerical experiments instead

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