Example for top level skeleton for a research paper: https://arxiv.org/abs/2003.07064 

S: Stand alone high level story line (one bullet per motivation, each term is motivated)
 a)	Convolution is important in deep learning 
 b)	Convolution is an inductive prior, shift-equivariant/invariant: Object can appear anywhere
 c)	Inductive prior is data-efficient, 
 d)	Convolution is important because it is data-efficient.
 e)	Due to border effects, current CNNs are not shift equivariant/invariant
 f)	Alleviating border effects will benefit the inductive prior and thus increase data-efficiency.

T: Options for the paper title 
 a)	On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location
 b)	Convolutional Layers can Exploit Absolute Spatial Location
 c)	Border effects break CNN translation equivariance
 d)	CNNs without borders: Border effects break CNN translation equivariance

C: Contributions (try to have ~3)
 a)	Show that boundary effects allow location specific coding
 b)	Show that current popular existing CNNs already encode absolute location
 c)	Solution to boundary effects
 d)	Demonstrate increased data-efficiency on various architectures & tasks

R: Related work (one bullet point per topic)
 a)	Fully connected vs convolution
 b)	Location encoding will influence image cropping methods
 c)	Robustness to transformations 
 d)	Boundary effects 
 e)	Encoding location information in CNNs
 f)	Visual inductive priors for data efficiency

M: Goals for the method (Not what you do; but the goal of why you do)
 a)	Formalize boundary effect in convolution
 b)	List convolution-types for boundary handling
 c)	Show relation between convolution types and spatial location coding

E: Questions for the experiments 
(link back to: Related baselines R, Contributions C, Method M)
(Type 1: Does it do what you claim? Type 2: variations/baselines; Type 3: State of the art)
 a)	Type 1: Q: Can a fully convolutional network learn location? (Fig 1) (C.a)
 b)	Type 1: Q: How far from the boundary can current CNNs learn location? (C.a; C.b)
 c)	Type 2: Q: How much do convolution types exploit spatial location? (C.a; M.c)
 d)	Type 2: Q: How much does removing location coding increase robustness to image shifts? (C.b; C.c; R.c)
 e)	Type 3: Q: Without location, how much does data efficiency increase? (C.d)
On: Classification, matching, action recognition

L: Limitations and their answers (What will reviewers say; and what is the response)
 a)	L: Extra compute; A: just 15% slower
 b)	L: Sometimes location is useful; A: true, but do it external to convolution

Paper skeleton per paragraph 

Introduction

Paragraph goal: XXX
Keywords: A, B, C
Conclusion: 

Paragraph goal: XXX
Keywords: A, B, C
Conclusion: 

Paragraph goal: XXX
Keywords: A, B, C
Conclusion: 

Related work

Paragraph goal: XXX
Keywords: A, B, C
Conclusion: 1. We follow; or 2. We differ because XXX

Paragraph goal: XXX
Keywords: A, B, C
Conclusion: 1. We follow; or 2. We differ because XXX

Paragraph goal: XXX
Keywords: A, B, C
Conclusion: 1. We follow; or 2. We differ because XXX

Method

Paragraph goal: XXX
Keywords: A, B, C
Conclusion: 

Paragraph goal: XXX
Keywords: A, B, C
Conclusion: 

Paragraph goal: XXX
Keywords: A, B, C
Conclusion: 

Experiments

Paragraph goal: XXX
Keywords: A, B, C
Conclusion: 

Paragraph goal: XXX
Keywords: A, B, C
Conclusion: 

Paragraph goal: XXX
Keywords: A, B, C
Conclusion: 

Conclusions & Discussion

Paragraph goal: XXX
Keywords: A, B, C
Conclusion: 

Paragraph goal: XXX
Keywords: A, B, C
Conclusion: