84 lines
4.3 KiB
Markdown
84 lines
4.3 KiB
Markdown
# Presentation Structure
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## List of all content
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<!--
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- (4) Background: Motivation
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- (5) Background: Previous Work
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- (6) Background: Presumptions & Notation
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- (7) Background: Optimization vs graph based methods
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- (9-11) Proximal: Theory
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- (12) Proximal: Sim: Able to replicate literature
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- (13) Proximal: Sim: Choice of gamma
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- (14) Proximal: Sim: FER due to DFR
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- (15-16) Proximal: Oscillation of estimate
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- (17) Proximal: Improved: Emprirical FoM
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- (18) Proximal: Improved: Algorithm
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- (19) Proximal: Improved: Results
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- (21-24) LP Decoding: General Theory
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- (25-26) LP Decoding: ADMM Theory
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- (27) LP Decoding: ADMM Sim: Able to replicate literature
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- (28-29) LP Decoding: ADMM Sim: Choice of mu and rho
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- (31) Comparison: Number of required iterations
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- (32) Comparison: Time Complexity
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- (33) Comparison: FER and BER
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- (34) Comparison: FER and BER for more codes
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- (35) Comparison: Algorithm structure
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- (36) Comparison: Algorithm structure: Message passing
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- (38) Conclusion and outlook
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- (41) Supplementary slides: Choice of gamma for more codes
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- (42) Supplementary slides: Choice of mu and rho for more codes
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-->
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| Slide | Content | TODO |
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| ----- | ---------------------------------------------------------- | :--: |
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| 4 | Background: Motivation | - |
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| 5 | Background: Previous Work | X |
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| 6 | Background: Presumptions & Notation | - |
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| 7 | Background: Optimization vs graph based methods | - |
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| 9-11 | Proximal: Theory | - |
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| 12 | Proximal: Sim: Able to replicate literature | - |
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| 13 | Proximal: Sim: Choice of gamma | X |
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| 14 | Proximal: Sim: FER due to DFR | - |
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| 15-16 | Proximal: Oscillation of estimate | - |
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| 17 | Proximal: Improved: Emprirical FoM | - |
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| 18 | Proximal: Improved: Algorithm | - |
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| 19 | Proximal: Improved: Results | - |
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| 21-24 | LP Decoding: General Theory | - |
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| 25-26 | LP Decoding: ADMM Theory | - |
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| 27 | LP Decoding: ADMM Sim: Able to replicate literature | - |
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| 28-29 | LP Decoding: ADMM Sim: Choice of mu and rho | S |
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| 31 | Comparison: Number of required iterations | S |
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| 32 | Comparison: Time Complexity | S |
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| 33 | Comparison: FER and BER | S |
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| 34 | Comparison: FER and BER for more codes | S |
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| 35 | Comparison: Algorithm structure | - |
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| 36 | Comparison: Algorithm structure: Message passing | X |
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| 38 | Conclusion and outlook | R |
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| 41 | Supplementary slides: Choice of gamma for more codes | - |
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| 42 | Supplementary slides: Choice of mu and rho for more codes | - |
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| - | Current and new approaches for improving proximal decoging | TODO |
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## Questions
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- Is a notational slide that important in this case?
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- Yes, otherwise the listeners won't understand the following slides
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- Should LP Decoding and ADMM even be mentioned?
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- Yes, the basis for the presentation is still the thesis. Also, the title of
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the presentation is "-- optimization --", not "-- proximal decoding --"
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- Mention approaches not taken (e.g., Adam for proximal decoding)?
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- Yes: "Zur Debatte stellen"
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- Present current state of research on improved algorithm (e.g., questions
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raised in last meeting with Jonathan)?
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- Yes, at least partly mention current approaches and insights not present in
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the thesis
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- Compare proximal and LP decoding using ADMM?
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- Maybe at least mention that the general structure of the objective function
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is similar and why we would expect proximal decoding to work better and why
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it's surprising it doesn't.
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- Present insights on parameter choice?
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- Maybe only in supplementary slides?
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- Corroborate results with slides showing additional simulations for multiple
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codes?
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- Maybe only in supplementary slides?
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