Comments (2)
amritbhanu
commented on July 20, 2024
RQ1: Does tuning improve the stability scores of LDA?
- Show it with a table.
RQ2: Does different data need different configurations to make LDA stable?
- Parameter ranges, boxplot with different datasets
RQ3: Does tuning changes some predefined notions of topic modelling?
- Like topic sizes with citemap dataset.
RQ5: Is tuning impractically slow?
- No of evaluations
RQ6: Does terms remain constant in a topic over different runs?
- heatmap
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amritbhanu
commented on July 20, 2024
RQ 1: Is it a quirk of the implementation?
Explain how the likelihood is measured in LDA?
Algorithm = untuned
Stability vs evaluation = let see for stack citemap
Stats on tha AUC
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Related Issues (20)
- DE results on pitsA
- Review - 04/27/2016 HOT 1
- Results HOT 2
- Updated ToDos
- Citemap Results HOT 5
- Meeting - 06/02
- Meeting - 06/16 HOT 2
- Results 06-23
- Parameter alpha and beta
- Classification using LDA HOT 8
- F CR Pop Graph
- Spark Results
- VEM vs Gibbs
- terms overlap HOT 2
- LDA topics as feature selector HOT 1
- Mail with Prof. Mika Mäntylä
- Randomness
- Credibility Of LDA HOT 4
- Weekly Report - 10/11/2016 HOT 3
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