Comments (2)
Outside of small-batch high-learning rate scenarios, this seems relatively compatible with what has previously observed:
http://discovery.dartmouth.edu/~cgreene/da-psb2015/parameter_sweep_net100.pdf
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Outside of small-batch high-learning rate scenarios, this seems relatively compatible with what has previously observed:
interesting...perhaps this is accurate then. Thanks for sending the link @cgreene !
I was also able to find a figure (I created it in July 2015) running ADAGE using the https://github.com/greenelab/adage implementation (I believe it was on bitbucket at the time). This was run on a previous version of pancanatlas data (RTCGAToolbox
data freeze version 20150402)
The figure for the psb2015 paper was run using only breast cancer data correct? It looks quite similar to the pancancer results too. I am no longer too concerned about noise = 0
but it will still be good to match implementations
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Related Issues (20)
- Simulation Experiments HOT 2
- Keras versioning error HOT 3
- Add R packages to environment.yml HOT 2
- Reproducing separation HOT 3
- Reorganize repository
- Replace data in encoded_adage_features.tsv HOT 1
- Something wrong in extracting weights? HOT 3
- Sampling space for specific genes HOT 4
- Zero'd out training HOT 3
- Sampling distriubtions HOT 7
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- t-SNT visualization HOT 2
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- Error when setting up environment HOT 7
- Passing list-likes to .loc or [] with any missing label will raise KeyError in the future, you can use .reindex() as an alternative. HOT 2
- Modify Tybalt to handle missing values for incomplete data HOT 4
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