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If you read the paper itself[1] the random forest is only used in the final stage. The main approach is a convolutional variational autoencoder (which of course
by nl 4y ago
If you read the paper itself[1] the random forest is only used in the final stage. The main approach is a convolutional variational autoencoder (which of course is a deep learning model).
The VAE model itself is defined in step 6 in [2]
[1] https://www.nature.com/articles/s41550-022-01872-z.epdf?sharing_token=t6jjoqbFXFLJH8B5_RNzEtRgN0jAjWel9jnR3ZoTv0Mkq1U55F4UpwCyo9pvCV4lj--uzspzi_o3Nto3GrgPPPK7bN8GhKil2WvNSdFgUJmpmWo-kBOlWGQDS8nBDmrm5jSNwB_Db9767cFT2RRBBvupuVMql4JeV3b9Nn2FjQw%3D https://www.nature.com/articles/s41550-022-01872-z.epdf?shar...
[2] https://github.com/PetchMa/ML_GBT_SETI/blob/4096_pipeline/test_bench/VAE_NEW_ACCELERATED-BLPC1-8hz-1.ipynb https://github.com/PetchMa/ML_GBT_SETI/blob/4096_pipeline/te...
- nyrikki 4y agoAnd deep learning models have no understanding of the underlying data because they are either classification or regression.
- nl 4y agoWell the humans are doing classification too so I'm not sure what this is supposed to mean. It's true that this model doesn't know anything outside its training dataset, but that's a different objection. It's an interesting question as to if there is anything humans do that isn't just classification or regression (and of course regression is just classification over an infinite set where we assign labels to certain ranges and then select the highest probability density).
- mr_toad 4y agoFor certain definitions of understanding.