Comments (3)
Hello @andreipit,
I am no expert in reinforcement learning but I don't think tabnet applies here, I don't think anyone applied tabnet to reinforcement learning yet.
As for the self supervised learning part, the original paper is mentioning it and gives some results. This feature will probably be added in the future as I think it's an interesting idea for small datastes.
from tabnet.
Hi, can you add self-supervised learning function? I think it's useful as the train data usually contains missing value.
from tabnet.
Self supervision could be used for nan imputing but I think itβs really a side use of self-supervised pre-training.
But I agree that it would be an interesting feature, need to find some time to do this.
from tabnet.
Related Issues (20)
- Current version on conda-forge is 4.0 while 4.1 is already released HOT 8
- Minimal working example for TabNetRegressor/Classifier HOT 4
- Transfer learning, capability to change structure of model HOT 1
- Generate Embeddings for Tabular Data HOT 1
- TabNet overfits (help wanted, not a bug) HOT 9
- TabNetRegressor vs other networks HOT 1
- spike in memory when training ends HOT 8
- Severe overfitting HOT 18
- OOM problem when I search hyperparameters with Tabnet HOT 3
- Support for complex-valued datasets HOT 4
- Different classification variables in the test set and train set HOT 1
- Struggling to get model to fit - Help Wanted HOT 7
- Optimizing TabNet for Disease Classification with Continuous Audio Features HOT 1
- Interpreting Sparsity on Global Importance HOT 5
- ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() HOT 1
- Validation loss HOT 1
- Lightweight Fine-tunning or few-shot learning for limited labeled data HOT 1
- Maybe `drop_last` should be set as False in default? HOT 1
- Incompatiblity of current round() method with pytorch tensors when performing early stopping HOT 1
- Retraining a saved model on different dataset HOT 3
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