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Safe and Efficient Model-free Adaptive Control via Bayesian Optimization. (arXiv:2101.07825v1 [eess.SY])
https://ift.tt/2LItvZi
Adaptive control approaches yield high-performance controllers when a pr…
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I am still exploring the possibilities of GuildAI. How well does it integrate with other libraries to perform hyperparameter optimization beyond grid search and random search? For example can it int…
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Develop and test models for one of the AI Institute CTF challenges!
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Steps to final first draft
- replicate fig1 in diclimente2018sequences
- If we use tag_spend as main var, use PCA as category controls
- Understand entropy differences
- Typical behaviou…
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Is there any inference method associated with Noisy-Or Models? I have a Bayesian network model, but each child generally has lots of parents, but I have few data to train that many parameters, so I'd …
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These two lines
```
import warnings
warnings.filterwarnings("ignore")
```
in `methods/bayesian_optimization.py` mean that any code that imports GPyOpt does not show any warnings. This is not good…
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I am getting the Error message on win10 bash, when i am trying:
python3.6 japonicus.py -b --repeat 7 --strat RSI_BULL_BEAR
```
The profits reported here depends on backtest interpreter function;…
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Should let the user specify what kind of distribution the data should be assumed to be coming from.
So for Bayesian Learning we can have the API like:
```python
from pgmpy.models import BayesianMod…
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What the easiest way (in TFP) to convert a Bayesian neural network to a standard neural network?
More precisely, I would like to build a standard neural network `S` where the weights of layer `l` a…
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Hi, I have read the part about the uncertainty quantification of neural networks, and I'm confused about the log-likelihood function you mentioned, i.e. -sum((y[:,1] - obs[:,1]).^2)/2σ^2 - sum(x.^2)/2…