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I'm stumbling a dead wall on how to optimize a scikit model/models based on multiple metric(accuracy, logloss and kappa) using hyperopt. I've be looking for an example or something that may help so I …
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Hello,
is it possible to calculate the marginal log likelihood given the observations and the model parameters? I want to do thinks similar to this tutorial from PINTS with statsmodesl in python: h…
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Hello,
I'm working with a very large dataset consisting of 7.5 million rows and 18 columns, which represents customer purchase behavior. I initially used UMAP for dimensionality reduction and attem…
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Hi, thank you so much for developing such a nice framework!
I'm new to model training and I'm trying to train the model on a dataset with about 6000 vocalizations, consider there are around 10 types…
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**Describe the bug**
I want to optimize the hyperparameters of a simple pipeline. According to the [MLJ docs](https://alan-turing-institute.github.io/MLJ.jl/dev/tuning_models/#Tuning-multiple-neste…
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Hi,
Could you please provide the hyperparameters for the reproduction of the results? I have run your train.sh, however, I cannot reproduce the unconditional generation results. Beside, different s…
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Dear author, I have two questions to ask you. The first question is, when I read the code, I found that you used test data to select hyperparameters in both validation and fine-tuning methods. Can tes…
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What hyperparameters do you use for training the best-performing VGG+BN+dropout model?
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Have you conducted any experiments on DanceTrack? If so, how should hyperparameters, such as lambda_iou, lambda_mhd, and lambda_shape, be set for DanceTrack?
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1. How do You initialize the weight matrices?
2. Are You sure that You set lambda to be 10^9 everywhere?
Bests,
Benedek