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Giulio Alessandrini
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Hi Ethan, to answer your questions 1. We are working on a stable diffusion model for the [net repository][1]. 2. Compression tools for models are nice but right now the core priority is to provide support for multiple framework in order to get...
Hello there! Tomorrow (16 November 2022) I am going to demo our Machine Learning functionality and the latest additions and developement directions. We'll look at the the full ML stack including a focus on the current efforts in model...
Using the same dataset ``` data = ResourceData["Sample Data: Titanic Survival"]; titanic = Classify[data -> "SurvivalStatus"] ``` after training you can extract the estimated data distribution using `Information` ``` dist =...
What you are looking for seems to be ``` AggregationLayer[Max] ``` which will take `[channels, height, width]` and return `[channels]` using `Max` to aggregate the other dimensions.
This network is solving a regression task and has no error rate. Building a classification network which automatically uses a cross entropy loss (e.g. which ends with a logistic sigmoid or a softmax layer) will automatically add the error rate among...
Hi Gianluca and thanks for sharing this! One question: why do you map `NetTrain` instead of of using `MaxTrainingRounds`? Do you want to reset the learning rate?
At the moment the clustering metrics are all internal and used to optimize hyper-parameters. We have a plan to expose them and if there is some interest all the better. For the time being, and keeping in mind that is code might change in the...
I believe this is due to some overzealous standardization step in the automated processing pipeline (boolean vectors are converted to numerical vectors for processing). You can disable that using the "Minimal" feature extraction: data = {True,...
I am not sure I understand your problem correctly. If what you are asking is how to get the prediction for specific values it is just ``` result[{200, 210, 215, 235}] (* {82.839, 82.8391, 82.8392, 82.8393} *) ``` By the way, there are ways...
Just a note, `ResizeLayer` has been updated to work on arbitrary rank ResizeLayer[{1, 2, 3, 4, 5}][RandomReal[1, {3, 2, 2, 2, 2, 2}]] // Dimensions (* {3, 1, 2, 3, 4, 5} *)