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#### Description
I was wondering if there was interest in adding a new imputation strategy (or a new Imputer class) based on a Gaussian Mixture Model (GMM) using the EM or CEM algorithm. The …
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Please post here interesting papers, possibly with a brief description of technique and results (improvement).
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Graph generative models are important for the tasks we have been describing.
The core idea is to posit a model which defines some distribution over graphs ```P(G)```, for instance via a low dimensi…
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Enabling Predictive Analytics In kabaddiPy Module There is a feature where people can predict and based on historical data whether any team can win the Kabaddi match or not.
**Key Features:**
- …
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Suppose I got a new dataset in the mail today & wanna see which brand-name distribution in [Distributions.jl](https://github.com/JuliaStats/Distributions.jl/) best fits it.
```julia
using Distribut…
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### Is your proposal related to a problem?
There are many circumstances in which we have some prior knowledge about matches in or between datasets. It is difficult to express this knowledge in the …
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After reading your paper, I am very curious about how the Inverse network achieves the one-to-many property-structure mapping. I believe that once the network is trained, the parameters are fixed, and…
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We need to convert keras.io examples to work with Keras 3.
This involves two stages:
## Stage 1: tf.keras backwards compatibility check
Keras 3 is intended as a drop-in replacement for tf.ker…
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## Prequest
- https://deepmind.com/learning-resources/-introduction-reinforcement-l…
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I have a small time series dataset of size 500x10 (500 time steps, 10 features). I want to make predictions several time steps into the future conditioned on the first 4 features. For example, predict…