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We have methods for associating individual genetic variants and phenotypes (https://github.com/pystatgen/sgkit/pull/16, https://github.com/pystatgen/sgkit/pull/66), but inferring associations with gen…
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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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After initializing a keras model:
``` ruby
def build_keras_regressor_model():
model = keras.Sequential([
layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),
la…
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This outlines a roadmap for basic statistical functionality that Julia needs to offer. It is heavily drawn from the table of contents for MASS.
- [ ] Data processing [DataFrames.jl](https://github.com…
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## Overview
Currently, we use a deterministic SIR model (see `sir` and `sim_sir` in [models.py](https://github.com/CodeForPhilly/chime/blob/develop/penn_chime/models.py)) to predict everything. It …
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I'm implementing a BayesianLinear layer implemented like this:
`self.dense = BayesianLinear(opt.hidden_dim, opt.polarities_dim, freeze = False)`
my loss from model.sample_elbo() returns "inf", and…
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# 해결하고자 하는 문제
https://github.com/codingeverybody/codingyahac/issues/1021
이 질문과 이어지는 질문입니다.
머신러닝이 변수를 만들어 준다고 해도,
히든 레이어의 개수는 몇 개가 제일 적당한지는 인간이 알아내야 하잖아요.
무조건 히든 레이어를 많이 넣는다고 해도 정확한 값을 머신러닝이 예…
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```ErgonomicFugitive```> What you're asking for is bayesian probability analysis, though that's always going to be highly approximate when you don't have access to the data sets themselves. Short of t…
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### Metadata
- Authors: Christopher P. Burgess, Irina Higgins, +4 authors Alexander Lerchner
- Organization: DeepMind
- Publish Date: 2018.04
- Paper: https://arxiv.org/pdf/1804.03599.pdf
- 3rd-p…
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## Project info
**Title:**
Sharing information across voxels with Bayesian hierarchical modelling to improve brain microstructure mapping
**Project lead:**
Paddy Slator (email: p.slat…