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Commit 80a0463

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Started working on latent graph learning
1 parent 21bf310 commit 80a0463

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6 files changed

+231
-19
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6 files changed

+231
-19
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‎FixedPointIterationsMethods.ipynb

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"cell_type": "code",
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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"version": "3.11.5"
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‎InformationTheoryOptimization.ipynb

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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true,
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}
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"source": [
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"### Cross Entropy\n",
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{
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"cell_type": "markdown",
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"metadata": {
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"source": [
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"### Mutual information\n",
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{
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"cell_type": "markdown",
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"metadata": {
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"jupyter": {
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}
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"source": [
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"# KL Divergence and maximum likelihood\n",
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true,
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}
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"source": [
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"### Solving the problem\n",
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{
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"cell_type": "markdown",
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"metadata": {
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"jupyter": {
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}
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"source": [
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"#### Expectation Maximization Algorithm\n",
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{
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"cell_type": "markdown",
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"metadata": {
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"jupyter": {
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"source": [
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"#### Conditional probability, sum of Poisson distibutions and binomial law\n",
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{
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"cell_type": "markdown",
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"metadata": {
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"source": [
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"#### Some remark about this Expectation Maximization instance\n",
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{
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"cell_type": "markdown",
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"metadata": {
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}
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"source": [
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"## Bayesian statistics, MCMC, and the magical world of probabilistic programming\n",
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"nbconvert_exporter": "python",
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"version": "3.11.5"
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‎LatentGraphLearning.ipynb

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‎OptimalTransportWasserteinDistance.ipynb

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"### Optimal transport and Sinkhorn divergence on graphs\n",
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"This chapter has been inspired by Nicolas Keriven tweet: https://twitter.com/n_keriven/status/1453276234637578253\n",
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"\n"
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"This chapter has been inspired by Nicolas Keriven tweet: https://twitter.com/n_keriven/status/1453276234637578253"
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"cell_type": "code",
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"outputs": [],
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"source": [
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"#IFrame(\"doc/OptimalTransportWasserteinDistance/LatentGraphLearning.pdf\", width=1200, height=800)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"And one of its source: \"A. L. Smith, D. M. Asta, and C. A. Calder. The geometry of continuous latent space models for network data\""
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]
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"cell_type": "code",
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"execution_count": 2,
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"#IFrame(\"doc/OptimalTransportWasserteinDistance/NKeriven_OTGraph.pdf\", width=1200, height=800)"
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‎RoddierWaveFrontReconstruction.ipynb

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"* Also [this](https://docs.sciml.ai/NeuralPDE/stable/) amazing website/github project that I also forked. You will also find a basic introduction to neural PDE, and why normalizing flows are useful for those type of models in the NormalizingFlows notebook.\n",
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"\n",
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"* Nvidi []()"
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"* Nvidia also release modulus, an opensource framework for physics informed PDEs introduced [here](https://developer.nvidia.com/blog/develop-physics-informed-machine-learning-models-with-graph-neural-networks/) and available [here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/modulus/containers/modulus)"
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