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intsystems · Nov 26, 2024 · 6e2599f · 6e2599f
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 ## 🗃 Algorithms
-- [x] [Relaxed Bernoulli](http://proceedings.mlr.press/v119/yamada20a/yamada20a.pdf) 
-- [x] [Correlated relaxed Bernoulli](https://openreview.net/pdf?id=oDFvtxzPOx)
-- [x] [Gumbel-softmax TOP-K](https://arxiv.org/pdf/1903.06059) 
-- [x] [Straight-Through Bernoulli](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=62c76ca0b2790c34e85ba1cce09d47be317c7235) 
-- [x] [Invertible Gaussian](https://arxiv.org/abs/1912.09588) with KL implemented
-- [x] [Hard Concrete](https://arxiv.org/pdf/1712.01312) 
-- [x] [REINFORCE](http://www.cs.toronto.edu/~tingwuwang/REINFORCE.pdf)
-- [x] [Logit-Normal](https://en.wikipedia.org/wiki/Logit-normal_distribution) and [Laplace-form approximation of Dirichlet](https://stats.stackexchange.com/questions/535560/approximating-the-logit-normal-by-dirichlet) 
+- [x] [Relaxed Bernoulli](https://github.com/intsystems/discrete-variables-relaxation/blob/main/src/relaxit/distributions/RelaxedBernoulli.py), also see [📝 paper](http://proceedings.mlr.press/v119/yamada20a/yamada20a.pdf) 
+- [x] [Correlated relaxed Bernoulli](https://github.com/intsystems/discrete-variables-relaxation/blob/main/src/relaxit/distributions/CorrelatedRelaxedBernoulli.py), also see [📝 paper](https://openreview.net/pdf?id=oDFvtxzPOx)
+- [x] [Gumbel-softmax TOP-K](https://github.com/intsystems/discrete-variables-relaxation/blob/main/src/relaxit/distributions/GumbelSoftmaxTopK.py), also see [📝 paper](https://arxiv.org/pdf/1903.06059) 
+- [x] [Straight-Through Bernoulli](https://github.com/intsystems/discrete-variables-relaxation/blob/main/src/relaxit/distributions/StraightThroughBernoulli.py), also see [📝 paper](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=62c76ca0b2790c34e85ba1cce09d47be317c7235) 
+- [x] [Invertible Gaussian](https://github.com/intsystems/discrete-variables-relaxation/blob/main/src/relaxit/distributions/InvertibleGaussian.py) with [KL implemented](https://github.com/intsystems/discrete-variables-relaxation/blob/f398ebbbac703582de392bc33d89b55c6c99ea68/src/relaxit/distributions/kl.py#L7), also see [📝 paper](https://arxiv.org/abs/1912.09588)
+- [x] [Hard Concrete](https://github.com/intsystems/discrete-variables-relaxation/blob/main/src/relaxit/distributions/HardConcrete.py), also see [📝 paper](https://arxiv.org/pdf/1712.01312) 
+- [x] [REINFORCE](https://github.com/intsystems/discrete-variables-relaxation/blob/main/src/relaxit/distributions/CorrelatedRelaxedBernoulli.py), also see [📺 slides](http://www.cs.toronto.edu/~tingwuwang/REINFORCE.pdf)
+- [x] [Logit-Normal](https://github.com/intsystems/discrete-variables-relaxation/blob/main/src/relaxit/distributions/LogisticNormalSoftmax.py) and [Laplace-form approximation of Dirichlet](https://github.com/intsystems/discrete-variables-relaxation/blob/main/src/relaxit/distributions/approx.py), also see [ℹ️ wiki](https://en.wikipedia.org/wiki/Logit-normal_distribution) and [💻 stackexchange](https://stats.stackexchange.com/questions/535560/approximating-the-logit-normal-by-dirichlet) 
 
 ## 🎮 Demo
 For demonstration purposes, we have implemented a simple VAE with discrete latents. Our code is available [here](https://github.com/intsystems/discrete-variables-relaxation/tree/main/demo).