From c690d83f68dc05a43df7ff9f32be22caa561f0bd Mon Sep 17 00:00:00 2001 From: Luke Wood Date: Tue, 7 Jun 2022 10:10:26 -0700 Subject: [PATCH] Remove broken links --- README.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/README.md b/README.md index 1346b67241..e1c5a3e129 100644 --- a/README.md +++ b/README.md @@ -87,15 +87,11 @@ for end-to-end examples. It includes tutorial notebooks such as: It also includes example scripts such as: -* [Variational Autoencoders](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/jupyter_notebooks/Probabilistic_Layers_VAE.ipynb). Representation learning with a latent code and variational inference. * [Vector-Quantized Autoencoder](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/vq_vae.py). Discrete representation learning with vector quantization. * [Disentangled Sequential Variational Autoencoder](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/disentangled_vae.py) Disentangled representation learning over sequences with variational inference. -* Latent Dirichlet Allocation - ([Distributions version](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/latent_dirichlet_allocation_distributions.py), - Mixed membership modeling for capturing topics in a document. * [Bayesian Neural Networks](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/bayesian_neural_network.py). Neural networks with uncertainty over their weights. * [Bayesian Logistic Regression](https://github.com/tensorflow/probability/tree/main/tensorflow_probability/examples/logistic_regression.py).