Bold links to examples
@@ -7,7 +7,7 @@ Internally, GPyTorch differs from many existing approaches to GP inference by pe
GPyTorch provides (1) significant GPU acceleration (through MVM based inference); (2) state-of-the-art implementations of the latest algorithmic advances for scalability and flexibility ([SKI/KISS-GP](http://proceedings.mlr.press/v37/wilson15.pdf), [stochastic Lanczos expansions](https://arxiv.org/abs/1711.03481), [LOVE](https://arxiv.org/pdf/1803.06058.pdf), [SKIP](https://arxiv.org/pdf/1802.08903.pdf), [stochastic variational](https://arxiv.org/pdf/1611.00336.pdf) [deep kernel learning](http://proceedings.mlr.press/v51/wilson16.pdf), ...); (3) easy integration with deep learning frameworks.
-Right now, the package is in alpha release, and while we believe that the interface is reasonably stable, things may change. For now, see our numerous [examples and tutorials](http://github.com/cornellius-gp/gpytorch/blob/master/examples) on how to construct all sorts of models in GPyTorch. These example notebooks and a walk through of GPyTorch are also available at our readthedocs page [here](https://gpytorch.readthedocs.io/en/latest/index.html)
+Right now, the package is in alpha release, and while we believe that the interface is reasonably stable, things may change. For now, see our numerous [**examples and tutorials**](http://github.com/cornellius-gp/gpytorch/blob/master/examples) on how to construct all sorts of models in GPyTorch. These example notebooks and a walk through of GPyTorch are also available at our **ReadTheDocs page [here](https://gpytorch.readthedocs.io/en/latest/index.html)**
If you use GPyTorch, please cite the following papers:
> [Gardner, Jacob R., Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, and Andrew Gordon Wilson. "Product Kernel Interpolation for Scalable Gaussian Processes." In *AISTATS* (2018).](https://arxiv.org/abs/1802.08903)
