diff --git a/docs/core.rst b/docs/core.rst
index 195a13ef..2b082aae 100644
--- a/docs/core.rst
+++ b/docs/core.rst
@@ -5,8 +5,10 @@ ott.core package
 .. automodule:: ott.core
 
 The core package contains the classic Sinkhorn routine, along with a
-generalization of that algorithm that can be used to compute barycenters [#]_ [#]_.
-We also provide a Gromov-Wasserstein solver, which builds itself on top of Sinkhorn.
+generalization of that algorithm that can be used to compute barycenters
+[#]_ [#]_. In addition, we provide a Gromov-Wasserstein solver, which builds
+itself on top of Sinkhorn, as well as an implementation of input convex neural
+networks [#]_
 
 
 Sinkhorn and variants
@@ -26,9 +28,18 @@ Gromov-Wasserstein Solver
 
     gromov_wasserstein.gromov_wasserstein
 
+Neural Potentials
+-------------------------
+.. autosummary::
+  :toctree: _autosummary
+
+    icnn.ICNN
+
+
 References
 ----------
 
 .. [#] J.D. Benamou et al., `Iterative Bregman Projections for Regularized Transportation Problems <https://epubs.siam.org/doi/abs/10.1137/141000439>`_ , SIAM J. Sci. Comput. 37(2), A1111-A1138.
 .. [#] H. Janati et al., `Debiased Sinkhorn Barycenters <http://proceedings.mlr.press/v119/janati20a.html>`_ , ICML 2020.
-.. [#] F. Memoli, `Gromov–Wasserstein distances and the metric approach to object matching <https://link.springer.com/article/10.1007/s10208-011-9093-5>`_ , FOCM 2011 
\ No newline at end of file
+.. [#] F. Memoli, `Gromov–Wasserstein distances and the metric approach to object matching <https://link.springer.com/article/10.1007/s10208-011-9093-5>`_ , FOCM 2011 
+.. [#] B. Amos, L. Xu, J. Z. Kolter, Input Convex Neural Networks, ICML 2017
\ No newline at end of file
diff --git a/ott/version.py b/ott/version.py
index 2b5d3c15..1bfdc6b0 100644
--- a/ott/version.py
+++ b/ott/version.py
@@ -15,4 +15,4 @@
 
 """Current ott version."""
 
-__version__ = "0.1.18"
+__version__ = "0.1.19"