typo

README.md

@@ -43,7 +43,7 @@ Applies the spline-based convolution operator
   <img width="50%" src="https://user-images.githubusercontent.com/6945922/38684093-36d9c52e-3e6f-11e8-9021-db054223c6b9.png" />
 </p>
 over several node features of an input graph.
-The kernel function g is defined over the weighted B-spline tensor product basis, as shown below for different B-spline degrees.
+The kernel function is defined over the weighted B-spline tensor product basis, as shown below for different B-spline degrees.
 
 <p align="center">
   <img width="45%" src="https://user-images.githubusercontent.com/6945922/38685443-3a2a0c68-3e72-11e8-8e13-9ce9ad8fe43e.png" />

torch_spline_conv/conv.py

@@ -20,8 +20,8 @@ def spline_conv(src,
     """Applies the spline-based convolution operator :math:`(f \star g)(i) =
     \frac{1}{|\mathcal{N}(i)|} \sum_{l=1}^{M_{in}} \sum_{j \in \mathcal{N}(i)}
     f_l(j) \cdot g_l(u(i, j))` over several node features of an input graph.
-    Here, :math:`g_l` denotes the kernel function defined over the weighted
-    B-spline tensor product basis for a single input feature map :math:`l`.
+    The kernel function :math:`g_l` is defined over the weighted B-spline
+    tensor product basis for a single input feature map :math:`l`.
 
     Args:
         src (Tensor or Variable): Input node features of shape