Update doc

doc/library/tensor/nnet/conv.txt

@@ -125,7 +125,7 @@ TODO: Give examples on how to use these things! They are pretty complicated.
     - :func:`GpuCorr3dMM <theano.sandbox.cuda.blas.GpuCorr3dMM>`
       This is a GPU-only 3d correlation relying on a Toeplitz matrix
       and gemm implementation (see sandbox.cuda.blas.GpuCorrMM)
-     It needs extra memory for the Toeplitz matrix, which is a 3D matrix of shape
+      It needs extra memory for the Toeplitz matrix, which is a 2D matrix of shape
       ``(no of channels * filter width * filter height * filter depth, output width * output height * output depth)``.
       As it provides a gradient, you can use it as a replacement for nnet.conv3d.
       Alternatively, you can use nnet.conv3d and allow Theano's graph optimizer
@@ -133,7 +133,9 @@ TODO: Give examples on how to use these things! They are pretty complicated.
       ``THEANO_FLAGS=optimizer_including=conv3d_gemm:convgrad3d_gemm:convtransp3d_gemm`` in your environment.
       This is not enabled by default because it uses some extra memory, but the
       overhead is small compared to conv3d_fft, there are no restrictions on
-      input or kernel shapes.
+      input or kernel shapes and strides are supported. If using it,
+      please see the warning about a bug in CUDA 5.0 to 6.0
+      in sandbox.cuda.blas.GpuCorrMM.
 
     - :func:`conv3d2d <theano.tensor.nnet.conv3d2d.conv3d>`
       Another conv3d implementation that uses the conv2d with data reshaping.