message: Fix year of publishing
diff: @@ -55,7 +55,7 @@ def ctc_loss(labels,
  logits=None):
  """Computes the CTC (Connectionist Temporal Classification) Loss.
  - This op implements the CTC loss as presented in (Graves et al., 2016).
  + This op implements the CTC loss as presented in (Graves et al., 2006).
  Input requirements:
  @@ -153,7 +153,7 @@ def ctc_loss(labels,
  References:
  Connectionist Temporal Classification - Labeling Unsegmented Sequence Data
  with Recurrent Neural Networks:
  - [Graves et al., 2016](https://dl.acm.org/citation.cfm?id=1143891)
  + [Graves et al., 2006](https://dl.acm.org/citation.cfm?id=1143891)
  ([pdf](http://www.cs.toronto.edu/~graves/icml_2006.pdf))
  """
  # The second, third, etc output tensors contain the gradients. We use it in
  @@ -608,7 +608,7 @@ def ctc_loss_v2(labels,
  name=None):
  """Computes CTC (Connectionist Temporal Classification) loss.
  - This op implements the CTC loss as presented in (Graves et al., 2016).
  + This op implements the CTC loss as presented in (Graves et al., 2006).
  Notes:
  @@ -648,7 +648,7 @@ def ctc_loss_v2(labels,
  References:
  Connectionist Temporal Classification - Labeling Unsegmented Sequence Data
  with Recurrent Neural Networks:
  - [Graves et al., 2016](https://dl.acm.org/citation.cfm?id=1143891)
  + [Graves et al., 2006](https://dl.acm.org/citation.cfm?id=1143891)
  ([pdf](http://www.cs.toronto.edu/~graves/icml_2006.pdf))
  """
  if isinstance(labels, sparse_tensor.SparseTensor):
  @@ -701,7 +701,7 @@ def ctc_loss_dense(labels,
  name=None):
  """Computes CTC (Connectionist Temporal Classification) loss.
  - This op implements the CTC loss as presented in (Graves et al., 2016),
  + This op implements the CTC loss as presented in (Graves et al., 2006),
  using the batched forward backward algorithm described in (Sim et al., 2017).
  Notes:
  @@ -748,7 +748,7 @@ def ctc_loss_dense(labels,
  References:
  Connectionist Temporal Classification - Labeling Unsegmented Sequence Data
  with Recurrent Neural Networks:
  - [Graves et al., 2016](https://dl.acm.org/citation.cfm?id=1143891)
  + [Graves et al., 2006](https://dl.acm.org/citation.cfm?id=1143891)
  ([pdf](http://www.cs.toronto.edu/~graves/icml_2006.pdf))
  Improving the efficiency of forward-backward algorithm using batched
  computation in TensorFlow:
  