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-rw-r--r--docs/source/readme.rst6
1 files changed, 3 insertions, 3 deletions
diff --git a/docs/source/readme.rst b/docs/source/readme.rst
index 2707a07..c96f191 100644
--- a/docs/source/readme.rst
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@@ -70,7 +70,7 @@ POT provides the following generic OT solvers (links to examples):
POT provides the following Machine Learning related solvers:
- `Optimal transport for domain
- adaptation <auto_examples/plot_otda_classes.html>`__
+ adaptation <auto_examples/domain-adaptation/plot_otda_classes.html>`__
with `group lasso
regularization <auto_examples/domain-adaptation/plot_otda_classes.html>`__,
`Laplacian
@@ -80,10 +80,10 @@ POT provides the following Machine Learning related solvers:
- `Linear OT
mapping <auto_examples/domain-adaptation/plot_otda_linear_mapping.html>`__
[14] and `Joint OT mapping
- estimation <auto_examples/plot_otda_mapping.html>`__
+ estimation <auto_examples/domain-adaptation/plot_otda_mapping.html>`__
[8].
- `Wasserstein Discriminant
- Analysis <auto_examples/domain-adaptation/plot_WDA.html>`__
+ Analysis <auto_examples/others/plot_WDA.html>`__
[11] (requires autograd + pymanopt).
- `JCPOT algorithm for multi-source domain adaptation with target
shift <auto_examples/domain-adaptation/plot_otda_jcpot.html>`__