Update cornac_bivae_deep_dive.ipynb: fix typos

examples/02_model_collaborative_filtering/cornac_bivae_deep_dive.ipynb

@@ -610,7 +610,7 @@
             "source": [
                 "## 4 Discussion\n",
                 "\n",
-                "BiVAE is a new variational autoencoder tailored for dyadic data, where observations consist of measurements associated with two sets of objects, e.g., users, items and corresponding ratings.  The model is symmetric, which makes it easier to extend axiliary data from both sides of users and items.  In addition to preference data, the model can be applied to other types of dyadic data such as documentword matrices, and other tasks such as co-clustering.  \n",
+                "BiVAE is a new variational autoencoder tailored for dyadic data, where observations consist of measurements associated with two sets of objects, e.g., users, items and corresponding ratings.  The model is symmetric, which makes it easier to extend auxiliary data from both sides of users and items.  In addition to preference data, the model can be applied to other types of dyadic data such as document-word matrices, and other tasks such as co-clustering.  \n",
                 "\n",
                 "In the paper, there is also a discussion on Constrained Adaptive Priors (CAP), a proposed method to build informative priors to mitigate the well-known posterior collapse problem. We have left out that part purposely, not to distract the audiences.  Nevertheless, it is very interesting and worth taking a look.  \n",
                 "\n",