diff --git a/docs/source/notebooks/tabular_notebooks/explaining_tabpfn.ipynb b/docs/source/notebooks/tabular_notebooks/explaining_tabpfn.ipynb
index 11a4343..d9044cb 100644
--- a/docs/source/notebooks/tabular_notebooks/explaining_tabpfn.ipynb
+++ b/docs/source/notebooks/tabular_notebooks/explaining_tabpfn.ipynb
@@ -1222,6 +1222,22 @@
     }
    ],
    "execution_count": 18
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## References\n",
+    "\n",
+    "Hollmann, N., Müller, S., Purucker, L. et al. Accurate predictions on small data with a tabular foundation model. Nature 637, 319–326 (2025). https://doi.org/10.1038/s41586-024-08328-6\n",
+    "\n",
+    "Hollmann, N., Müller, S., Eggensperger, K. & Hutter, F. TabPFN: a transformer that solves small tabular classification problems in a second. In Proc. The Eleventh International Conference on Learning Representations (ICLR, 2023).\n",
+    "\n",
+    "Rundel, D., Kobialka, J., von Crailsheim, C., Feurer, M., Nagler, T., Rügamer, D. (2024). Interpretable Machine Learning for TabPFN. In: Longo, L., Lapuschkin, S., Seifert, C. (eds) Explainable Artificial Intelligence. xAI 2024. Communications in Computer and Information Science, vol 2154. Springer, Cham. https://doi.org/10.1007/978-3-031-63797-1_23\n",
+    "\n",
+    "TabPFN Repository: https://github.com/PriorLabs/TabPFN\n"
+   ],
+   "id": "f498a67051f7fb7"
   }
  ],
  "metadata": {