raise ValueError on non-https author URLs

fix yaml whitespace

data/applications.yml

@@ -35,7 +35,7 @@
     - name: Srinivas Vasudevan
   description: Uses normalizing flows in conjunction with Monte Carlo estimation to have more expressive distributions and better posterior estimation.
 
-- title: 'SRFlow: Learning the Super-Resolution Space with Normalizing Flow'
+- title: "SRFlow: Learning the Super-Resolution Space with Normalizing Flow"
   url: https://arxiv.org/abs/2006.14200
   date: 2020-06-25
   authors:

data/make_readme.py

@@ -66,13 +66,7 @@ def load_items(key: str) -> list[Item]:
 
 seen_titles: set[tuple[str, str]] = set()
 required_keys = {"title", "url", "date", "authors", "description"}
-optional_keys = {
-    "lang",
-    "repo",
-    "docs",
-    "date_added",
-    "last_updated",
-}
+optional_keys = {"lang", "repo", "docs", "date_added", "last_updated"}
 valid_languages = {"PyTorch", "TensorFlow", "JAX", "Julia", "Other"}
 et_al_after = 2
 
@@ -157,12 +151,18 @@ def validate_item(itm: Item, section_title: str) -> None:
                     auth | {"name": auth["name"].split(" ")[-1]} for auth in authors
                 ]
 
-            authors_str = ", ".join(
-                f"[{auth['name']}]({auth_url})"
-                if (auth_url := auth.get("url"))
-                else auth["name"]
-                for auth in authors[:et_al_after]
-            )
+            def auth_str(auth: Author) -> str:
+                """Return a markdown string for an author."""
+                auth_str = auth["name"]
+                if url := auth.get("url"):
+                    if not url.startswith("https://"):
+                        raise ValueError(
+                            f"Invalid author {url=}, must start with https://"
+                        )
+                    auth_str = f"[{auth_str}]({url})"
+                return auth_str
+
+            authors_str = ", ".join(map(auth_str, authors[:et_al_after]))
             if len(authors) > et_al_after:
                 authors_str += " et al."
 

data/packages.yml

@@ -55,8 +55,9 @@
   date: 2021-04-12
   url: https://github.com/deepmind/distrax
   authors:
-  - name: DeepMind
-    url: https://deepmind.com
+    - name: DeepMind
+      url: https://deepmind.com
+      github: https://github.com/google-deepmind
   lang: JAX
   description: Distrax is a lightweight library of probability distributions and bijectors. It acts as a JAX-native re-implementation of a subset of TensorFlow Probability (TFP), with some new features and emphasis on extensibility.
 

data/posts.yml

@@ -36,4 +36,6 @@
   # file url: https://github.com/whitead/dmol-book/blob/master/dl/flows.ipynb
   authors:
     - name: Andrew White
+      url: https://thewhitelab.org
+      github: https://github.com/whitead
   description: A nice introduction starting with the change of variables formula (aka flow equation), going on to cover some common bijectors and finishing with a code example showing how to fit the double-moon distribution with TensorFlow Probability.

data/publications.yml

@@ -194,13 +194,13 @@
   url: https://arxiv.org/abs/1907.08982
   date: 2019-07-21
   authors:
-  - name: Jonas Rothfuss
-  - name: Fabio Ferreira
-  - name: Simon Boehm
-  - name: Simon Walther
-  - name: Maxim Ulrich
-  - name: Tamim Asfour
-  - name: Andreas Krause
+    - name: Jonas Rothfuss
+    - name: Fabio Ferreira
+    - name: Simon Boehm
+    - name: Simon Walther
+    - name: Maxim Ulrich
+    - name: Tamim Asfour
+    - name: Andreas Krause
   description: Normalizing flows for conditional density estimation. This paper proposes noise regularization to reduce overfitting. [[Blog](https://siboehm.com/articles/19/normalizing-flow-network)]
 
 - title: "Normalizing Flows: An Introduction and Review of Current Methods"
@@ -337,9 +337,9 @@
   url: https://arxiv.org/abs/2006.08545
   date: 2020-06-15
   authors:
-  - name: Polina Kirichenko
-  - name: Pavel Izmailov
-  - name: Andrew Gordon Wilson
+    - name: Polina Kirichenko
+    - name: Pavel Izmailov
+    - name: Andrew Gordon Wilson
   description: This study how traditional normalizing flow models can suffer from out-of-distribution data. They offer a solution to combat this issue by modifying the coupling layers. [[Tweet](https://twitter.com/polkirichenko/status/1272715634544119809)]
   repo: https://github.com/PolinaKirichenko/flows_ood
 

data/videos.yml

@@ -2,7 +2,7 @@
   url: https://youtu.be/VeYyUcIDVHI
   date: 2018-10-04
   authors:
-   - name: Rianne van den Berg
+    - name: Rianne van den Berg
   description: Introduces Sylvester normalizing flows which remove the single-unit bottleneck from planar flows for increased flexibility in the variational posterior.
 
 - title: Graph Normalizing Flows

readme.md

@@ -515,7 +515,7 @@ Table 1 in the paper has a good comparison with traditional NFs.
 
 ## 🌐 Blog Posts <small>(5)</small>
 
-1. 2020-08-19 - [Chapter on flows from the book 'Deep Learning for Molecules and Materials'](https://dmol.pub/dl/flows) by Andrew White<br>
+1. 2020-08-19 - [Chapter on flows from the book 'Deep Learning for Molecules and Materials'](https://dmol.pub/dl/flows) by [Andrew White](https://thewhitelab.org)<br>
    A nice introduction starting with the change of variables formula (aka flow equation), going on to cover some common bijectors and finishing with a code example showing how to fit the double-moon distribution with TensorFlow Probability.
 
 1. 2018-10-21 - [Change of Variables for Normalizing Flows](https://nealjean.com/ml/change-of-variables) by Neal Jean<br>