run pre-commit

.markdownlint.yaml

@@ -23,3 +23,9 @@ MD037: false
 MD049: false
 
 MD046: false
+
+MD052: false
+
+MD053: false
+
+MD045: false

dl/energy-based-models/ebm.diffusion.md

@@ -5,7 +5,7 @@ Many deep learning models utilize the concept of latent space, e.g., $\mathbf z$
 which is usually a compression of the real data space, e.g., $\mathbf x$, to enable easier computations for our task.
 
 However, such models usually require the computation of an intractable marginalization of the joint distribution $p(\mathbf x, \mathbf z)$ over the latent space[@Luo2022-hz]. To make such computations tractable, we have to apply approximations or theoretical assumptions.
-Diffusion models in deep learning establish the connection between the real data space $\mathbf x$ and the latent space $\mathbf z$ assuming invertible [diffusion processes](https://en.wikipedia.org/wiki/Diffusion_process) [@Sohl-Dickstein2015-th][@Ho2020-er].
+Diffusion models in deep learning establish the connection between the real data space $\mathbf x$ and the latent space $\mathbf z$ assuming invertible [diffusion processes](https://en.wikipedia.org/wiki/Diffusion_process) [@Sohl-Dickstein2015-th] [@Ho2020-er].
 
 ## Objective
 

dl/notebooks/transformer_history.py

@@ -72,9 +72,11 @@
                 i["publicationDate"], "%Y-%m-%dT%H:%M:%SZ"
             ).toordinal()
         ),
-        np.log(1 + i.get("forwardEdgeCount", 0)) * (1 + rng.random() * 0.2)
-        if i.get("forwardEdgeCount", 0) > 50
-        else np.log(1 + i.get("forwardEdgeCount", 0)) + rng.random() * 3,
+        (
+            np.log(1 + i.get("forwardEdgeCount", 0)) * (1 + rng.random() * 0.2)
+            if i.get("forwardEdgeCount", 0) > 50
+            else np.log(1 + i.get("forwardEdgeCount", 0)) + rng.random() * 3
+        ),
     ]
 )