final fixes

2025-09-10 11:28:00 +00:00
parent fa32707957
commit 3a642c53a0
2 changed files with 2 additions and 2 deletions
@@ -593,7 +593,7 @@ In practice, convolutional architectures have achieved strong performance on seq
 For example,~\citeauthor{lecun_convolutional_1998} showed that a simple Temporal Convolutional Network often outperforms canonical
 recurrent models (like LSTMs) across diverse sequence modeling benchmarks.
 Their experiments suggest that CNNs are “a natural starting point for sequence modeling,”
-especially when temporal features are local or multi-scale.
+especially when temporal features are local or multiscale.
 In summary, 1D convolutions provide an efficient way to extract local temporal features and compress high-resolution sequences,
 complementing recurrent and attention-based models in time-series analysis