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probml · Jul 12, 2024 · cc393d3 · cc393d3
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diff --git a/paper/paper.bib b/paper/paper.bib
@@ -69,7 +69,6 @@ @inproceedings{chang2023low
 @article{weinreb2024keypoint,
   author = {Weinreb, Caleb and Pearl, Jonah E. and Lin, Sherry and Osman, Mohammed Abdal Monium and Zhang, Libby and Annapragada, Sidharth and Conlin, Eli and Hoffmann, Red and Makowska, Sofia and Gillis, Winthrop F. and Jay, Maya and Ye, Shaokai and Mathis, Alexander and Mathis, Mackenzie W. and Pereira, Talmo and Linderman, Scott W. and Datta, Sandeep Robert},
   date = {2024/07/01},
-  doi = {10.1038/s41592-024-02318-2},
   id = {Weinreb2024},
   isbn = {1548-7105},
   journal = {Nature Methods},

diff --git a/paper/paper.md b/paper/paper.md
@@ -64,7 +64,7 @@ The API for `Dynamax` is divided into two parts: a set of core, functionally pur
 - Kalman filtering and smoothing algorithms for linear Gaussian SSMs, 
 - Extended and unscented Kalman filtering and smoothing for nonlinear Gaussian SSMs, 
 - Conditional moment filtering and smoothing algorithms for models with non-Gaussian emissions, and
-- Parallel message passing routines take advantage of GPU or TPU acceleration to perform message passing in sublinear time. 
+- Parallel message passing routines that leverage GPU or TPU acceleration to perform message passing in sublinear time. 
 
 The high-level model API makes it easy to construct, fit, and inspect HMMs and linear Gaussian SSMs. Finally, the online `Dynamax` documentation and tutorials provide a wealth of resources for state space modeling experts and newcomers alike.