rename ref

Author: basnijholt

not_on_crossref.bib

@@ -8,7 +8,7 @@ @misc{papercode
   commit = {ebf381e9e0018808684c6a4199d04d96b35e936c}
 }
 
-@misc{Nijholt2019,
+@misc{Nijholt2019a,
   doi = {10.5281/zenodo.1182437},
   author = {Bas Nijholt and Joseph Weston and Jorn Hoofwijk and Anton Akhmerov},
   title = {\textit{Adaptive}: parallel active learning of mathematical functions},

paper.bib

@@ -13,7 +13,7 @@ @misc{papercode
   commit = {ebf381e9e0018808684c6a4199d04d96b35e936c}
 }
 
-@misc{Nijholt2019,
+@misc{Nijholt2019a,
   doi = {10.5281/zenodo.1182437},
   author = {Bas Nijholt and Joseph Weston and Jorn Hoofwijk and Anton Akhmerov},
   title = {\textit{Adaptive}: parallel active learning of mathematical functions},

paper.md

@@ -71,7 +71,7 @@ In all cases using Adaptive results in a higher fidelity plot.
 
 
 #### We provide a reference implementation, the Adaptive package, and demonstrate its performance.
-We provide a reference implementation, the open-source Python package called Adaptive [@Nijholt2019], which has previously been used in several scientific publications [@Vuik2018; @Laeven2019; @Bommer2019; @Melo2019].
+We provide a reference implementation, the open-source Python package called Adaptive [@Nijholt2019a], which has previously been used in several scientific publications [@Vuik2018; @Laeven2019; @Bommer2019; @Melo2019].
 It has algorithms for $f \colon \mathbb{R}^N \to \mathbb{R}^M$, where $N, M \in \mathbb{Z}^+$ but which work best when $N$ is small; integration in $\mathbb{R}$; and the averaging of stochastic functions.
 Most of our algorithms allow for a customizable loss function with which one can adapt the sampling algorithm to work optimally for different classes of functions.
 It integrates with the Jupyter notebook environment as well as popular parallel computation frameworks such as `ipyparallel`, `mpi4py`, and `dask.distributed`.