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Add : Anjishnu ORCID #7

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Nov 9, 2022
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Add : Anjishnu ORCID #7

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7 changes: 4 additions & 3 deletions paper.md
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Expand Up @@ -27,6 +27,7 @@ authors:
- name: Aakash Kaushik
- name: Sangyeon Kim
affiliation: 5
orcid: 0000-0003-4012-8466
- name: Anjishnu Mukherjee
affiliation: 6
- name: Nanubala Gnana Sai
Expand Down Expand Up @@ -72,7 +73,7 @@ that allow prototyping to be seamlessly performed in environments other than C++
The use of machine learning has become ubiquitous in almost every scientific
discipline and countless commercial applications [@jordan2015machine] [@carleo2019machine].
There is one important commonality to virtually all of these applications:
machine learning is often computationally intensive, due to the
machine learning is often computationally intensive, due to the
large number of parameters and large amounts of training data.
This was the main motivator for the original development of mlpack in the C++
language, which allows for efficient close-to-the-metal implementations [@curtin2013mlpack].
Expand All @@ -95,7 +96,7 @@ The library contains a wide variety of machine learning algorithms,
some of which are new to mlpack 4. The list of algorithms includes linear regression,
logistic regression, random forests, furthest-neighbor search [@curtin2016fast],
accelerated k-means variants [@curtin2017dual], kernel density estimation [@lee2008fast],
and fast max-kernel search [@curtin2014dual]. There is also a module for
and fast max-kernel search [@curtin2014dual]. There is also a module for
deep neural networks, which has implementations of numerous layer types,
activation functions, and reinforcement learning applications.
Details of the available functionality are provided in the online
Expand Down Expand Up @@ -162,7 +163,7 @@ Furthermore, since mlpack's reduced dependency footprint has significantly
simplified the deployment process, mlpack's Python dependencies are now
available for numerous architectures both on PyPI and in `conda-forge`.

*Cross-compilation support and build system improvements.*
*Cross-compilation support and build system improvements.*
mlpack's build configuration now supports easy cross-compilation, for instance
via toolchains such as [buildroot](https://buildroot.org). By specifying a few
flags, a user may produce a working mlpack setup for a variety of embedded systems.
Expand Down