wanyu/IteraTeR_v2 huggingface dataset:
Original: High dimensional B-splines are catching tremendous attentions in fields of Iso-geometry Analysis, dynamic surface reconstruction and so on.
Revised:  B-splines are catching tremendous attentions in fields of Iso-geometry Analysis, dynamic surface reconstruction and so on.
Edit intent: coherence
Original:  It is shown that the generated dynamic surface accurately interpolates the sparsely distributed temperature data , preserves the dynamic characteristics, with fewer control points than those of traditional B-spline surface interpolation algorithm .
Revised:  It is shown that the generated dynamic surface accurately interpolates the temperature data provided by the weather stations, and the preserved dynamic characteristics can be useful for meteorology studies .
Edit intent: coherence
Original: We propose a novel accelerated variance-reduced gradient method called ANITA for  finite-sum optimization .
Revised: In this paper, we propose a novel accelerated variance-reduced gradient method called ANITA for finite-sum optimization .
Edit intent: clarity
Original:  In the general convex setting, ANITA achieves the convergence result O%DIFDELCMD < \big(%%% n\min%DIFDELCMD < \big\{%%% 1+\log\frac{1{\epsilonn}, \logn}%DIFDELCMD < \big\} %%% + \sqrt{\frac{nL{\epsilon}} }%DIFDELCMD < \big)%%% , which improves the previous best result O%DIFDELCMD < \big(%%% n\min\{\log\frac{1{\epsilon}, \log n\}+\frac{nL{\epsilon}}}%DIFDELCMD < \big) %%%  given by Varag (Lan et al., 2019).
Revised:  In the general convex setting, ANITA achieves the convergence result O%DIFDELCMD < \big(%%% %DIFDELCMD < \big\{%%% 1+\log\frac{1{\epsilonn}, \logn}%DIFDELCMD < \big\} %%% + \sqrt{\frac{nL{\epsilon}} }%DIFDELCMD < \big)%%% , which improves the previous best result O%DIFDELCMD < \big(%%% n\min\{\log\frac{1{\epsilon}, \log n\}+\frac{nL{\epsilon}}}%DIFDELCMD < \big) %%% given by Varag (Lan et al., 2019).
Edit intent: fluency
Original:  In the general convex setting, ANITA achieves the convergence result O%DIFDELCMD < \big(%%% n\min%DIFDELCMD < \big\{%%% 1+\log\frac{1{\epsilonn}, \logn}%DIFDELCMD < \big\} %%% + \sqrt{\frac{nL{\epsilon}} }%DIFDELCMD < \big)%%% , which improves the previous best result O%DIFDELCMD < \big(%%% n\min\{\log\frac{1{\epsilon}, \log n\}+\frac{nL{\epsilon}}}%DIFDELCMD < \big) %%%  given by Varag (Lan et al., 2019).
Revised:  In the general convex setting, ANITA achieves the convergence result O%DIFDELCMD < \big(%%% n\min%DIFDELCMD < \big\{%%% 1+\log\frac{1{\epsilonn}, \logn}%DIFDELCMD < \big\} %%% + \sqrt{\frac{nL{\epsilon}} }%DIFDELCMD < \big)%%% , which improves the previous best result O%DIFDELCMD < \big(%%% {\epsilon}, \log n\}+\frac{nL{\epsilon}}}%DIFDELCMD < \big) %%% given by Varag (Lan et al., 2019).
Edit intent: clarity
Original:  In particular, for a very wide range of \epsilon , i.e., \epsilon \in (0,L{n\log^2n \cup%DIFDELCMD < [%%% \frac{1{\sqrt{n}},+\infty), where \epsilon is the error tolerance f(x_T)-f^*\leq \epsilon and n is the number of data samples, ANITA can achieve the optimal convergence result O}%DIFDELCMD < \big(%%% n+\sqrt{\frac{nL{\epsilon}}}%DIFDELCMD < \big)
Revised:  In particular, for a very wide range of \epsilon , i.e., \cup%DIFDELCMD < [%%% \frac{1{\sqrt{n}},+\infty), where \epsilon is the error tolerance f(x_T)-f^*\leq \epsilon and n is the number of data samples, ANITA can achieve the optimal convergence result O}%DIFDELCMD < \big(%%% n+\sqrt{\frac{nL{\epsilon}}}%DIFDELCMD < \big)
Edit intent: coherence
Original:  In particular, for a very wide range of \epsilon , i.e., \epsilon \in (0,L{n\log^2n \cup%DIFDELCMD < [%%% \frac{1{\sqrt{n}},+\infty), where \epsilon is the error tolerance f(x_T)-f^*\leq \epsilon and n is the number of data samples, ANITA can achieve the optimal convergence result O}%DIFDELCMD < \big(%%% n+\sqrt{\frac{nL{\epsilon}}}%DIFDELCMD < \big)
Revised:  In particular, for a very wide range of \epsilon , i.e., \epsilon \in (0,L{n\log^2n %DIFDELCMD < [%%% \frac{1{\sqrt{n}},+\infty), where \epsilon is the error tolerance f(x_T)-f^*\leq \epsilon and n is the number of data samples, ANITA can achieve the optimal convergence result O}%DIFDELCMD < \big(%%% n+\sqrt{\frac{nL{\epsilon}}}%DIFDELCMD < \big)
Edit intent: fluency
Original:  To the best of our knowledge, ANITA is the\emph{first accelerated algorithm which can\emph{exactly achieve this optimal result O%DIFDELCMD < \big(%%% n +\sqrt{\frac{nL{\epsilon}}}%DIFDELCMD < \big) %%% for general convex{\epsilon}) of Varag (Lan et al., 2019) and O(\frac{n}{\sqrt{\epsilon}}) of Katyusha (Allen-Zhu, 2017). ii) For strongly convex } finite-sum problems.
Revised:  To the best of our knowledge, ANITA is the\emph{ accelerated algorithm which can\emph{exactly achieve this optimal result O%DIFDELCMD < \big(%%% n +\sqrt{\frac{nL{\epsilon}}}%DIFDELCMD < \big) %%% for general convex{\epsilon}) of Varag (Lan et al., 2019) and O(\frac{n}{\sqrt{\epsilon}}) of Katyusha (Allen-Zhu, 2017). ii) For strongly convex } finite-sum problems.
Edit intent: fluency
Original:  To the best of our knowledge, ANITA is the\emph{first accelerated algorithm which can\emph{exactly achieve this optimal result O%DIFDELCMD < \big(%%% n +\sqrt{\frac{nL{\epsilon}}}%DIFDELCMD < \big) %%% for general convex{\epsilon}) of Varag (Lan et al., 2019) and O(\frac{n}{\sqrt{\epsilon}}) of Katyusha (Allen-Zhu, 2017). ii) For strongly convex } finite-sum problems.
Revised:  To the best of our knowledge, ANITA is the\emph{first \emph{exactly achieve this optimal result O%DIFDELCMD < \big(%%% n +\sqrt{\frac{nL{\epsilon}}}%DIFDELCMD < \big) %%% for general convex{\epsilon}) of Varag (Lan et al., 2019) and O(\frac{n}{\sqrt{\epsilon}}) of Katyusha (Allen-Zhu, 2017). ii) For strongly convex } finite-sum problems.
Edit intent: clarity
Original:  To the best of our knowledge, ANITA is the\emph{first accelerated algorithm which can\emph{exactly achieve this optimal result O%DIFDELCMD < \big(%%% n +\sqrt{\frac{nL{\epsilon}}}%DIFDELCMD < \big) %%% for general convex{\epsilon}) of Varag (Lan et al., 2019) and O(\frac{n}{\sqrt{\epsilon}}) of Katyusha (Allen-Zhu, 2017). ii) For strongly convex } finite-sum problems.
Revised:  To the best of our knowledge, ANITA is the\emph{first accelerated algorithm which can\emph{ achieve this optimal result O%DIFDELCMD < \big(%%% n +\sqrt{\frac{nL{\epsilon}}}%DIFDELCMD < \big) %%% for general convex{\epsilon}) of Varag (Lan et al., 2019) and O(\frac{n}{\sqrt{\epsilon}}) of Katyusha (Allen-Zhu, 2017). ii) For strongly convex } finite-sum problems.
Edit intent: clarity
Original:  To the best of our knowledge, ANITA is the\emph{first accelerated algorithm which can\emph{exactly achieve this optimal result O%DIFDELCMD < \big(%%% n +\sqrt{\frac{nL{\epsilon}}}%DIFDELCMD < \big) %%% for general convex{\epsilon}) of Varag (Lan et al., 2019) and O(\frac{n}{\sqrt{\epsilon}}) of Katyusha (Allen-Zhu, 2017). ii) For strongly convex } finite-sum problems.
Revised:  To the best of our knowledge, ANITA is the\emph{first accelerated algorithm which can\emph{exactly %DIFDELCMD < \big(%%% n +\sqrt{\frac{nL{\epsilon}}}%DIFDELCMD < \big) %%% for general convex{\epsilon}) of Varag (Lan et al., 2019) and O(\frac{n}{\sqrt{\epsilon}}) of Katyusha (Allen-Zhu, 2017). ii) For strongly convex } finite-sum problems.
Edit intent: clarity
Original: In the strongly convex setting , we also show that ANITA can achieve the optimal convergence result O%DIFDELCMD < \Big(%%%  \big(  n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %%% {\epsilon}}\big) matching the lower bound \Omega %DIFDELCMD < \Big(%%% \big(  n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %DIFDELCMD < %%% {\epsilon}}\big) provided by Lan and Zhou (2015).
Revised: In the strongly convex setting , we also show that ANITA can achieve the optimal convergence %DIFDELCMD < \Big(%%% \big( n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %%% {\epsilon}}\big) matching the lower bound \Omega %DIFDELCMD < \Big(%%% \big( n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %DIFDELCMD < %%% {\epsilon}}\big) provided by Lan and Zhou (2015).
Edit intent: clarity
Original: In the strongly convex setting , we also show that ANITA can achieve the optimal convergence result O%DIFDELCMD < \Big(%%%  \big(  n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %%% {\epsilon}}\big) matching the lower bound \Omega %DIFDELCMD < \Big(%%% \big(  n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %DIFDELCMD < %%% {\epsilon}}\big) provided by Lan and Zhou (2015).
Revised: In the strongly convex setting , we also show that ANITA can achieve the optimal convergence result O%DIFDELCMD < \Big(%%% \big( ( n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %%% {\epsilon}}\big) matching the lower bound \Omega %DIFDELCMD < \Big(%%% \big( n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %DIFDELCMD < %%% {\epsilon}}\big) provided by Lan and Zhou (2015).
Edit intent: fluency
Original: In the strongly convex setting , we also show that ANITA can achieve the optimal convergence result O%DIFDELCMD < \Big(%%%  \big(  n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %%% {\epsilon}}\big) matching the lower bound \Omega %DIFDELCMD < \Big(%%% \big(  n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %DIFDELCMD < %%% {\epsilon}}\big) provided by Lan and Zhou (2015).
Revised: In the strongly convex setting , we also show that ANITA can achieve the optimal convergence result O%DIFDELCMD < \Big(%%% \big( n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% {\epsilon}}%DIFDELCMD < \Big) %%% {\epsilon}}\big) matching the lower bound \Omega %DIFDELCMD < \Big(%%% \big( n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %DIFDELCMD < %%% {\epsilon}}\big) provided by Lan and Zhou (2015).
Edit intent: fluency
Original: In the strongly convex setting , we also show that ANITA can achieve the optimal convergence result O%DIFDELCMD < \Big(%%%  \big(  n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %%% {\epsilon}}\big) matching the lower bound \Omega %DIFDELCMD < \Big(%%% \big(  n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %DIFDELCMD < %%% {\epsilon}}\big) provided by Lan and Zhou (2015).
Revised: In the strongly convex setting , we also show that ANITA can achieve the optimal convergence result O%DIFDELCMD < \Big(%%% \big( n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %%% )\log\frac{1{\epsilon}}\big) matching the lower bound \Omega %DIFDELCMD < \Big(%%% \big( n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %DIFDELCMD < %%% {\epsilon}}\big) provided by Lan and Zhou (2015).
Edit intent: coherence
Original: In the strongly convex setting , we also show that ANITA can achieve the optimal convergence result O%DIFDELCMD < \Big(%%%  \big(  n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %%% {\epsilon}}\big) matching the lower bound \Omega %DIFDELCMD < \Big(%%% \big(  n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %DIFDELCMD < %%% {\epsilon}}\big) provided by Lan and Zhou (2015).
Revised: In the strongly convex setting , we also show that ANITA can achieve the optimal convergence result O%DIFDELCMD < \Big(%%% \big( n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %%% {\epsilon}}\big) matching the lower bound \Omega %DIFDELCMD < \Big(%%% \big( ( n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %DIFDELCMD < %%% {\epsilon}}\big) provided by Lan and Zhou (2015).
Edit intent: fluency
Original: In the strongly convex setting , we also show that ANITA can achieve the optimal convergence result O%DIFDELCMD < \Big(%%%  \big(  n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %%% {\epsilon}}\big) matching the lower bound \Omega %DIFDELCMD < \Big(%%% \big(  n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %DIFDELCMD < %%% {\epsilon}}\big) provided by Lan and Zhou (2015).
Revised: In the strongly convex setting , we also show that ANITA can achieve the optimal convergence result O%DIFDELCMD < \Big(%%% \big( n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %%% {\epsilon}}\big) matching the lower bound \Omega %DIFDELCMD < \Big(%%% \big( n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% {\epsilon}}%DIFDELCMD < \Big) %DIFDELCMD < %%% {\epsilon}}\big) provided by Lan and Zhou (2015).
Edit intent: fluency
Original: In the strongly convex setting , we also show that ANITA can achieve the optimal convergence result O%DIFDELCMD < \Big(%%%  \big(  n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %%% {\epsilon}}\big) matching the lower bound \Omega %DIFDELCMD < \Big(%%% \big(  n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %DIFDELCMD < %%% {\epsilon}}\big) provided by Lan and Zhou (2015).
Revised: In the strongly convex setting , we also show that ANITA can achieve the optimal convergence result O%DIFDELCMD < \Big(%%% \big( n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %%% {\epsilon}}\big) matching the lower bound \Omega %DIFDELCMD < \Big(%%% \big( n+\sqrt{\frac{nL}{\mu}} %DIFDELCMD < \big)%%% \log\frac{1{\epsilon}}%DIFDELCMD < \Big) %DIFDELCMD < %%% )\log\frac{1{\epsilon}}\big) provided by Lan and Zhou (2015).
Edit intent: fluency
Original:  Moreover , ANITA enjoys a simpler loopless algorithmic structure unlike previous accelerated algorithms such as Katyusha (Allen-Zhu, 2017) and Varag (Lan et al., 2019)  where they use an inconvenient double-loop structure.
Revised:  Besides , ANITA enjoys a simpler loopless algorithmic structure unlike previous accelerated algorithms such as Katyusha (Allen-Zhu, 2017) and Varag (Lan et al., 2019) where they use an inconvenient double-loop structure.
Edit intent: coherence
Original:  Moreover , ANITA enjoys a simpler loopless algorithmic structure unlike previous accelerated algorithms such as Katyusha (Allen-Zhu, 2017) and Varag (Lan et al., 2019)  where they use an inconvenient double-loop structure.
Revised:  Moreover , ANITA enjoys a simpler loopless algorithmic structure unlike previous accelerated algorithms such as Varag (Lan et al., 2019) where they use an inconvenient double-loop structure.
Edit intent: coherence
Original:  Moreover , ANITA enjoys a simpler loopless algorithmic structure unlike previous accelerated algorithms such as Katyusha (Allen-Zhu, 2017) and Varag (Lan et al., 2019)  where they use an inconvenient double-loop structure.
Revised:  Moreover , ANITA enjoys a simpler loopless algorithmic structure unlike previous accelerated algorithms such as Katyusha (Allen-Zhu, 2017) and Varag (Lan et al., 2019) and Katyusha (Allen-Zhu, 2017) where they use an inconvenient double-loop structure.
Edit intent: coherence
Original:  Finally, the experimental results also\emph{  show that ANITA converges faster than  previous state-of-the-art Varag (Lan et al., 2019), validating our theoretical results and confirming the practical superiority of ANITA  .
Revised:  Finally, the experimental results also\emph{ show that ANITA converges faster than the previous state-of-the-art Varag (Lan et al., 2019), validating our theoretical results and confirming the practical superiority of ANITA .
Edit intent: fluency
Original:  the second follows David Christopher's recent new proof relying on partition-theoretic arguments.
Revised:  the second follows David Christopher's more recent proof relying on partition-theoretic arguments.
Edit intent: clarity
Original:  We argue that it is the prohibitively large design space for Deep Reinforcement Learning (DRL), rather than algorithmic limitations per se , that are truly responsible for this lack of adoption.
Revised:  We argue that it is the prohibitively large design space for Deep Reinforcement Learning (DRL), rather than algorithmic limitations per se , that are truly responsible for this lack of adoption.
Edit intent: fluency
Original: On the space of rooted trees with possibly repeated labels, where all vertices are unordered  , we define two metrics: the best-match metric and the left-regular metric .
Revised: On the space of rooted trees with possibly repeated labels, where all vertices are unordered . On this space , we define two metrics: the best-match metric and the left-regular metric .
Edit intent: clarity
Original:  However, the best-match metric can be slightly modified to become a semimetric on this space .
Revised:  However, the best-match metric .
Edit intent: coherence
Original:  To compute the left-regular distance between two trees, the expected time complexity is O(n), and the worst-case time complexity is O(n\log n) . Such metrics (semimetric) can be used to compare developmental trees, which describe embryogenesis in developmental biology .
Revised:  To compute the left-regular distance between two trees, the expected time complexity is O(n), and the worst-case time complexity is O(n\log n) .
Edit intent: coherence
Original: This survey paper focuses on the estimation schemes in molecular communications (MC) systems.
Revised: This survey paper focuses on the estimation schemes in molecular communication (MC) systems.
Edit intent: fluency
Original:  Next, we propose the L2R task with challenging metrics, inspired by learning-to-drive challenges, Formula-E racing, and multimodal trajectory prediction for autonomous driving.
Revised:  Next, we propose the L2R task with challenging metrics, inspired by learning-to-drive challenges, Formula-style racing, and multimodal trajectory prediction for autonomous driving.
Edit intent: clarity
Original:  We will make our code publicly available.
Revised:  We make all code available: URL
Edit intent: clarity
Original:  Additionally, we provide the L2R framework suite, facilitating simulated racing on high-precision models of real-world tracks , such as the famed Thruxton Circuit and the Las Vegas Motor Speedway .
Revised:  Additionally, we provide the L2R framework suite, facilitating simulated racing on high-precision models of real-world tracks .
Edit intent: clarity
Original: We show that the existence of a ``good'' coupling w.r.t.
Revised: We show that the existence of a "good"' coupling w.r.t.
Edit intent: fluency
Original: In this letter , we present an active visual SLAM approach for omnidirectional robots.
Revised: In this paper , we present an active visual SLAM approach for omnidirectional robots.
Edit intent: clarity
Original:  The goal is to generate control commands that allow such a robot to simultaneously localize itself and map an unknown environment while maximizing the amount of information gained and consume as little energy as possible.
Revised:  The goal is to generate control commands that allow such a robot to simultaneously localize itself and map an unknown environment while maximizing the amount of information gained and consume as low energy as possible.
Edit intent: clarity
Original:  Besides, the DRL agents performing well in training scenarios are found to perform poorly in some unseen real-world scenarios.
Revised:  Besides, the DRL agents performing well in training scenarios are found to perform poorly in unfamiliar real-world scenarios.
Edit intent: clarity
Original:  In this paper, we discuss why the DRL agent fails in such unseen scenarios and find the representation of LiDAR readings is the key factor behind the agent' s performance degradation .
Revised:  In this work, we present the representation of LiDAR readings is the key factor behind the agent' s performance degradation .
Edit intent: clarity
Original:  In this paper, we discuss why the DRL agent fails in such unseen scenarios and find the representation of LiDAR readings is the key factor behind the agent' s performance degradation .
Revised:  In this paper, we discuss why the DRL agent fails in such unseen scenarios and find the representation of LiDAR readings as a key factor behind the agent' s performance degradation .
Edit intent: fluency
Original:  The proposed IP functions can highlight the important short-distance values of laser scans and compress the range of less-important long-distance values . Extensive comparative experiments are carried out, and the experimental results demonstrate the high performance of the proposed IP approaches .
Revised:  The proposed IP functions can highlight the important short-distance values and compress the range of less-important long-distance values . Extensive comparative experiments are carried out, and the experimental results demonstrate the high performance of the proposed IP approaches .
Edit intent: clarity
Original:  The efficient implementation of most of these RBM requires to modify this high fidelity code, which cannot be done, for example in an industrial context if the high fidelity code is only accessible as a '' black-box '' solver.
Revised:  The efficient implementation of most of these RBM requires to modify this high fidelity code, which cannot be done, for example in an industrial context if the high fidelity code is only accessible as a " black-box '' solver.
Edit intent: fluency
Original:  The efficient implementation of most of these RBM requires to modify this high fidelity code, which cannot be done, for example in an industrial context if the high fidelity code is only accessible as a '' black-box '' solver.
Revised:  The efficient implementation of most of these RBM requires to modify this high fidelity code, which cannot be done, for example in an industrial context if the high fidelity code is only accessible as a '' black-box " solver.
Edit intent: fluency
Original:  Using them as pretrained models and fine-tuning them for specific tasks, researchers have extended the state of the art for many NLP tasks and shown that they not only capture linguistic knowledge but also retain general knowledge implicitly present in the data.
Revised:  Using them as pre-trained models and fine-tuning them for specific tasks, researchers have extended the state of the art for many NLP tasks and shown that they not only capture linguistic knowledge but also retain general knowledge implicitly present in the data.
Edit intent: fluency
Original:  Using them as pretrained models and fine-tuning them for specific tasks, researchers have extended the state of the art for many NLP tasks and shown that they not only capture linguistic knowledge but also retain general knowledge implicitly present in the data.
Revised:  Using them as pretrained models and fine-tuning them for specific tasks, researchers have extended state of the art for many NLP tasks and shown that they not only capture linguistic knowledge but also retain general knowledge implicitly present in the data.
Edit intent: fluency
Original:  Using them as pretrained models and fine-tuning them for specific tasks, researchers have extended the state of the art for many NLP tasks and shown that they not only capture linguistic knowledge but also retain general knowledge implicitly present in the data.
Revised:  Using them as pretrained models and fine-tuning them for specific tasks, researchers have extended the state of the art for many NLP tasks and shown that they capture not only linguistic knowledge but also retain general knowledge implicitly present in the data.
Edit intent: fluency
Original:  Unfortunately, LMs trained on unfiltered text corpora suffer from degenerate and biased behaviour.
Revised:  Unfortunately, LMs trained on unfiltered text corpora suffer from degenerated and biased behaviour.
Edit intent: fluency