wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this paper, we have used a graph based self-supervised learning strategy with different loss functions (Barlow Twins[ 7 ], HSIC[ 4 ], VICReg[ 1 ]) which have shown promising results when applied with CNNs previously.
Revised:  In this paper, we have used a graph based self-supervised learning strategy with different loss functions (Barlow Twins[ Zbontar et al., 2021 ], HSIC[ 4 ], VICReg[ 1 ]) which have shown promising results when applied with CNNs previously.
Edit intent: meaning-changed
Original:  In this paper, we have used a graph based self-supervised learning strategy with different loss functions (Barlow Twins[ 7 ], HSIC[ 4 ], VICReg[ 1 ]) which have shown promising results when applied with CNNs previously.
Revised:  In this paper, we have used a graph based self-supervised learning strategy with different loss functions (Barlow Twins[ 7 ], HSIC[ Tsai et al., 2021 ], VICReg[ 1 ]) which have shown promising results when applied with CNNs previously.
Edit intent: meaning-changed
Original:  In this paper, we have used a graph based self-supervised learning strategy with different loss functions (Barlow Twins[ 7 ], HSIC[ 4 ], VICReg[ 1 ]) which have shown promising results when applied with CNNs previously.
Revised:  In this paper, we have used a graph based self-supervised learning strategy with different loss functions (Barlow Twins[ 7 ], HSIC[ 4 ], VICReg[ Bardes et al., 2021 ]) which have shown promising results when applied with CNNs previously.
Edit intent: meaning-changed
Original:  The performance of these aforementioned methods have been compared when applied to two different datasets namely MUTAGand PROTEINS . Moreover, the impact of different batch sizes, projector dimensions and data augmentation strategies have also been explored .
Revised:  The performance of these aforementioned methods have been compared when applied to different datasets such as MUTAG, PROTEINS and IMDB-Binary . Moreover, the impact of different batch sizes, projector dimensions and data augmentation strategies have also been explored .
Edit intent: meaning-changed
Original:  The performance of these aforementioned methods have been compared when applied to two different datasets namely MUTAGand PROTEINS . Moreover, the impact of different batch sizes, projector dimensions and data augmentation strategies have also been explored . The results are preliminary and we will be continuing to explore with other datasets.
Revised:  The performance of these aforementioned methods have been compared when applied to two different datasets namely MUTAGand PROTEINS . Moreover, the impact of different batch sizes, projector dimensions and data augmentation strategies have also been explored 
Edit intent: coherence
Original:  The coexistence problem was modeled as a  Dec-POMDP  and Bayesian approach was adopted for policy learning with nonparametric prior to accommodate the uncertainty of policy for different agents.
Revised:  The coexistence problem was modeled as a decentralized partially observable Markov decision process ( Dec-POMDP and Bayesian approach was adopted for policy learning with nonparametric prior to accommodate the uncertainty of policy for different agents.
Edit intent: meaning-changed
Original:  The coexistence problem was modeled as a  Dec-POMDP  and Bayesian approach was adopted for policy learning with nonparametric prior to accommodate the uncertainty of policy for different agents.
Revised:  The coexistence problem was modeled as a Dec-POMDP ) and Bayesian approach was adopted for policy learning with nonparametric prior to accommodate the uncertainty of policy for different agents.
Edit intent: fluency
Original:  To address such challenges , we propose a conditional hidden Markov model (CHMM) . It inherits the hidden Markov model's ability to aggregating the labels from weak sources through unsupervised learning.
Revised:  To address this challenge , we propose a conditional hidden Markov model (CHMM) . It inherits the hidden Markov model's ability to aggregating the labels from weak sources through unsupervised learning.
Edit intent: coherence
Original:  To address such challenges , we propose a conditional hidden Markov model (CHMM) . It inherits the hidden Markov model's ability to aggregating the labels from weak sources through unsupervised learning. However, CHMM enhances the  hidden Markov model 's flexibility and context representation capability by predicting token-wise transition and emission probabilities from the BERT embeddings of the input tokens .
Revised:  To address such challenges , we propose a conditional hidden Markov model (CHMM) , which can effectively infer true labels from multi-source noisy labels in an unsupervised way. CHMM enhances the hidden Markov model 's flexibility and context representation capability by predicting token-wise transition and emission probabilities from the BERT embeddings of the input tokens .
Edit intent: meaning-changed
Original:  However, CHMM enhances the  hidden Markov model 's flexibility and context representation capability by predicting token-wise transition and emission probabilities from the BERT embeddings of the input tokens .
Revised:  However, CHMM enhances the classic hidden Markov model 's flexibility and context representation capability by predicting token-wise transition and emission probabilities from the BERT embeddings of the input tokens .
Edit intent: meaning-changed
Original:  However, CHMM enhances the  hidden Markov model 's flexibility and context representation capability by predicting token-wise transition and emission probabilities from the BERT embeddings of the input tokens .
Revised:  However, CHMM enhances the hidden Markov model with the contextual representation power of pre-trained language models. Specifically, CHMM learns token-wise transition and emission probabilities from the BERT embeddings of the input tokens .
Edit intent: meaning-changed
Original:  However, CHMM enhances the  hidden Markov model 's flexibility and context representation capability by predicting token-wise transition and emission probabilities from the BERT embeddings of the input tokens . In addition, we refine CHMM 's prediction with an alternate-training approach ( CHMM-AlT ).
Revised:  However, CHMM enhances the hidden Markov model 's flexibility and context representation capability by predicting token-wise transition and emission probabilities from the BERT embeddings of the input tokens to infer the latent true labels from noisy observations. We further refine CHMM with an alternate-training approach ( CHMM-AlT ).
Edit intent: meaning-changed
Original:  It fine-tunes a BERT-based NER model with the labels inferred by CHMM, and this BERT-NER's output is regarded as an additional weak source to train the CHMM in return.
Revised:  It fine-tunes a BERT-NER model with the labels inferred by CHMM, and this BERT-NER's output is regarded as an additional weak source to train the CHMM in return.
Edit intent: clarity
Original:  It achieves 76.4%DIFDELCMD < \\%%% %DIF < Mean Intersection over Union (mIoU\\%) on the Cityscapes test dataset with the speed of 161 FPS on a single RTX 2080Ti card.
Revised:  It achieves %DIFDELCMD < \\%%% %DIF < Mean Intersection over Union (mIoU\\%) on the Cityscapes test dataset with the speed of 161 FPS on a single RTX 2080Ti card.
Edit intent: meaning-changed
Original:  It achieves 69 \% Mean Intersection over Union (mIoU\%) on the Cityscapes test dataset with the speed of 132on a single RTX 2080Ti card.
Revised:  It achieves 72 \% Mean Intersection over Union (mIoU\%) on the Cityscapes test dataset with the speed of 132on a single RTX 2080Ti card.
Edit intent: meaning-changed
Original:  It achieves 69 \% Mean Intersection over Union (mIoU\%) on the Cityscapes test dataset with the speed of 132on a single RTX 2080Ti card.
Revised:  It achieves 69 \% Mean Intersection over Union (mIoU\%) on the Cityscapes test dataset with the speed of 144 on a single RTX 2080Ti card.
Edit intent: meaning-changed
Original:  It achieves 69 \% Mean Intersection over Union (mIoU\%) on the Cityscapes test dataset with the speed of 132on a single RTX 2080Ti card.
Revised:  It achieves 69 \% Mean Intersection over Union (mIoU\%) on the Cityscapes test dataset with the speed of 132on a single RTX 1080Ti card.
Edit intent: meaning-changed
Original:  We then show that c-GWNNs are strictly more powerful than c-GNNs in the continuous limit, and prove their universality on several random graph models of interest, including most SBMs and a large class of random geometric graphs.
Revised:  We then show that c-SGNNs are strictly more powerful than c-GNNs in the continuous limit, and prove their universality on several random graph models of interest, including most SBMs and a large class of random geometric graphs.
Edit intent: meaning-changed
Original:  Both the theoretical analysis and the practical algorithms are presented.
Revised:  The penalty function introduced involves both the shape and the scale parameters, making it very flexible. The theoretical analysis results in terms of the null space property, the spherical section property and the restricted invertibility factor are established for both constrained and unconstrained models. The practical algorithms via both the iteratively reweighted \ell_1 and the difference of convex functions algorithms are presented.
Edit intent: meaning-changed
Original:  In this paper, we investigate one emerging questionif model's equilibrium point can be regarded as the solution of an optimization problem . Specifically , we first decompose DNNs into a new class of unit layer that is differential of an implicit convex function while keeping its output unchanged.
Revised:  In this paper, we investigate one emerging questionif model's equilibrium point can be regarded as the solution of an optimization problem ? To this end , we first decompose DNNs into a new class of unit layer that is differential of an implicit convex function while keeping its output unchanged.
Edit intent: coherence
Original:  In contrast to compute-intensive Deep Neural Networks that employ separate training and inference phases, TNNs are capable of extremely efficient online incremental/ continuous learning and are excellent candidates for building edge-native sensory processing units.
Revised:  In contrast to compute-intensive deep neural networks that employ separate training and inference phases, TNNs are capable of extremely efficient online incremental/ continuous learning and are excellent candidates for building edge-native sensory processing units.
Edit intent: fluency
Original:  In contrast to compute-intensive Deep Neural Networks that employ separate training and inference phases, TNNs are capable of extremely efficient online incremental/ continuous learning and are excellent candidates for building edge-native sensory processing units.
Revised:  In contrast to compute-intensive Deep Neural Networks that employ separate training and inference phases, TNNs are capable of extremely efficient online incremental/ continual learning and are excellent candidates for building edge-native sensory processing units.
Edit intent: fluency
Original:  As a consequence, the original NP-hard MPC problem is reduced to a polynomial time prediction and optimization  . Experimental results show that the hybrid approach achieves 27\% further reduction in rider waiting time than the MPC optimization , thanks to its ability to model a longer time horizonwithin the computational limits .
Revised:  As a consequence, the original NP-hard MPC problem is reduced to a polynomial time prediction and optimization , which allows the ride-sharing operators to consider a longer time horizon . Experimental results show that the hybrid approach achieves 27\% further reduction in rider waiting time than the MPC optimization , thanks to its ability to model a longer time horizonwithin the computational limits .
Edit intent: meaning-changed
Original:  As a consequence, the original NP-hard MPC problem is reduced to a polynomial time prediction and optimization  . Experimental results show that the hybrid approach achieves 27\% further reduction in rider waiting time than the MPC optimization , thanks to its ability to model a longer time horizonwithin the computational limits .
Revised:  As a consequence, the original NP-hard MPC problem is reduced to a polynomial time prediction and optimization . Experimental results show that the hybrid approach achieves significantly better service quality than the MPC optimization , thanks to its ability to model a longer time horizonwithin the computational limits .
Edit intent: meaning-changed
Original:  As a consequence, the original NP-hard MPC problem is reduced to a polynomial time prediction and optimization  . Experimental results show that the hybrid approach achieves 27\% further reduction in rider waiting time than the MPC optimization , thanks to its ability to model a longer time horizonwithin the computational limits .
Revised:  As a consequence, the original NP-hard MPC problem is reduced to a polynomial time prediction and optimization . Experimental results show that the hybrid approach achieves 27\% further reduction in rider waiting time than the MPC optimization in terms of average rider waiting time, due to its ability to model a longer time horizonwithin the computational limits .
Edit intent: meaning-changed
Original:  As a consequence, the original NP-hard MPC problem is reduced to a polynomial time prediction and optimization  . Experimental results show that the hybrid approach achieves 27\% further reduction in rider waiting time than the MPC optimization , thanks to its ability to model a longer time horizonwithin the computational limits .
Revised:  As a consequence, the original NP-hard MPC problem is reduced to a polynomial time prediction and optimization . Experimental results show that the hybrid approach achieves 27\% further reduction in rider waiting time than the MPC optimization , thanks to its ability to model a longer horizon .
Edit intent: clarity
Original: Neural SDEs combine many of the best qualities of both RNNs and SDEs , and as such are a natural choice for modelling many types of temporal dynamics. They offer memory efficiency , high-capacity function approximation, and strong priors on model space.
Revised: Neural SDEs combine many of the best qualities of both RNNs and SDEs : memory efficient training , high-capacity function approximation, and strong priors on model space.
Edit intent: clarity
Original:  First, we introduce the reversible Heun method: a new SDE solver that is algebraically reversible -- which reduces \textit{  numerical gradient errors to almost zero, improving several test metrics by substantial margins over state-of-the-art . Moreover it requires half as many function evaluations as comparable solvers, giving up to a 1.98\times speedup.
Revised:  First, we introduce the reversible Heun method a new SDE solver that is algebraically reversible -- which reduces \textit{ numerical gradient errors to almost zero, improving several test metrics by substantial margins over state-of-the-art . Moreover it requires half as many function evaluations as comparable solvers, giving up to a 1.98\times speedup.
Edit intent: fluency
Original:  First, we introduce the reversible Heun method: a new SDE solver that is algebraically reversible -- which reduces \textit{  numerical gradient errors to almost zero, improving several test metrics by substantial margins over state-of-the-art . Moreover it requires half as many function evaluations as comparable solvers, giving up to a 1.98\times speedup.
Revised:  First, we introduce the reversible Heun method:. This is a new SDE solver that is algebraically reversible -- which reduces \textit{ numerical gradient errors to almost zero, improving several test metrics by substantial margins over state-of-the-art . Moreover it requires half as many function evaluations as comparable solvers, giving up to a 1.98\times speedup.
Edit intent: coherence
Original:  First, we introduce the reversible Heun method: a new SDE solver that is algebraically reversible -- which reduces \textit{  numerical gradient errors to almost zero, improving several test metrics by substantial margins over state-of-the-art . Moreover it requires half as many function evaluations as comparable solvers, giving up to a 1.98\times speedup.
Revised:  First, we introduce the reversible Heun method: a new SDE solver that is \textit{ numerical gradient errors to almost zero, improving several test metrics by substantial margins over state-of-the-art . Moreover it requires half as many function evaluations as comparable solvers, giving up to a 1.98\times speedup.
Edit intent: coherence
Original:  First, we introduce the reversible Heun method: a new SDE solver that is algebraically reversible -- which reduces \textit{  numerical gradient errors to almost zero, improving several test metrics by substantial margins over state-of-the-art . Moreover it requires half as many function evaluations as comparable solvers, giving up to a 1.98\times speedup.
Revised:  First, we introduce the reversible Heun method: a new SDE solver that is algebraically reversible -- which reduces \textit{algebraically reversible numerical gradient errors to almost zero, improving several test metrics by substantial margins over state-of-the-art . Moreover it requires half as many function evaluations as comparable solvers, giving up to a 1.98\times speedup.
Edit intent: meaning-changed
Original:  First, we introduce the reversible Heun method: a new SDE solver that is algebraically reversible -- which reduces \textit{  numerical gradient errors to almost zero, improving several test metrics by substantial margins over state-of-the-art . Moreover it requires half as many function evaluations as comparable solvers, giving up to a 1.98\times speedup.
Revised:  First, we introduce the reversible Heun method: a new SDE solver that is algebraically reversible -- which reduces \textit{ : eliminating numerical gradient errors to almost zero, improving several test metrics by substantial margins over state-of-the-art . Moreover it requires half as many function evaluations as comparable solvers, giving up to a 1.98\times speedup.
Edit intent: meaning-changed
Original:  First, we introduce the reversible Heun method: a new SDE solver that is algebraically reversible -- which reduces \textit{  numerical gradient errors to almost zero, improving several test metrics by substantial margins over state-of-the-art . Moreover it requires half as many function evaluations as comparable solvers, giving up to a 1.98\times speedup.
Revised:  First, we introduce the reversible Heun method: a new SDE solver that is algebraically reversible -- which reduces \textit{ numerical gradient errors , and the first such solver of which we are aware . Moreover it requires half as many function evaluations as comparable solvers, giving up to a 1.98\times speedup.
Edit intent: meaning-changed
Original:  Next , we introduce the Brownian interval. This is a newand computationally efficientway of exactly sampling and reconstructing Brownian motion; this is in contrast to previous reconstruction techniques that are both approximate and relatively slow. This gives \textit{ \textit{  up to a 10.6\times speed improvement over previous techniques . After that , when specifically training Neural SDEs as GANs (Kidger et al. 2021), we demonstrate how SDE-GANs may be trained through careful weight clipping and choice of activation function.
Revised:  Next , we introduce the \textit{ \textit{ up to a 10.6\times speed improvement over previous techniques . After that , when specifically training Neural SDEs as GANs (Kidger et al. 2021), we demonstrate how SDE-GANs may be trained through careful weight clipping and choice of activation function.
Edit intent: coherence
Original:  This gives \textit{ \textit{  up to a 10.6\times speed improvement over previous techniques . After that , when specifically training Neural SDEs as GANs (Kidger et al. 2021), we demonstrate how SDE-GANs may be trained through careful weight clipping and choice of activation function.
Revised:  This gives \textit{Brownian Interval \textit{ up to a 10.6\times speed improvement over previous techniques . After that , when specifically training Neural SDEs as GANs (Kidger et al. 2021), we demonstrate how SDE-GANs may be trained through careful weight clipping and choice of activation function.
Edit intent: meaning-changed
Original:  This gives \textit{ \textit{  up to a 10.6\times speed improvement over previous techniques . After that , when specifically training Neural SDEs as GANs (Kidger et al. 2021), we demonstrate how SDE-GANs may be trained through careful weight clipping and choice of activation function.
Revised:  This gives \textit{ : a new, fast, memory efficient, and exact way of sampling\textit{ up to a 10.6\times speed improvement over previous techniques . After that , when specifically training Neural SDEs as GANs (Kidger et al. 2021), we demonstrate how SDE-GANs may be trained through careful weight clipping and choice of activation function.
Edit intent: meaning-changed
Original:  This gives \textit{ \textit{  up to a 10.6\times speed improvement over previous techniques . After that , when specifically training Neural SDEs as GANs (Kidger et al. 2021), we demonstrate how SDE-GANs may be trained through careful weight clipping and choice of activation function.
Revised:  This gives \textit{ \textit{and reconstructing up to a 10.6\times speed improvement over previous techniques . After that , when specifically training Neural SDEs as GANs (Kidger et al. 2021), we demonstrate how SDE-GANs may be trained through careful weight clipping and choice of activation function.
Edit intent: meaning-changed
Original:  This gives \textit{ \textit{  up to a 10.6\times speed improvement over previous techniques . After that , when specifically training Neural SDEs as GANs (Kidger et al. 2021), we demonstrate how SDE-GANs may be trained through careful weight clipping and choice of activation function.
Revised:  This gives \textit{ \textit{ Brownian motion. With this we obtain up to a 10.6\times speed improvement over previous techniques . After that , when specifically training Neural SDEs as GANs (Kidger et al. 2021), we demonstrate how SDE-GANs may be trained through careful weight clipping and choice of activation function.
Edit intent: meaning-changed
Original:  This gives \textit{ \textit{  up to a 10.6\times speed improvement over previous techniques . After that , when specifically training Neural SDEs as GANs (Kidger et al. 2021), we demonstrate how SDE-GANs may be trained through careful weight clipping and choice of activation function.
Revised:  This gives \textit{ \textit{ up to a 10.6\times speed improvement over previous techniques , which in contrast are both approximate and relatively slow. Third , when specifically training Neural SDEs as GANs (Kidger et al. 2021), we demonstrate how SDE-GANs may be trained through careful weight clipping and choice of activation function.
Edit intent: meaning-changed
Original:  This reduces computational cost (giving up to a 1.87\times speedup) , and removes the truncation errors of the double adjoint required for gradient penalty, substantially improving several test metrics.
Revised:  This reduces computational cost (giving up to a 1.87\times speedup) , and removes the numerical truncation errors associated with gradient penalty. Altogether, we outperform the state-of-the-art by substantial margins, with respect to training speed, and with respect to classification, prediction, and MMD test metrics.
Edit intent: meaning-changed