wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this work, we present a fast and accurate simulation framework to enable evaluation and re-training of large-scale DNNs on resistive crossbar based hardware fabrics.
Revised:  In this work, we present a fast and accurate simulation framework to evaluate large-scale DNNs on resistive crossbar based hardware fabrics.
Edit intent: clarity
Original:  We propose a Fast Crossbar Model (FCM) to accurately capture the errors arising due to crossbar non-idealities while being four-to-five orders of magnitude faster than circuit simulation.
Revised:  We propose a Fast Crossbar Model (FCM) that accurately captures the errors arising due to crossbar non-idealities while being four-to-five orders of magnitude faster than circuit simulation.
Edit intent: fluency
Original:  RxNN is based on the popular Caffe machine learning framework , and we use it to evaluate a suite of  large-scale DNNs developed for the ImageNet Challenge (ILSVRC) . Our experiments reveal that resistive crossbar non-idealities can lead to significant accuracy degradations (9.6\%-32\%) for these large-scale DNNs.
Revised:  RxNN is based on the popular Caffe machine learning framework and use it to evaluate a suite of large-scale DNNs developed for the ImageNet Challenge (ILSVRC) . Our experiments reveal that resistive crossbar non-idealities can lead to significant accuracy degradations (9.6\%-32\%) for these large-scale DNNs.
Edit intent: coherence
Original:  RxNN is based on the popular Caffe machine learning framework , and we use it to evaluate a suite of  large-scale DNNs developed for the ImageNet Challenge (ILSVRC) . Our experiments reveal that resistive crossbar non-idealities can lead to significant accuracy degradations (9.6\%-32\%) for these large-scale DNNs.
Revised:  RxNN is based on the popular Caffe machine learning framework , and we use it to evaluate a suite of large-scale DNNs developed for the ImageNet Challenge . Our experiments reveal that resistive crossbar non-idealities can lead to significant accuracy degradations (9.6\%-32\%) for these large-scale DNNs.
Edit intent: clarity
Original: We introduce a meta-algorithm , called non-Euclidean upgrading (NEU), which learns algorithm-specific geometries to improve the training and validation set performance of a wide class of learning algorithms.
Revised: We introduce a new meta-learning procedure , called non-Euclidean upgrading (NEU), which learns algorithm-specific geometries to improve the training and validation set performance of a wide class of learning algorithms.
Edit intent: clarity
Original: We introduce a meta-algorithm , called non-Euclidean upgrading (NEU), which learns algorithm-specific geometries to improve the training and validation set performance of a wide class of learning algorithms.
Revised: We introduce a meta-algorithm , called non-Euclidean upgrading (NEU), which learns algorithm-specific geometries to improve the training and validation set performance of learning algorithms.
Edit intent: clarity
Original: We introduce a new meta-learning procedure, called non-Euclidean upgrading (NEU), which learns algorithm-specific geometries by deforming the ambient space until the algorithm can achieve optimal performance.
Revised: Effective feature representation is key to the predictive performance of any algorithm. This paper introduces a meta-procedure, called Non-Euclidean Upgrading (NEU), which learns algorithm-specific geometries by deforming the ambient space until the algorithm can achieve optimal performance.
Edit intent: clarity
Original: The concept of graph energies has remained mostly a theoretical idea with no practical applications in the world of complex and social networks.
Edit intent: coherence
Original:  Graph energy is the energy of the matrix representation of the graph, where matrix energy is the sum of absolute values of the eigenvalues of the matrix .
Revised:  Graph energy is the energy of the matrix representation of the graph, where the energy of a matrix is the sum of absolute values of the eigenvalues of the matrix .
Edit intent: clarity
Original:  Graph energy is the energy of the matrix representation of the graph, where matrix energy is the sum of absolute values of the eigenvalues of the matrix .
Revised:  Graph energy is the energy of the matrix representation of the graph, where matrix energy is the sum of singular values of the eigenvalues of the matrix .
Edit intent: clarity
Original:  In this work we investigate the usefulnes and usability of graph energies and their entropies in describing a wide spectrum of networks.
Revised:  In this paper we investigate the usefulnes and usability of graph energies and their entropies in describing a wide spectrum of networks.
Edit intent: clarity
Original:  In this work we investigate the usefulnes and usability of graph energies and their entropies in describing a wide spectrum of networks.
Revised:  In this work we investigate the usefulness of the concept of graph energy in the context of large, complex networks.
Edit intent: clarity
Original:  We show that when graph energies are applied to local egocentric networks within larger topological structures , the values of these energies correlate strongly with several centrality indexes .
Revised:  We show that when graph energies are applied to local egocentric networks , the values of these energies correlate strongly with several centrality indexes .
Edit intent: clarity
Original:  We show that when graph energies are applied to local egocentric networks within larger topological structures , the values of these energies correlate strongly with several centrality indexes .
Revised:  We show that when graph energies are applied to local egocentric networks within larger topological structures , the values of these energies correlate strongly with vertex centrality measures .
Edit intent: clarity
Original:  In particular, for some network models, graph energies tend to correlate very strongly with the betweenness and the eigencentrality of nodes .
Revised:  In particular, for some network models, graph energies tend to correlate strongly with the betweenness and the eigencentrality of nodes .
Edit intent: clarity
Original:  In particular, for some network models, graph energies tend to correlate very strongly with the betweenness and the eigencentrality of nodes .
Revised:  In particular, for some network models, graph energies tend to correlate very strongly with the betweenness and the eigencentrality of vertices .
Edit intent: clarity
Original:  As the  computation of these centrality measures is expensive and requires global processing of a network, our research opens the possibility of devising efficient algorithms for the estimation of these centrality metrics based only on local information.
Revised:  As the computation of these centrality measures is expensive and requires global processing of a network, our research opens the possibility of devising efficient algorithms for the estimation of these centrality measures based only on local information.
Edit intent: clarity
Original: We consider the quantization of deep neural networks (DNNs) to produce low-precision models for efficient inference of fixed-point operations.
Revised: We consider learning deep neural networks (DNNs) to produce low-precision models for efficient inference of fixed-point operations.
Edit intent: clarity
Original: We consider the quantization of deep neural networks (DNNs) to produce low-precision models for efficient inference of fixed-point operations.
Revised: We consider the quantization of deep neural networks (DNNs) that consist of low-precision models for efficient inference of fixed-point operations.
Edit intent: clarity
Original: We consider the quantization of deep neural networks (DNNs) to produce low-precision models for efficient inference of fixed-point operations.
Revised: We consider the quantization of deep neural networks (DNNs) to produce low-precision weights and activations for efficient inference of fixed-point operations.
Edit intent: clarity
Original:  Compared to previous approaches to training quantized DNNs directly under the constraints of low-precision  weights and activations , we learn the quantization of DNNs with minimal quantization loss through regularization.
Revised:  In training low-precision weights and activations , we learn the quantization of DNNs with minimal quantization loss through regularization.
Edit intent: clarity
Original:  In particular, we introduce the learnable regularization coefficient to find accurate low-precision models efficiently in training.
Revised:  In particular, we propose using a learnable regularization coefficient to find accurate low-precision models efficiently in training.
Edit intent: clarity
Original:  In our experiments, the proposed scheme yields the state-of-the-art low-precision models of AlexNet and ResNet-18, which have better accuracy than their previously available low-precision models.
Revised:  In our experiments, the proposed method yields low-precision models of AlexNet and ResNet-18, which have better accuracy than their previously available low-precision models.
Edit intent: clarity
Original:  We propose an alternative solution to highlighting  main components of a network using a diffusion process based on exchanges ;
Revised:  We propose an alternative solution to highlighting the main components of a network using a diffusion process based on exchanges ;
Edit intent: fluency
Original:  We propose an alternative solution to highlighting  main components of a network using a diffusion process based on exchanges ;
Revised:  We propose an alternative solution to highlighting main components of a network using a diffusion process based on exchanges :
Edit intent: fluency
Original:  This process allows to evaluate the importance not only at the vertices level but also at the regrouping level.
Revised:  This process allows to evaluate the importance not only of the vertices but also of the regrouping level.
Edit intent: fluency
Original:  To model the diffusion process, we extend the concept of hypergraphs that are family of sets to family of multisets, that we call hb-graphs  .
Revised:  To model the diffusion process, we extend the concept of hypergraphs that are families of sets to family of multisets, that we call hb-graphs .
Edit intent: fluency
Original:  To model the diffusion process, we extend the concept of hypergraphs that are family of sets to family of multisets, that we call hb-graphs  .
Revised:  To model the diffusion process, we extend the concept of hypergraphs that are family of sets to families of multisets, that we call hb-graphs .
Edit intent: fluency
Original: The L^2-orthogonal projection  is an important mathematical tool in scientific computing and numerical analysis , which has been widely applied in many fields such as linear least squares problems, eigenvalue problems, ill-posed problems, and randomized algorithms.
Revised: The L^2-orthogonal projection is an important mathematical tool , which has been widely applied in many fields such as linear least squares problems, eigenvalue problems, ill-posed problems, and randomized algorithms.
Edit intent: clarity
Original:  The new results contain some sharper counterparts of the existing bounds . Numerical examples are also given to illustrate our theoretical results.
Revised:  The new bounds contain some sharper counterparts of the existing bounds . Numerical examples are also given to illustrate our theoretical results.
Edit intent: fluency
Original:  The new results contain some sharper counterparts of the existing bounds . Numerical examples are also given to illustrate our theoretical results.
Revised:  The new results contain some sharper counterparts of the existing ones . Numerical examples are also given to illustrate our theoretical results.
Edit intent: clarity
Original: Over the past decades, the Moore-Penrose inverse  has been extensively investigated and widely applied in many fields  .
Revised: The Moore-Penrose inverse has been extensively investigated and widely applied in many fields .
Edit intent: clarity
Original:  One reason for this interest is that the Moore-Penrose inverse can succinctly express some important geometric constructions in finite-dimensional spaces, such as the orthogonal projection onto a subspace and the linear least squares problem.
Revised:  One reason for the interest is that the Moore-Penrose inverse can succinctly express some important geometric constructions in finite-dimensional spaces, such as the orthogonal projection onto a subspace and the linear least squares problem.
Edit intent: coherence
Original:  However, the entries of a matrix will seldom be known exactly in numerical applications, so it is necessary to develop some bounds to characterize the effects of matrix perturbation.
Edit intent: coherence
Original: The truncated singular value decomposition (TSVD) may be used to find the solution of the linear discrete ill-posed problem A x \approx b using Tikhonov regularization . Regularization parameter \alpha^2 balances between the sizes of the fit to data functional \|A x - b\|_2^2 and the regularization term \| x\|_2^2.
Revised: The truncated singular value decomposition may be used to find the solution of the linear discrete ill-posed problem A x \approx b using Tikhonov regularization . Regularization parameter \alpha^2 balances between the sizes of the fit to data functional \|A x - b\|_2^2 and the regularization term \| x\|_2^2.
Edit intent: clarity
Original: The truncated singular value decomposition (TSVD) may be used to find the solution of the linear discrete ill-posed problem A x \approx b using Tikhonov regularization . Regularization parameter \alpha^2 balances between the sizes of the fit to data functional \|A x - b\|_2^2 and the regularization term \| x\|_2^2.
Revised: The truncated singular value decomposition (TSVD) may be used to find the solution of linear discrete ill-posed problem A x \approx b using Tikhonov regularization . Regularization parameter \alpha^2 balances between the sizes of the fit to data functional \|A x - b\|_2^2 and the regularization term \| x\|_2^2.
Edit intent: fluency
Original: The truncated singular value decomposition (TSVD) may be used to find the solution of the linear discrete ill-posed problem A x \approx b using Tikhonov regularization . Regularization parameter \alpha^2 balances between the sizes of the fit to data functional \|A x - b\|_2^2 and the regularization term \| x\|_2^2.
Revised: The truncated singular value decomposition (TSVD) may be used to find the solution of the linear discrete ill-posed problems in conjunction with Tikhonov regularization and requires the estimation of a regularization parameter that balances between the sizes of the fit to data functional \|A x - b\|_2^2 and the regularization term \| x\|_2^2.
Edit intent: clarity
Original: The truncated singular value decomposition (TSVD) may be used to find the solution of the linear discrete ill-posed problem A x \approx b using Tikhonov regularization . Regularization parameter \alpha^2 balances between the sizes of the fit to data functional \|A x - b\|_2^2 and the regularization term \| x\|_2^2.
Revised: The truncated singular value decomposition (TSVD) may be used to find the solution of the linear discrete ill-posed problem A x \approx b using Tikhonov regularization . Regularization parameter \alpha^2 balances between the sizes of the fit to data function and the regularization term \| x\|_2^2.
Edit intent: coherence
Original:  Minimization of the unbiased predictive risk estimation (UPRE) function is one suggested method to find \alpha when the noise \eta in the measurements b is assumed to be normally distributed with white noise variance\sigma^2.
Revised:  Minimization of the unbiased predictive risk estimator is one suggested method to find \alpha when the noise \eta in the measurements b is assumed to be normally distributed with white noise variance\sigma^2.
Edit intent: clarity
Original:  Minimization of the unbiased predictive risk estimation (UPRE) function is one suggested method to find \alpha when the noise \eta in the measurements b is assumed to be normally distributed with white noise variance\sigma^2.
Revised:  Minimization of the unbiased predictive risk estimation (UPRE) function is one suggested method to find \alpha when the noise in the measurements b is assumed to be normally distributed with white noise variance\sigma^2.
Edit intent: clarity
Original:  Minimization of the unbiased predictive risk estimation (UPRE) function is one suggested method to find \alpha when the noise \eta in the measurements b is assumed to be normally distributed with white noise variance\sigma^2.
Revised:  Minimization of the unbiased predictive risk estimation (UPRE) function is one suggested method to find \alpha when the noise \eta in the measurements is normally distributed with white noise variance\sigma^2.
Edit intent: clarity
Original:  For the analysis it is sufficient to assume that the discrete Picard condition is satisfied for exact data but that noise contaminates the measured data coefficients s_i for some \ell, E(s^2_i)=\sigma^2 for i>\ell, and that the problem is mildly, moderately or severely ill-posed.
Revised:  For the analysis it is sufficient to assume that the discrete Picard condition is satisfied for exact data and that noise completely contaminates the measured data coefficients s_i for some \ell, E(s^2_i)=\sigma^2 for i>\ell, and that the problem is mildly, moderately or severely ill-posed.
Edit intent: clarity
Original:  Supporting results are contrasted with those obtained using the method of generalized cross validation that is another often suggested method for estimating \alpha.
Revised:  Supporting results are compared with those obtained using the method of generalized cross validation that is another often suggested method for estimating \alpha.
Edit intent: fluency
Original: In this work, we study solving (decoupled) forward-backward stochastic differential equations FBSDEs numerically using the regression trees.
Revised: In this work, we study solving (decoupled) forward-backward stochastic differential equations (FBSDEs) numerically using the regression trees.
Edit intent: clarity
Original:  For the one-step scheme, we apply firstly the the general theta-discretization for the time-integrands and show then how to use regression tree-based methods to solve the resulting conditional expectations.
Revised:  Based on the general theta-discretization for the time-integrands and show then how to use regression tree-based methods to solve the resulting conditional expectations.
Edit intent: coherence
Original:  For the one-step scheme, we apply firstly the the general theta-discretization for the time-integrands and show then how to use regression tree-based methods to solve the resulting conditional expectations.
Revised:  For the one-step scheme, we apply firstly the the general theta-discretization for the time-integrands , we show how to efficiently use regression tree-based methods to solve the resulting conditional expectations.
Edit intent: clarity
Original:  For the multistep scheme, we use Lagrange interpolating polynomials with values at multiple time levels to approximate the time-integrands, the resulting conditional expectation are approximated by using the tree-based approaches.
Edit intent: coherence
Original:  Several numerical experiments also with high-dimensional problems are included to demonstrate the accuracy and the performance of  tree-based approach for a high-dimensional problem .
Revised:  Several numerical experiments also with high-dimensional problems are included to demonstrate the accuracy and the performance of the tree-based approach for a high-dimensional problem .
Edit intent: fluency
Original:  Several numerical experiments also with high-dimensional problems are included to demonstrate the accuracy and the performance of  tree-based approach for a high-dimensional problem .
Revised:  Several numerical experiments also with high-dimensional problems are included to demonstrate the accuracy and the performance of tree-based approach .
Edit intent: clarity
Original:  For the applicability of FBSDEs in financial problems, we apply our tree-based approach to solve the Heston stochastic volatility model  .
Revised:  For the applicability of FBSDEs in financial problems, we apply our tree-based approach to the Heston stochastic volatility model .
Edit intent: clarity
Original:  Several numerical experiments also with high-dimensional problems are included to demonstrate the accuracy and the performance of the tree-based approach.
Revised:  Several numerical experiments including high-dimensional problems are included to demonstrate the accuracy and the performance of the tree-based approach.
Edit intent: coherence
Original:  Several numerical experiments also with high-dimensional problems are included to demonstrate the accuracy and the performance of the tree-based approach.
Revised:  Several numerical experiments also with high-dimensional problems are provided to demonstrate the accuracy and the performance of the tree-based approach.
Edit intent: clarity
Original:  Several numerical experiments also with high-dimensional problems are included to demonstrate the accuracy and the performance of the tree-based approach.
Revised:  Several numerical experiments also with high-dimensional problems are included to demonstrate the accuracy and performance of the tree-based approach.
Edit intent: fluency
Original:  For the applicability of FBSDEs in financial problems, we apply our tree-based approach to the Heston stochastic volatility model and the high-dimensional Rainbow option pricing problem .
Revised:  For the applicability of FBSDEs in financial problems, we apply our tree-based approach to the Heston stochastic volatility model , the high-dimensional Rainbow option pricing problem .
Edit intent: fluency
Original:  In the presence of heterogeneity partially synchronized states remininscent to collective chaos, QPS and M-QPS persist.
Revised:  In the presence of heterogeneity partially synchronized states reminiscent to collective chaos, QPS and M-QPS persist.
Edit intent: fluency
Original:  In addition, the presence of heterogeneity greatly amplifies the differences between the incoherence stability boundaries of excitaton and inhibition.
Revised:  In addition, the presence of heterogeneity greatly amplifies the differences between the incoherence stability boundaries of excitation and inhibition.
Edit intent: fluency
Original: Users are often ill-equipped to identify online advertisements that masquerade as non-advertising content .
Revised: Online advertisements that masquerade as non-advertising content .
Edit intent: clarity
Original:  In this paper, we examined disclosures within affiliate marketing, an endorsement-based advertising strategy used by social media content creators.
Revised:  In this paper, we studied disclosures within affiliate marketing, an endorsement-based advertising strategy used by social media content creators.
Edit intent: clarity
Original:  We asked whether content creators follow the FTC's disclosure guidelines, how they word the disclosures, and whether these disclosure help users identify affiliate marketing content as advertisements.
Revised:  We examined whether content creators follow the FTC's disclosure guidelines, how they word the disclosures, and whether these disclosure help users identify affiliate marketing content as advertisements.
Edit intent: clarity
Original:  We asked whether content creators follow the FTC's disclosure guidelines, how they word the disclosures, and whether these disclosure help users identify affiliate marketing content as advertisements.
Revised:  We asked whether content creators follow the FTC's disclosure guidelines, how they word the disclosures, and whether these disclosures help users identify affiliate marketing content as advertisements.
Edit intent: fluency