wanyu/IteraTeR_v2 huggingface dataset:
Original:  EmoXNet is an ensemble learning technique for learning convoluted facial representations, whereas EmoXNetLite is a distillation technique  for transferring the knowledge from our ensemble model to an efficient deep neural network using label-smoothen soft labels to detect expressions effectively in real-time.
Revised:  EmoXNet is an ensemble learning technique for learning convoluted facial representations, whereas EmoXNetLite is a distillation technique for transferring the knowledge from our ensemble model to an efficient deep neural network using label-smoothen soft labels for able to effectively detect expressions in real-time.
Edit intent: clarity
Original:  The ensemble model (EmoXNet) attained 85.07\% test accuracy on FER-2013 with FER+ annotations and 86.25\% test accuracy on Real-world Affective Faces Database ( RAF-DB ).
Revised:  The ensemble model (EmoXNet) helped to achieve 85.07\% test accuracy on FER-2013 with FER+ annotations and 86.25\% test accuracy on Real-world Affective Faces Database ( RAF-DB ).
Edit intent: clarity
Original:  The ensemble model (EmoXNet) attained 85.07\% test accuracy on FER-2013 with FER+ annotations and 86.25\% test accuracy on Real-world Affective Faces Database ( RAF-DB ).
Revised:  The ensemble model (EmoXNet) attained 85.07\% test accuracy on FER2013 with FER+ annotations and 86.25\% test accuracy on Real-world Affective Faces Database ( RAF-DB ).
Edit intent: fluency
Original:  The ensemble model (EmoXNet) attained 85.07\% test accuracy on FER-2013 with FER+ annotations and 86.25\% test accuracy on Real-world Affective Faces Database ( RAF-DB ).
Revised:  The ensemble model (EmoXNet) attained 85.07\% test accuracy on FER-2013 with FER+ annotations and 86.25\% test accuracy on RAF-DB ).
Edit intent: clarity
Original:  Whereas , the distilled model (EmoXNetLite) attained 82.07\% test accuracy on FER-2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB.
Revised:  Whereas , the distilled model (EmoXNetLite) showed 82.07\% test accuracy on FER-2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB.
Edit intent: clarity
Original:  Whereas , the distilled model (EmoXNetLite) attained 82.07\% test accuracy on FER-2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB.
Revised:  Whereas , the distilled model (EmoXNetLite) attained 82.07\% test accuracy on FER2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB.
Edit intent: fluency
Original:  It is known that the diameter of the associahedra of paths of length n, the classical associahedra, is 2n-6 for a large enough n.
Edit intent: coherence
Original: We study the problem of explainable clustering in the setting first formalized by Moshkovitz, Dasgupta, Rashtchian, and Frost (ICML 2020).
Revised: We study the problem of explainable clustering in the setting first formalized by Dasgupta, Frost, Moshkovitz, and Rashtchian (ICML 2020).
Edit intent: fluency
Original: We propose an efficient method for the numerical solution of a general class of two dimensional semilinear parabolic problems on polygonal meshes.
Revised: We propose an efficient method for the numerical approximation of a general class of two dimensional semilinear parabolic problems on polygonal meshes.
Edit intent: clarity
Original:  The proposed approach takes advantage of the properties of the serendipity version of the Virtual Element Method (VEM), which not only significantly reduces the number of degrees of freedom compared to the classical VEM but also, under certain conditions on the mesh  allows to approximate the nonlinear term with an interpolant in the Serendipity VEM space;
Revised:  The proposed approach takes advantage of the properties of the serendipity version of the Virtual Element Method (VEM), which not only significantly reduces the number of degrees of freedom compared to the classical VEM but also, under certain conditions on the mesh , allows to approximate the nonlinear term with an interpolant in the Serendipity VEM space;
Edit intent: fluency
Original:  An error analysis for the semi discrete formulation is carried out, and an optimal estimate for the error in the L_2-norm is obtained.
Revised:  An error analysis for the semi-discrete formulation is carried out, and an optimal estimate for the error in the L_2-norm is obtained.
Edit intent: fluency
Original:  The accuracy and efficiency of the proposed method when combined with a second order Strang operator splitting time discretization is illustrated in our numerical experiments, with high order approximations up to order 6.
Revised:  The accuracy and efficiency of the proposed method when combined with a second order Strang operator splitting time discretization is illustrated in our numerical experiments, with approximations up to order 6.
Edit intent: clarity
Original:  We describe the Jack's arbitrage process and discuss risks associated with using arbitrage bots.
Revised:  We describe Jack's arbitrage process and discuss risks associated with using arbitrage bots.
Edit intent: fluency
Original: Learned indexes like the recursive model index (RMI) have recently been introduced as a machine-learned replacement for traditional indexes with possibly game-changing results for how database indexes are constructed and used.
Revised: The recursive model index (RMI) have recently been introduced as a machine-learned replacement for traditional indexes with possibly game-changing results for how database indexes are constructed and used.
Edit intent: coherence
Original: Learned indexes like the recursive model index (RMI) have recently been introduced as a machine-learned replacement for traditional indexes with possibly game-changing results for how database indexes are constructed and used.
Revised: Learned indexes like the recursive model index (RMI) has recently been introduced as a machine-learned replacement for traditional indexes with possibly game-changing results for how database indexes are constructed and used.
Edit intent: fluency
Original: The cells and their spatial patterns in the tumor microenvironment (TME) play a key role in tumor evolution, and yet  remains an understudied topic in computational pathology.
Revised: The cells and their spatial patterns in the tumor microenvironment (TME) play a key role in tumor evolution, and yet the latter remains an understudied topic in computational pathology.
Edit intent: clarity
Original:  This study, to the best of our knowledge, is among the first to hybrid local and global graph methods to profile orchestration and interaction of cellular components.
Revised:  This study, to the best of our knowledge, is among the first to hybridize local and global graph methods to profile orchestration and interaction of cellular components.
Edit intent: clarity
Original:  To address the challenge in hematolymphoid cancers  where the cell classes in TME are unclear, we first implemented cell level unsupervised learning and identified two new cell subtypes.
Revised:  To address the challenge in hematolymphoid cancers , where the cell classes in TME are unclear, we first implemented cell level unsupervised learning and identified two new cell subtypes.
Edit intent: fluency
Original:  To address the challenge in hematolymphoid cancers  where the cell classes in TME are unclear, we first implemented cell level unsupervised learning and identified two new cell subtypes.
Revised:  To address the challenge in hematolymphoid cancers where the cell classes in TME may be unclear, we first implemented cell level unsupervised learning and identified two new cell subtypes.
Edit intent: fluency
Original:  To address the challenge in hematolymphoid cancers  where the cell classes in TME are unclear, we first implemented cell level unsupervised learning and identified two new cell subtypes.
Revised:  To address the challenge in hematolymphoid cancers where the cell classes in TME are unclear, we first implemented cell-level unsupervised learning and identified two new cell subtypes.
Edit intent: fluency
Original:  We evaluate the proposed algorithm on H \& E slides of 60 hematolymphoid neoplasm patients and further compared it with three cell level graph-based algorithms, including the global cell graph, cluster cell graph, and FLocK.
Revised:  We evaluate the proposed algorithm on H E slides of 60 hematolymphoid neoplasm patients and further compared it with three cell level graph-based algorithms, including the global cell graph, cluster cell graph, and FLocK.
Edit intent: fluency
Original:  We evaluate the proposed algorithm on H \& E slides of 60 hematolymphoid neoplasm patients and further compared it with three cell level graph-based algorithms, including the global cell graph, cluster cell graph, and FLocK.
Revised:  We evaluate the proposed algorithm on H \& E slides of 60 hematolymphoid neoplasms and further compared it with three cell level graph-based algorithms, including the global cell graph, cluster cell graph, and FLocK.
Edit intent: clarity
Original:  The proposed algorithm achieves a mean diagnosis accuracy of 0.703 with the repeated 5-fold cross-validation scheme.
Revised:  The proposed algorithm achieved a mean diagnosis accuracy of 0.703 with the repeated 5-fold cross-validation scheme.
Edit intent: fluency
Original: Semi-Supervised Learning (SSL) can decrease the amount of required labeled image data and thus the cost for deep learning.
Revised: Semi-Supervised Learning (SSL) can decrease the required amount of labeled image data and thus the cost for deep learning.
Edit intent: fluency
Original:  Most SSL methods only consider a clear distinction between classes but in many real-world datasets , this clear distinction is not given due to intra- or interobserver variability.
Revised:  Most SSL methods assume a clear distinction between classes but in many real-world datasets , this clear distinction is not given due to intra- or interobserver variability.
Edit intent: clarity
Original:  Most SSL methods only consider a clear distinction between classes but in many real-world datasets , this clear distinction is not given due to intra- or interobserver variability.
Revised:  Most SSL methods only consider a clear distinction between classes but in many real-world datasets due to intra- or interobserver variability.
Edit intent: clarity
Original:  This fuzziness of labels must be addressed as it will  limit the performance of Semi-Supervised Learning (SSL ) and deep learning in general  .
Revised:  This fuzziness of labels must be addressed as it will otherwise limit the performance of Semi-Supervised Learning (SSL ) and deep learning in general .
Edit intent: clarity
Original:  This fuzziness of labels must be addressed as it will  limit the performance of Semi-Supervised Learning (SSL ) and deep learning in general  .
Revised:  This fuzziness of labels must be addressed as it will limit the performance of SSL and deep learning in general .
Edit intent: clarity
Original:  S2C2 can estimate the fuzziness of a label and applies SSL as a classification to certainly labeled data while creating distinct clusters for images with similar but fuzzy labels .
Revised:  S2C2 can estimate the fuzziness of a label and applies SSL as a classification to certainly labeled data while partitioning the ambiguous data into clusters of visual similar images .
Edit intent: clarity
Original:  We show that S2C2 results in median 7.4 \% better F1-score for classifications and 5.4 \% lower inner distance of clusters  across multiple SSL algorithms and datasets while being more interpretable due to the fuzziness estimation of our method .
Revised:  We show that S2C2 results in median 7.4 \% better F1-score for classifications and 5.4 \% lower inner distance of clusters on average across multiple SSL algorithms and datasets while being more interpretable due to the fuzziness estimation of our method .
Edit intent: clarity
Original:  Overall, a combination of Semi-Supervised Learning with our method S2C2 leads to better handling of the fuzziness of labels and thus real-world datasets.
Revised:  Overall, a combination of Semi-Supervised Learning with our method S2C2 leads to better handling of ambiguous labels and thus real-world datasets.
Edit intent: clarity
Original:  We show that our method is superior to alternatives in terms of completeness and diversity of shapes 
Revised:  We show that our method is superior to alternatives in terms of completeness and diversity of shapes .
Edit intent: fluency
Original: Model-agnostic meta-learning (MAML) is arguably  the most popular meta-learning algorithm nowadays, given its flexibility to incorporate various model architectures and to be applied to different problems .
Revised: Model-agnostic meta-learning (MAML) is arguably one of the most popular meta-learning algorithm nowadays, given its flexibility to incorporate various model architectures and to be applied to different problems .
Edit intent: clarity
Original: Model-agnostic meta-learning (MAML) is arguably  the most popular meta-learning algorithm nowadays, given its flexibility to incorporate various model architectures and to be applied to different problems .
Revised: Model-agnostic meta-learning (MAML) is arguably the most popular meta-learning algorithms nowadays .
Edit intent: clarity
Original:  First, we find that  a large number of gradient steps are needed for the inner loop update, which contradicts the common usage of MAML for few-shot classification.
Revised:  First, we find that a large number of gradient steps in its inner loop update, which contradicts the common usage of MAML for few-shot classification.
Edit intent: clarity
Original:  First, we find that  a large number of gradient steps are needed for the inner loop update, which contradicts the common usage of MAML for few-shot classification.
Revised:  First, we find that a large number of gradient steps are needed for the inner loop update, which contradicts its common usage in few-shot classification.
Edit intent: clarity
Original:  Second, we find that MAML is sensitive to the permutation of class assignments in meta-testing : for a few-shot task of N classes, there are exponentially many ways to assign the learned initialization of the N-way classifier to the N classes, leading to an unavoidably huge variance .
Revised:  Second, we find that MAML is sensitive to the class label assignments during meta-testing : for a few-shot task of N classes, there are exponentially many ways to assign the learned initialization of the N-way classifier to the N classes, leading to an unavoidably huge variance .
Edit intent: clarity
Original:  Second, we find that MAML is sensitive to the permutation of class assignments in meta-testing : for a few-shot task of N classes, there are exponentially many ways to assign the learned initialization of the N-way classifier to the N classes, leading to an unavoidably huge variance .
Revised:  Second, we find that MAML is sensitive to the permutation of class assignments in meta-testing . Concretely, MAML meta-trains the initialization of an N-way classifier to the N classes, leading to an unavoidably huge variance .
Edit intent: clarity
Original:  Third, we investigate several ways for permutation invariance and find that learning a shared classifier initialization for all the classes performs the best.
Revised:  Third, we investigate several approaches to make MAML permutation-invariant, among which meta-training a single vector to initialize all the N weight vectors in the classification head performs the best.
Edit intent: clarity
Original:  On benchmark datasets such as MiniImageNet and TieredImageNet, our approach, which we name UNICORN-MAML, performs on a par with or even outperforms state-of-the-art algorithms, while keeping the simplicity of MAML without adding any extra sub-networks .
Revised:  On benchmark datasets like MiniImageNet and TieredImageNet, our approach, which we name UNICORN-MAML, performs on a par with or even outperforms state-of-the-art algorithms, while keeping the simplicity of MAML without adding any extra sub-networks .
Edit intent: fluency
Original: Two of the most prominent algorithms for solving unconstrained smooth games are the classical stochastic gradient descent-ascent (SGDA) and the recently introduced stochastic consensus optimization (SCO) ( Mescheder et al., 2017 ) . SGDA is known to converge to a stationary point for specific classes of games, but current convergence analyses require a bounded variance assumption.
Revised: Two of the most prominent algorithms for solving unconstrained smooth games are the classical stochastic gradient descent-ascent (SGDA) and the recently introduced stochastic consensus optimization (SCO) Mescheder et al., 2017 ) . SGDA is known to converge to a stationary point for specific classes of games, but current convergence analyses require a bounded variance assumption.
Edit intent: fluency
Original: Two of the most prominent algorithms for solving unconstrained smooth games are the classical stochastic gradient descent-ascent (SGDA) and the recently introduced stochastic consensus optimization (SCO) ( Mescheder et al., 2017 ) . SGDA is known to converge to a stationary point for specific classes of games, but current convergence analyses require a bounded variance assumption.
Revised: Two of the most prominent algorithms for solving unconstrained smooth games are the classical stochastic gradient descent-ascent (SGDA) and the recently introduced stochastic consensus optimization (SCO) ( Mescheder et al., 2017 . SGDA is known to converge to a stationary point for specific classes of games, but current convergence analyses require a bounded variance assumption.
Edit intent: fluency
Original:  We consider the case where there may be multiple local minimizers of performative risk, motivated by real world situations where the initial conditions may have significant impact on the long-term behavior of the system .
Revised:  We consider the case where there may be multiple local minimizers of performative risk, motivated by situations where the initial conditions may have significant impact on the long-term behavior of the system .
Edit intent: clarity
Original:  The implementation of our improved method, discussed in a tool paper 2 , also required to reason about the efficiency of some of the needed operations on the matrices produced by the analysis.
Revised:  The implementation of our improved method, discussed in a tool paper 2 URL also required to reason about the efficiency of some of the needed operations on the matrices produced by the analysis.
Edit intent: fluency
Original:  Strategies to provide meaningful feedback on non-polynomial programs and to ``tame'' the non-determinism of the original analysis were implemented following recent progresses [3], but required particular care to accommodate thegrowing complexity of the analysis.
Revised:  Strategies to provide meaningful feedback on non-polynomial programs and to ``tame'' the non-determinism of the original analysis were implemented following recent progresses [3], but required particular care to accommodate the growing complexity of the analysis.
Edit intent: fluency
Original: Program synthesis from input-output  examples has been a long-standing challenge , and recent works have demonstrated some success in designing deep neural networks for program synthesis.
Revised: Program synthesis from input-output (IO) examples has been a long-standing challenge , and recent works have demonstrated some success in designing deep neural networks for program synthesis.
Edit intent: clarity
Original:  We show that training on these synthesized programs further improves the prediction performance for both Karel and C program synthesis, indicating the promise of leveraging the learned program synthesizer to improve the dataset quality for input-output program synthesis  .
Revised:  Furthermore, retraining our model with these synthesized programs further improves the prediction performance for both Karel and C program synthesis, indicating the promise of leveraging the learned program synthesizer to improve the dataset quality for input-output program synthesis .
Edit intent: clarity
Original:  We hope that these polynomials will be a useful analytical tool in settings where one has a symmetric function of a collection of random or pseudorandom vectors.
Edit intent: coherence
Original:  However, traditional learning over patch-wise features using convolutional neural networks limits the model when attempting to capture global contextual information  .
Revised:  However, learning over patch-wise features using convolutional neural networks limits the model when attempting to capture global contextual information .
Edit intent: clarity
Original:  However, traditional learning over patch-wise features using convolutional neural networks limits the model when attempting to capture global contextual information  .
Revised:  However, traditional learning over patch-wise features using convolutional neural networks limits the ability of the model to capture global contextual information .
Edit intent: clarity
Original:  We also outline the limitations of existing techniques, and suggest potential future advances in this domain.
Revised:  We also outline the limitations of existing techniques, and suggest potential future research directions in this domain.
Edit intent: clarity
Original:  Replacing original goals with virtual goals generated from interaction with a trained dynamics model leads to a novel relabeling method, model-based relabeling (MBR).
Revised:  Replacing original goals with virtual goals generated from interaction with a trained dynamics model leads to a novel relabeling method, model-based relabeling (MBR).
Edit intent: fluency
Original: Automatic speaker verification (ASV), one of the most important technology for biometric identification, has been widely adopted in security-critic applications, including transaction authentication and access control.
Revised: Automatic speaker verification (ASV), one of the most important technology for biometric identification, has been widely adopted in security-critical applications, including transaction authentication and access control.
Edit intent: fluency
Original:  However, previous works have shown ASV is seriously vulnerable to recently emerged adversarial attacks, yet effective countermeasures against them are limited.
Revised:  However, previous work has shown that ASV is seriously vulnerable to recently emerged adversarial attacks, yet effective countermeasures against them are limited.
Edit intent: fluency
Original:  As the very beginning work in this direction of detecting adversarial samples for ASV, there is no reliable baseline for comparison.
Revised:  As the very beginning work in this direction of detecting adversarial samples for ASV, and hence there is a lack of established baselines for comparison.
Edit intent: clarity
Original:  So we first implement Griffin-Lim for detection and set it as our baseline.
Revised:  Consequently, we implement the Griffin-Lim for detection and set it as our baseline.
Edit intent: coherence
Original:  So we first implement Griffin-Lim for detection and set it as our baseline.
Revised:  So we first implement Griffin-Lim algorithm as the detection baseline.
Edit intent: clarity
Original:  The proposed method accomplishes effective detection performance and outperforms all the baselines in all the settings.
Revised:  The proposed approach achieves effective detection performance and outperforms all the baselines in all the settings.
Edit intent: clarity
Original:  The proposed method accomplishes effective detection performance and outperforms all the baselines in all the settings.
Revised:  The proposed method accomplishes effective detection performance that outperforms all the baselines in all the settings.
Edit intent: clarity
Original:  We also show  the neural vocoder adopted in the detection framework is dataset independent .
Revised:  We also show that the neural vocoder adopted in the detection framework is dataset independent .
Edit intent: fluency
Original:  We also show  the neural vocoder adopted in the detection framework is dataset independent .
Revised:  We also show the neural vocoder adopted in the detection framework is dataset-independent .
Edit intent: fluency