wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We also devise algorithms for the computation of this invariantand we furthermore show that the persistent cup-length is 2-Lipschitz continuous with respect to the homotopy interleaving and Gromov-Hausdorff distances .
Revised:  We show that the Persistent Cup-Length can be computed from a family of representative cocycles and devise a polynomial time algorithm for the computation of this invariantand we furthermore show that the persistent cup-length is 2-Lipschitz continuous with respect to the homotopy interleaving and Gromov-Hausdorff distances .
Edit intent: meaning-changed
Original:  We also devise algorithms for the computation of this invariantand we furthermore show that the persistent cup-length is 2-Lipschitz continuous with respect to the homotopy interleaving and Gromov-Hausdorff distances .
Revised:  We also devise algorithms for the computation of the Persistent Cup-Length invariant. We furthermore show that the persistent cup-length is 2-Lipschitz continuous with respect to the homotopy interleaving and Gromov-Hausdorff distances .
Edit intent: clarity
Original:  We also devise algorithms for the computation of this invariantand we furthermore show that the persistent cup-length is 2-Lipschitz continuous with respect to the homotopy interleaving and Gromov-Hausdorff distances .
Revised:  We also devise algorithms for the computation of this invariantand we furthermore show that this invariant is stable under suitable interleaving-type distances. Along the way, we identify an invariant which we call the Cup-Length Diagram, which is stronger than persistent cup-length is 2-Lipschitz continuous with respect to the homotopy interleaving and Gromov-Hausdorff distances .
Edit intent: meaning-changed
Original:  Compared with self-supervised baselines, which learning tasks one by one without taking any technique, we improve the image classification top-1 accuracy by 1.60\% on CIFAR100 and 2.86\% on ImageNet-Sub  under 10 incremental steps setting.
Revised:  Compared with the baselines, which learning tasks one by one without taking any technique, we improve the image classification top-1 accuracy by 1.60\% on CIFAR100 and 2.86\% on ImageNet-Sub under 10 incremental steps setting.
Edit intent: clarity
Original:  Compared with self-supervised baselines, which learning tasks one by one without taking any technique, we improve the image classification top-1 accuracy by 1.60\% on CIFAR100 and 2.86\% on ImageNet-Sub  under 10 incremental steps setting.
Revised:  Compared with self-supervised baselines, which learning tasks without taking any technique, we improve the image classification top-1 accuracy by 1.60\% on CIFAR100 and 2.86\% on ImageNet-Sub under 10 incremental steps setting.
Edit intent: clarity
Original:  Compared with self-supervised baselines, which learning tasks one by one without taking any technique, we improve the image classification top-1 accuracy by 1.60\% on CIFAR100 and 2.86\% on ImageNet-Sub  under 10 incremental steps setting.
Revised:  Compared with self-supervised baselines, which learning tasks one by one without taking any technique, we improve the image classification top-1 accuracy by 1.60\% on CIFAR100 , 2.86\% on ImageNet-Sub under 10 incremental steps setting.
Edit intent: coherence
Original:  Compared with self-supervised baselines, which learning tasks one by one without taking any technique, we improve the image classification top-1 accuracy by 1.60\% on CIFAR100 and 2.86\% on ImageNet-Sub  under 10 incremental steps setting.
Revised:  Compared with self-supervised baselines, which learning tasks one by one without taking any technique, we improve the image classification top-1 accuracy by 1.60\% on CIFAR100 and 2.86\% on ImageNet-Sub and 1.29\% on ImageNet-Full under 10 incremental steps setting.
Edit intent: meaning-changed
Original:  We show that, when a classification network istrained with the cross-entropy loss, its final classi-fication layer forms aGenerative-Discriminativepairwith a generative classifier based on a specificdistribution of penultimate activations.
Revised:  We show that, when a classification network is trained with the cross-entropy loss, its final classi-fication layer forms aGenerative-Discriminativepairwith a generative classifier based on a specificdistribution of penultimate activations.
Edit intent: fluency
Original:  We show that, when a classification network istrained with the cross-entropy loss, its final classi-fication layer forms aGenerative-Discriminativepairwith a generative classifier based on a specificdistribution of penultimate activations.
Revised:  We show that, when a classification network istrained with the cross-entropy loss, its final classification layer forms a Generative-Discriminative pair with a generative classifier based on a specificdistribution of penultimate activations.
Edit intent: fluency
Original:  We show that, when a classification network istrained with the cross-entropy loss, its final classi-fication layer forms aGenerative-Discriminativepairwith a generative classifier based on a specificdistribution of penultimate activations.
Revised:  We show that, when a classification network istrained with the cross-entropy loss, its final classi-fication layer forms aGenerative-Discriminativepairwith a generative classifier based on a specific distribution of penultimate activations.
Edit intent: fluency
Original:  More im-portantly , the distribution is parameterized by theweights of the final fully-connected layer, and canbe considered as a generative model that synthe-sizes the penultimate activations without feedinginput data.
Revised:  More importantly , the distribution is parameterized by theweights of the final fully-connected layer, and canbe considered as a generative model that synthe-sizes the penultimate activations without feedinginput data.
Edit intent: fluency
Original:  More im-portantly , the distribution is parameterized by theweights of the final fully-connected layer, and canbe considered as a generative model that synthe-sizes the penultimate activations without feedinginput data.
Revised:  More im-portantly , the distribution is parameterized by the weights of the final fully-connected layer, and canbe considered as a generative model that synthe-sizes the penultimate activations without feedinginput data.
Edit intent: fluency
Original:  More im-portantly , the distribution is parameterized by theweights of the final fully-connected layer, and canbe considered as a generative model that synthe-sizes the penultimate activations without feedinginput data.
Revised:  More im-portantly , the distribution is parameterized by theweights of the final fully-connected layer, and can be considered as a generative model that synthe-sizes the penultimate activations without feedinginput data.
Edit intent: fluency
Original:  More im-portantly , the distribution is parameterized by theweights of the final fully-connected layer, and canbe considered as a generative model that synthe-sizes the penultimate activations without feedinginput data.
Revised:  More im-portantly , the distribution is parameterized by theweights of the final fully-connected layer, and canbe considered as a generative model that synthesizes the penultimate activations without feedinginput data.
Edit intent: fluency
Original:  More im-portantly , the distribution is parameterized by theweights of the final fully-connected layer, and canbe considered as a generative model that synthe-sizes the penultimate activations without feedinginput data.
Revised:  More im-portantly , the distribution is parameterized by theweights of the final fully-connected layer, and canbe considered as a generative model that synthe-sizes the penultimate activations without feeding input data.
Edit intent: fluency
Original:  We empirically demonstrate that thisgenerative model enables stable knowledge dis-tillation in the presence of domain shift, and cantransfer knowledge from a classifier to variationalautoencoders and generative adversarial networksfor class-conditional image generation.
Revised:  We empirically demonstrate that this generative model enables stable knowledge dis-tillation in the presence of domain shift, and cantransfer knowledge from a classifier to variationalautoencoders and generative adversarial networksfor class-conditional image generation.
Edit intent: fluency
Original:  We empirically demonstrate that thisgenerative model enables stable knowledge dis-tillation in the presence of domain shift, and cantransfer knowledge from a classifier to variationalautoencoders and generative adversarial networksfor class-conditional image generation.
Revised:  We empirically demonstrate that thisgenerative model enables stable knowledge distillation in the presence of domain shift, and cantransfer knowledge from a classifier to variationalautoencoders and generative adversarial networksfor class-conditional image generation.
Edit intent: fluency
Original:  We empirically demonstrate that thisgenerative model enables stable knowledge dis-tillation in the presence of domain shift, and cantransfer knowledge from a classifier to variationalautoencoders and generative adversarial networksfor class-conditional image generation.
Revised:  We empirically demonstrate that thisgenerative model enables stable knowledge dis-tillation in the presence of domain shift, and can transfer knowledge from a classifier to variationalautoencoders and generative adversarial networksfor class-conditional image generation.
Edit intent: fluency
Original:  We empirically demonstrate that thisgenerative model enables stable knowledge dis-tillation in the presence of domain shift, and cantransfer knowledge from a classifier to variationalautoencoders and generative adversarial networksfor class-conditional image generation.
Revised:  We empirically demonstrate that thisgenerative model enables stable knowledge dis-tillation in the presence of domain shift, and cantransfer knowledge from a classifier to variational autoencoders and generative adversarial networksfor class-conditional image generation.
Edit intent: fluency
Original:  We empirically demonstrate that thisgenerative model enables stable knowledge dis-tillation in the presence of domain shift, and cantransfer knowledge from a classifier to variationalautoencoders and generative adversarial networksfor class-conditional image generation.
Revised:  We empirically demonstrate that thisgenerative model enables stable knowledge dis-tillation in the presence of domain shift, and cantransfer knowledge from a classifier to variationalautoencoders and generative adversarial networks for class-conditional image generation.
Edit intent: fluency
Original:  Standard methodologies that rely solely on the extraction  of facial features often fall short of accurate emotion prediction in cases where the aforementioned source of affective information is inaccessible due to head/body orientation, low resolution and poor illumination.
Revised:  Poor illumination conditions, head/body orientation and low image resolution constitute factors that can potentially hinder performance in case of methodologies that solely rely on the extraction of facial features often fall short of accurate emotion prediction in cases where the aforementioned source of affective information is inaccessible due to head/body orientation, low resolution and poor illumination.
Edit intent: meaning-changed
Original:  Standard methodologies that rely solely on the extraction  of facial features often fall short of accurate emotion prediction in cases where the aforementioned source of affective information is inaccessible due to head/body orientation, low resolution and poor illumination.
Revised:  Standard methodologies that rely solely on the extraction and analysis of facial features often fall short of accurate emotion prediction in cases where the aforementioned source of affective information is inaccessible due to head/body orientation, low resolution and poor illumination.
Edit intent: clarity
Original:  Standard methodologies that rely solely on the extraction  of facial features often fall short of accurate emotion prediction in cases where the aforementioned source of affective information is inaccessible due to head/body orientation, low resolution and poor illumination. We aspire to alleviate this problem by leveraging bodily as well as contextual features, as part of a broader emotion recognition framework.
Revised:  Standard methodologies that rely solely on the extraction of facial features . In order to alleviate this problem by leveraging bodily as well as contextual features, as part of a broader emotion recognition framework.
Edit intent: coherence
Original: While the original Ait-Sahalia interest rate model has been found considerable use as a model for describing time series evolution of interest rates, it may not possess adequate specifications to explain responses of interest rates to empirical phenomena such as volatility 'skews' and 'smiles', jump behaviour, market regulatory lapses, economic crisis, financial clashes  among others collectively.
Revised: While the original Ait-Sahalia interest rate model has been found considerable use as a model for describing time series evolution of interest rates, it may not possess adequate specifications to explain responses of interest rates to empirical phenomena such as volatility 'skews' and 'smiles', jump behaviour, market regulatory lapses, economic crisis, financial clashes , political instability, among others collectively.
Edit intent: meaning-changed
Original:  Moreover, due to lack of a closed-form solution to the proposed model, we employ several new truncated EM techniques to numerically study this model and justify the scheme within Monte Carlo framework to compute some financial quantities such as a bond and a path-dependent barrier option.
Revised:  Moreover, due to lack of a closed-form solution to the proposed model, we employ several new truncated EM techniques to numerically study this model and justify the scheme within Monte Carlo framework to compute some financial quantities such as a bond and a barrier option.
Edit intent: clarity
Original:  From the perspective of Bayesian probability, this paper analyzes how to improve the performance of imbalanced classification by adjusting the distance between classes and the distribution within a class and proposes a time-series fault diagnosis model based on deep metric learning . As a core of deep metric learning, a novel quadruplet data pair design considering imbalance class is proposed with reference to traditional deep metric learning . Based on such data pair, this paper proposes a quadruplet loss function which takes into account the inter-class distance and the intra-class data distribution , and pays special attention to imbalanced sample pairs .
Revised:  From the perspective of Bayesian probability, this paper analyzes how to improve the performance of imbalanced classification by adjusting the distance between classes and the distribution within a class and proposes a time-series fault diagnosis model based on deep metric learning model, where imbalanced fault data and a quadruplet data pair design considering imbalance class is proposed with reference to traditional deep metric learning . Based on such data pair, this paper proposes a quadruplet loss function which takes into account the inter-class distance and the intra-class data distribution , and pays special attention to imbalanced sample pairs .
Edit intent: coherence
Original:  As a core of deep metric learning, a novel quadruplet data pair design considering imbalance class is proposed with reference to traditional deep metric learning . Based on such data pair, this paper proposes a quadruplet loss function which takes into account the inter-class distance and the intra-class data distribution , and pays special attention to imbalanced sample pairs .
Revised:  As a core of deep metric learning, a novel quadruplet data pair design manner are considered . Based on such data pair, this paper proposes a quadruplet loss function which takes into account the inter-class distance and the intra-class data distribution , and pays special attention to imbalanced sample pairs .
Edit intent: clarity
Original:  As a core of deep metric learning, a novel quadruplet data pair design considering imbalance class is proposed with reference to traditional deep metric learning . Based on such data pair, this paper proposes a quadruplet loss function which takes into account the inter-class distance and the intra-class data distribution , and pays special attention to imbalanced sample pairs .
Revised:  As a core of deep metric learning, a novel quadruplet data pair design considering imbalance class is proposed with reference to traditional deep metric learning . Based on such data pair, a quadruplet loss function which takes into account the inter-class distance and the intra-class data distribution , and pays special attention to imbalanced sample pairs .
Edit intent: coherence
Original:  As a core of deep metric learning, a novel quadruplet data pair design considering imbalance class is proposed with reference to traditional deep metric learning . Based on such data pair, this paper proposes a quadruplet loss function which takes into account the inter-class distance and the intra-class data distribution , and pays special attention to imbalanced sample pairs .
Revised:  As a core of deep metric learning, a novel quadruplet data pair design considering imbalance class is proposed with reference to traditional deep metric learning . Based on such data pair, this paper proposes a quadruplet loss function which takes into account the inter-class distance and the intra-class data distribution are proposed. This quadruplet loss pays special attention to imbalanced sample pairs .
Edit intent: coherence
Original:  As a core of deep metric learning, a novel quadruplet data pair design considering imbalance class is proposed with reference to traditional deep metric learning . Based on such data pair, this paper proposes a quadruplet loss function which takes into account the inter-class distance and the intra-class data distribution , and pays special attention to imbalanced sample pairs .
Revised:  As a core of deep metric learning, a novel quadruplet data pair design considering imbalance class is proposed with reference to traditional deep metric learning . Based on such data pair, this paper proposes a quadruplet loss function which takes into account the inter-class distance and the intra-class data distribution , and pays special attention to imbalanced sample pair .
Edit intent: fluency
Original:  In this paper , we present a statistical framework for LFI that unifies classical statistics with modern machine learning to: (1)  construct frequentist confidence sets and hypothesis tests with finite-sample guarantees of nominal coverage (type I error control) and power , and (2) provide rigorous diagnostics for assessing empirical coverage over the entire parameter space.
Revised:  This paper presents a statistical framework for LFI that unifies classical statistics with modern machine learning to: (1) construct frequentist confidence sets and hypothesis tests with finite-sample guarantees of nominal coverage (type I error control) and power , and (2) provide rigorous diagnostics for assessing empirical coverage over the entire parameter space.
Edit intent: coherence
Original:  In this paper , we present a statistical framework for LFI that unifies classical statistics with modern machine learning to: (1)  construct frequentist confidence sets and hypothesis tests with finite-sample guarantees of nominal coverage (type I error control) and power , and (2) provide rigorous diagnostics for assessing empirical coverage over the entire parameter space.
Revised:  In this paper , we present a statistical framework for LFI that unifies classical statistics with modern machine learning to: (1) efficiently construct frequentist confidence sets and hypothesis tests with finite-sample guarantees of nominal coverage (type I error control) and power , and (2) provide rigorous diagnostics for assessing empirical coverage over the entire parameter space.
Edit intent: clarity
Original:  In this paper , we present a statistical framework for LFI that unifies classical statistics with modern machine learning to: (1)  construct frequentist confidence sets and hypothesis tests with finite-sample guarantees of nominal coverage (type I error control) and power , and (2) provide rigorous diagnostics for assessing empirical coverage over the entire parameter space.
Revised:  In this paper , we present a statistical framework for LFI that unifies classical statistics with modern machine learning to: (1) construct frequentist confidence sets and hypothesis tests with finite-sample guarantees of nominal coverage (type I error control) and power ; (2) provide rigorous diagnostics for assessing empirical coverage over the entire parameter space.
Edit intent: coherence
Original:  In this paper , we present a statistical framework for LFI that unifies classical statistics with modern machine learning to: (1)  construct frequentist confidence sets and hypothesis tests with finite-sample guarantees of nominal coverage (type I error control) and power , and (2) provide rigorous diagnostics for assessing empirical coverage over the entire parameter space.
Revised:  In this paper , we present a statistical framework for LFI that unifies classical statistics with modern machine learning to: (1) construct frequentist confidence sets and hypothesis tests with finite-sample guarantees of nominal coverage (type I error control) and power , and (2) provide practical diagnostics for assessing empirical coverage over the entire parameter space.
Edit intent: clarity
Original: Meta-reinforcement learning (RL) can be used to train policies that quickly adapt to new tasks with orders of magnitude less data than standard RL, but this fast adaptation often comes at the cost of greatly increasing the amount of reward supervision during meta-training time.
Revised: Meta-reinforcement learning (RL) can meta-train policies that adapt to new tasks with orders of magnitude less data than standard RL, but this fast adaptation often comes at the cost of greatly increasing the amount of reward supervision during meta-training time.
Edit intent: clarity
Original: Meta-reinforcement learning (RL) can be used to train policies that quickly adapt to new tasks with orders of magnitude less data than standard RL, but this fast adaptation often comes at the cost of greatly increasing the amount of reward supervision during meta-training time. Offline meta-RL removes the need to continuously provide reward supervision because rewards must only be provided once when the offline dataset is generated.
Revised: Meta-reinforcement learning (RL) can be used to train policies that quickly adapt to new tasks with orders of magnitude less data than standard RL, but meta-training itself is costly and time-consuming. If we can meta-train on offline data, then we can reuse the same static dataset, labeled once with rewards for different tasks, to meta-train policies that adapt to a variety of new tasks at meta-test time. Although this capability would make meta-RL removes the need to continuously provide reward supervision because rewards must only be provided once when the offline dataset is generated.
Edit intent: coherence