wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our novel approach achieves state-of-the-art  in extracting information from documents and answering questions , demanding layout understanding (DocVQA, CORD, WikiOps, SROIE).
Revised:  Our novel approach achieves state-of-the-art in extracting information from documents and answering questions which demand layout understanding (DocVQA, CORD, WikiOps, SROIE).
Edit intent: coherence
Original: Deep reinforcement learning agents may learn complex tasks more efficiently when they coordinate with one another. We consider a teacher-student coordination scheme wherein an agent may ask another agent for demonstrations . Despite the benefits of sharing demonstrations, however, potential adversaries may obtain sensitive information belonging to the teacher by observing the demonstrations.
Revised: Kickstarting deep reinforcement learning algorithms facilitate a teacher-student coordination scheme wherein an agent may ask another agent for demonstrations . Despite the benefits of sharing demonstrations, however, potential adversaries may obtain sensitive information belonging to the teacher by observing the demonstrations.
Edit intent: clarity
Original:  We consider a teacher-student coordination scheme wherein an agent may ask another agent for demonstrations . Despite the benefits of sharing demonstrations, however, potential adversaries may obtain sensitive information belonging to the teacher by observing the demonstrations.
Revised:  We consider a teacher-student relationship among the agents and allow for a well-performing teacher to share demonstrations with a student to expedite the student's training. However, despite the known benefits, the demonstrations may contain sensitive information about the teacher's training data and existing kickstarting methods do not take any measures to protect it . Despite the benefits of sharing demonstrations, however, potential adversaries may obtain sensitive information belonging to the teacher by observing the demonstrations.
Edit intent: meaning-changed
Original:  We consider a teacher-student coordination scheme wherein an agent may ask another agent for demonstrations . Despite the benefits of sharing demonstrations, however, potential adversaries may obtain sensitive information belonging to the teacher by observing the demonstrations. In particular, deep reinforcement learning algorithms are known to be vulnerable to membership attacks, which make accurate inferences about the membership of the entries of training datasets.
Revised:  We consider a teacher-student coordination scheme wherein an agent may ask another agent for demonstrations . 
Edit intent: coherence
Original:  Therefore, there is a need to safeguard the teacher against such privacy threats. We fix the teacher's policy as the context of the demonstrations , which allows for different internal models across the student and the teacher , and contrasts the existing methods.
Revised:  Therefore, we use the framework of differential privacy to develop a mechanism that securely shares the teacher's policy as the context of the demonstrations , which allows for different internal models across the student and the teacher , and contrasts the existing methods.
Edit intent: meaning-changed
Original:  We fix the teacher's policy as the context of the demonstrations , which allows for different internal models across the student and the teacher , and contrasts the existing methods. We make the following two contributions. (i) We develop a differentially private mechanism that protects the privacy of the teacher's training dataset.
Revised:  We fix the teacher's demonstrations with the student. The mechanism allows for the teacher to decide upon the accuracy of its demonstrations with respect to the privacy budget that it consumes, thereby granting the teacher full control over its data privacy. We then develop a kickstarted deep reinforcement learning algorithm for the student that is privacy-aware because we calibrate its objective with the parameters of the teacher's training dataset.
Edit intent: meaning-changed
Original:  (i) We develop a differentially private mechanism that protects the privacy of the teacher's training dataset. (ii) We propose a proximal policy-optimization objective that enables the student to benefit from the demonstrations despite the perturbations of the privacy mechanism.
Revised:  (i) We develop a differentially private mechanism that protects the privacy of the teacher's privacy mechanism. The privacy-aware design of the algorithm makes it possible to kickstart the student's learning despite the perturbations of the privacy mechanism.
Edit intent: clarity
Original:  We empirically show that the algorithm improves the student's learning upon convergence rate and utility. Specifically, compared with an agent who learns the same task on its own, we observe that the student 's policy converges faster, and the converging policy accumulates higher rewards more robustly .
Revised:  We empirically show that the algorithm improves the student's learning , (ii) the student converges to a performance level that was not possible without the demonstrations, and (iii) the student maintains its enhanced performance even after the teacher stops sharing useful demonstrations due to its privacy budget constraints .
Edit intent: meaning-changed
Original:  Data containing sensitive personal information, such as documents with people's profiles or relationships as edges, are prone to privacy leaks from GCNs, as an adversary might reveal the original input from the trained model .
Revised:  However, sensitive personal information, such as documents with people's profiles or relationships as edges, are prone to privacy leaks from GCNs, as an adversary might reveal the original input from the trained model .
Edit intent: coherence
Original:  We address these challenges by adapting differentially-private gradient-based training to GCNs . We investigate the impact of various privacy budgets, dataset sizes, and two optimizers in an experimental setup over five NLP datasets in two languages  .
Revised:  We address these challenges by adapting differentially-private gradient-based training to GCNs and conduct experiments using two optimizers on five NLP datasets in two languages .
Edit intent: clarity
Original:  We address these challenges by adapting differentially-private gradient-based training to GCNs . We investigate the impact of various privacy budgets, dataset sizes, and two optimizers in an experimental setup over five NLP datasets in two languages  .
Revised:  We address these challenges by adapting differentially-private gradient-based training to GCNs . We investigate the impact of various privacy budgets, dataset sizes, and two optimizers in an experimental setup over five NLP datasets in two languages . We propose a simple yet efficient method based on random graph splits that not only improves the baseline privacy bounds by a factor of 2.7 while retaining competitive F1 scores, but also provides strong privacy guarantees of epsilon = 1.0 .
Edit intent: meaning-changed
Original:  We demonstrate that the staggered mesh method circumvents a significant error source in the standard method, in which certain quadrature nodes are always placed on points where the integrand is discontinuous  .
Revised:  We demonstrate that the staggered mesh method circumvents a significant error source in the standard method, in which certain quadrature nodes are always placed on points where the integrand is discontinuous . One significant advantage of the proposed method is that there are no tunable parameters, and the additional numerical effort needed can be negligible compared to the standard MP2 calculation .
Edit intent: meaning-changed
Original:  In this paper, we consider a resilient DRL framework with observational interferences . Under this framework, we discuss the importance of the causal relation and propose a causal inference based DRL algorithm called causal inference Q-network (CIQ).
Revised:  In this paper, we consider a generative DRL framework training with an auxiliary task of observational interferences such as artificial noises . Under this framework, we discuss the importance of the causal relation and propose a causal inference based DRL algorithm called causal inference Q-network (CIQ).
Edit intent: meaning-changed
Original:   Experimental results show that the framework provides comparable performance to that of the approaches that use human experts, and that it is also capable of proving theorems that it has never seen during training .
Revised:  We implement the framework in the HOL4 theorem prover. Experimental results show that the framework provides comparable performance to that of the approaches that use human experts, and that it is also capable of proving theorems that it has never seen during training .
Edit intent: meaning-changed
Original:  PGCs strictly subsume many existing tractable probabilistic models, including determinantal point processes (DPPs), probabilistic circuits (PCs) such as sum-product networks, and tractable graphical models.
Revised:  PGCs are strictly more expressive efficient than many existing tractable probabilistic models, including determinantal point processes (DPPs), probabilistic circuits (PCs) such as sum-product networks, and tractable graphical models.
Edit intent: meaning-changed
Original:  The standard HO-GSVD factors N matrices A_i\inR^{m_i\times n} as A_i=U_i\Sigma_i V ^{\text{T , but requires that each of the matrices A_i has full column rank.
Revised:  The standard HO-GSVD factors N matrices A_i\inR^{m_i\times n} as A_i=U_i\Sigma_i V ^\text{T , but requires that each of the matrices A_i has full column rank.
Edit intent: fluency
Original:  NVEs aim to give distributed users a feeling of immersion in a virtual world and the possibility of interacting with other users or with virtual elements inside it, in a similar way to when they interact in the real world.
Revised:  NVEs aim to give distributed users a feeling of immersion in a virtual world and the possibility of interacting with other users or with virtual objects inside it, in a similar way to when they interact in the real world.
Edit intent: clarity
Original:  NVEs aim to give distributed users a feeling of immersion in a virtual world and the possibility of interacting with other users or with virtual elements inside it, in a similar way to when they interact in the real world.
Revised:  NVEs aim to give distributed users a feeling of immersion in a virtual world and the possibility of interacting with other users or with virtual elements inside it, like when they interact in the real world.
Edit intent: clarity
Original:  Nevertheless, this goal is especially sensitive to different issues, such as different connections with heterogeneous bitrates or different network latencies, which can lead to consistency and synchronization problems and, thus, to a worsening of the users' quality of experience or QoE.
Revised:  Nevertheless, this goal is especially sensitive to different issues, such as different connections with heterogeneous throughput or different network latencies, which can lead to consistency and synchronization problems and, thus, to a worsening of the users' quality of experience or QoE.
Edit intent: clarity
Original:  With the purpose of solving these issues, many technical solutions have been proposed and evaluated so far , in fields like network architectures, data distribution and filtering, resource balancing, computing models, predictive modeling and synchronization in NVEs.
Revised:  With the purpose of solving these issues, researchers have proposed and evaluated so far , in fields like network architectures, data distribution and filtering, resource balancing, computing models, predictive modeling and synchronization in NVEs.
Edit intent: clarity
Original:  With the purpose of solving these issues, many technical solutions have been proposed and evaluated so far , in fields like network architectures, data distribution and filtering, resource balancing, computing models, predictive modeling and synchronization in NVEs.
Revised:  With the purpose of solving these issues, many technical solutions have been proposed and evaluated numerous technical solutions , in fields like network architectures, data distribution and filtering, resource balancing, computing models, predictive modeling and synchronization in NVEs.
Edit intent: meaning-changed
Original:  With the current increase of the number of NVEs and the multiple solutions proposed so far, this work aims to become a useful tool and a starting point not only for future researchers in this field but also for those who are new in NVEs development, in which guaranteeing a good  QoE is essential.
Revised:  With the current increase of the number of NVEs and the multiple solutions proposed so far, this work aims to become a useful tool and a starting point not only for future researchers in this field but also for those who are new in NVEs development, in which guaranteeing a good users' QoE is essential.
Edit intent: clarity
Original:  We prove that an optimal policy can be learned over this model-invariance state abstraction  . Next, we propose a practical method to approximately learn a model-invariant representation for complex domains .
Revised:  We prove that an optimal policy can be learned over this model-invariance state abstraction and show improved generalization in a simple toy domain . Next, we propose a practical method to approximately learn a model-invariant representation for complex domains .
Edit intent: meaning-changed
Original:  We prove that an optimal policy can be learned over this model-invariance state abstraction  . Next, we propose a practical method to approximately learn a model-invariant representation for complex domains . We validate our approach by showing improved modeling performance over standard maximum likelihood approaches on challenging tasks, such as the MuJoCo-based Humanoid.
Revised:  We prove that an optimal policy can be learned over this model-invariance state abstraction . Next, we propose a practical method to approximately learn a model-invariant representation for complex domains and validate our approach by showing improved modeling performance over standard maximum likelihood approaches on challenging tasks, such as the MuJoCo-based Humanoid.
Edit intent: coherence
Original: This note introduces a simple metric for benchmarking shock-capturing schemes , and this metric is especially focusing on the shock-capturing overshoots, which may damage the robustness of numerical simulations, as well as the reliability of numerical results.
Revised: This note introduces a simple metric for benchmarking shock-capturing schemes . This metric is especially focusing on the shock-capturing overshoots, which may damage the robustness of numerical simulations, as well as the reliability of numerical results.
Edit intent: coherence
Original: This note introduces a simple metric for benchmarking shock-capturing schemes , and this metric is especially focusing on the shock-capturing overshoots, which may damage the robustness of numerical simulations, as well as the reliability of numerical results.
Revised: This note introduces a simple metric for benchmarking shock-capturing schemes , and this metric is especially focused on the shock-capturing overshoots, which may damage the robustness of numerical simulations, as well as the reliability of numerical results.
Edit intent: fluency
Original: This note introduces a simple metric for benchmarking shock-capturing schemes , and this metric is especially focusing on the shock-capturing overshoots, which may damage the robustness of numerical simulations, as well as the reliability of numerical results.
Revised: This note introduces a simple metric for benchmarking shock-capturing schemes , and this metric is especially focusing on the shock-capturing overshoots, which may undermine the robustness of numerical simulations, as well as the reliability of numerical results.
Edit intent: clarity
Original:  The idea is to numerically solve the  linear advection equation with square waves of different wavenumbers as initial solutions. Therefore, the exact overshoots after one step of temporal evolution  can be easily calculated, and shown as a function of  CFL number and  reduced wavenumber.
Revised:  The idea is to numerically solve the linear advection equation with an initial condition of a square wave characterized with different wavenumbers. After one step of temporal evolution can be easily calculated, and shown as a function of CFL number and reduced wavenumber.
Edit intent: clarity
Original:  The present metricis able to quantify the numerical overshoots of shock-capturing schemes , and some new features of several canonical schemes are shown .
Revised:  The present metricis able to quantify the numerical overshoots of shock-capturing schemes are analyzed accordingly, and several findings including the amplitude of overshoots non-monotonously varying with the CFL number, and the amplitude of overshoots significantly depending on the reduced wavenumber of the square waves (discontinuities), are newly discovered, which are not before .
Edit intent: meaning-changed
Original: The personalization of cardiac models is the cornerstone of patient-specific modeling. Ideally, non-invasive or minimally-invasive clinical data, such as the standard ECG or intracardiac contact recordings, could provide an insight on modelparameters.
Revised: The identification of the initial ventricular activation sequence is a critical step for the correct personalization of patient-specific cardiac models. In healthy conditions, the Purkinje network is the main source of the electrical activation, but under pathological conditions the so-called earliest activation sites (EASs) are possibly sparser and more localized. Yet, their number, location and timing may not be easily inferred from remote recordings, such as the standard ECG or intracardiac contact recordings, could provide an insight on modelparameters.
Edit intent: meaning-changed
Original:  Ideally, non-invasive or minimally-invasive clinical data, such as the standard ECG or intracardiac contact recordings, could provide an insight on modelparameters. Parameter selection of such models is however a challenging and potentially time-consuming task.
Revised:  Ideally, non-invasive or minimally-invasive clinical data, such as the epicardial activation or the 12-lead electrocardiogram (ECG), due to the underlying complexity of the model.
Edit intent: clarity
Original:  In this work, we estimate the earliest activation sites governing the cardiac electrical activation. Specifically, we introduce GEASI (Geodesic-based Earliest Activation Sites Identification) as a novel approach to simultaneously identify their locations and times .
Revised:  In this work, we introduce GEASI (Geodesic-based Earliest Activation Sites Identification) as a novel approach to simultaneously identify their locations and times .
Edit intent: coherence
Original:  To this end, we start from the anisotropic eikonal equation modeling cardiac electrical activation and exploit its Hamilton-Jacobi formulation to minimize a given objective functional, which in the case of GEASI is the quadratic mismatch to given activation measurements.
Revised:  To this end, we start from the anisotropic eikonal equation modeling cardiac electrical activation and exploit its Hamilton--Jacobi formulation to minimize a given objective functional, which in the case of GEASI is the quadratic mismatch to given activation measurements.
Edit intent: fluency
Original:  To this end, we start from the anisotropic eikonal equation modeling cardiac electrical activation and exploit its Hamilton-Jacobi formulation to minimize a given objective functional, which in the case of GEASI is the quadratic mismatch to given activation measurements.
Revised:  To this end, we start from the anisotropic eikonal equation modeling cardiac electrical activation and exploit its Hamilton-Jacobi formulation to minimize a given objective function, e.g. the quadratic mismatch to given activation measurements.
Edit intent: coherence
Original: Implicit regularization in deep learning is perceived as a tendency of gradient-based optimization to fit training data with predictors of minimal "complexity. " The fact that only some types of data give rise to generalization is understood to result from them being especially amenable to fitting with low complexity predictors. A major challenge in formalizing this intuition is to define complexity measures that are quantitative yet capture the essence of data that admits generalization. With an eye towards this challenge , we provide the first  analysis of implicit regularization in tensor factorization , equivalent to a certain non-linear neural network.
Revised: Implicit regularization in deep learning have led to a theoretical focus on matrix factorization -- matrix completion via linear neural network. As a step further towards practical deep learning , we provide the first analysis of implicit regularization in tensor factorization , equivalent to a certain non-linear neural network.
Edit intent: meaning-changed
Original:  We characterize the dynamics that gradient descent induces on the factorization, and establish a bias towards low tensor rank , in compliance with empirical evidence. Then, motivated by tensor rank capturing  implicit regularization of a non-linear neural network, we empirically explore it as a measure of complexity, and find that it stays extremely low when fitting standard datasets .
Revised:  We characterize the dynamics that gradient descent induces on the factorization, and establish a bias towards low tensor rank search, which we rigorously prove under certain conditions, and empirically demonstrate under others. Motivated by tensor rank capturing implicit regularization of a non-linear neural network, we empirically explore it as a measure of complexity, and find that it stays extremely low when fitting standard datasets .
Edit intent: meaning-changed
Original:  Then, motivated by tensor rank capturing  implicit regularization of a non-linear neural network, we empirically explore it as a measure of complexity, and find that it stays extremely low when fitting standard datasets .
Revised:  Then, motivated by tensor rank capturing implicit regularization of a non-linear neural network, we empirically explore it as a measure of complexity, and find that it captures the essence of datasets on which neural networks generalize .
Edit intent: meaning-changed
Original: While Curriculum learning (CL) has recently gained traction in Natural language Processing Tasks, it still isn't being analyzed adequately . Previous works only show their effectiveness but fail short to fully explain and interpret the internal workings  .
Revised: While Curriculum Learning (CL) has recently gained traction in Natural language Processing Tasks, it still isn't being analyzed adequately . Previous works only show their effectiveness but fail short to fully explain and interpret the internal workings .
Edit intent: fluency
Original: While Curriculum learning (CL) has recently gained traction in Natural language Processing Tasks, it still isn't being analyzed adequately . Previous works only show their effectiveness but fail short to fully explain and interpret the internal workings  .
Revised: While Curriculum learning (CL) has recently gained traction in Natural language Processing Tasks, it is still not adequately analyzed . Previous works only show their effectiveness but fail short to fully explain and interpret the internal workings .
Edit intent: clarity
Original: While Curriculum learning (CL) has recently gained traction in Natural language Processing Tasks, it still isn't being analyzed adequately . Previous works only show their effectiveness but fail short to fully explain and interpret the internal workings  .
Revised: While Curriculum learning (CL) has recently gained traction in Natural language Processing Tasks, it still isn't being analyzed adequately . Previous works only show their effectiveness but fail short to explain and interpret the internal workings .
Edit intent: clarity