wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this paper, we formalize principles, that aim to model a range of learning opportunity propertiesin recommendations , and a  metric that combines them to quantify the equality of  learning opportunities among learners.
Revised:  In this paper, we formalize educational principles that model recommendations' learning properties , and a metric that combines them to quantify the equality of learning opportunities among learners.
Edit intent: clarity
Original:  In this paper, we formalize principles, that aim to model a range of learning opportunity propertiesin recommendations , and a  metric that combines them to quantify the equality of  learning opportunities among learners.
Revised:  In this paper, we formalize principles, that aim to model a range of learning opportunity propertiesin recommendations , and a metric that combines them in order to monitor the equality of learning opportunities among learners.
Edit intent: clarity
Original:  In this paper, we formalize principles, that aim to model a range of learning opportunity propertiesin recommendations , and a  metric that combines them to quantify the equality of  learning opportunities among learners.
Revised:  In this paper, we formalize principles, that aim to model a range of learning opportunity propertiesin recommendations , and a metric that combines them to quantify the equality of recommended learning opportunities among learners.
Edit intent: clarity
Original:  Then, we envision a scenario wherein platform owners seek to guarantee that the generated recommendations meet each principle , to a certain degree , for all learners, constrained to their individual preferences.
Revised:  Then, we envision a scenario wherein an educational platform should be arranged in such a way that the generated recommendations meet each principle , to a certain degree , for all learners, constrained to their individual preferences.
Edit intent: clarity
Original:  Then, we envision a scenario wherein platform owners seek to guarantee that the generated recommendations meet each principle , to a certain degree , for all learners, constrained to their individual preferences.
Revised:  Then, we envision a scenario wherein platform owners seek to guarantee that the generated recommendations meet each principle to a certain degree , for all learners, constrained to their individual preferences.
Edit intent: fluency
Original:  Then, we envision a scenario wherein platform owners seek to guarantee that the generated recommendations meet each principle , to a certain degree , for all learners, constrained to their individual preferences.
Revised:  Then, we envision a scenario wherein platform owners seek to guarantee that the generated recommendations meet each principle , to a certain degree for all learners, constrained to their individual preferences.
Edit intent: fluency
Original:  To mitigate this effect, we propose a  post-processing approach that balances personalization and learning opportunity equality in recommendations.
Revised:  To mitigate this effect, we propose a novel post-processing approach that balances personalization and learning opportunity equality in recommendations.
Edit intent: clarity
Original:  Experiments on a large-scale dataset demonstrate that our approach leads to higher equality of learning opportunity , with a small loss in personalization  .
Revised:  Experiments on a large-scale dataset demonstrate that our approach leads to higher equality , with a small loss in personalization .
Edit intent: clarity
Original:  Experiments on a large-scale dataset demonstrate that our approach leads to higher equality of learning opportunity , with a small loss in personalization  .
Revised:  Experiments on a large-scale dataset demonstrate that our approach leads to higher equality of learning opportunity , with a negligible loss in personalization .
Edit intent: clarity
Original:  Consumer-supplier matching platforms pose several technical challenges, specially due to the trade-off between recommending suitable suppliers to consumers and avoiding collisions among consumers' preferences.
Revised:  Consumer-supplier matching platforms pose several technical challenges, specially due to the tradeoff between recommending suitable suppliers to consumers and avoiding collisions among consumers' preferences.
Edit intent: fluency
Original:  Given the computational complexity of the problem, we show several provable guarantees for the general model , which in particular, significantly improve the approximation factors previously obtained  .
Revised:  Given the computational complexity of the problem, we show several provable guarantees for the general model that, in particular, significantly improve the approximation factors previously obtained .
Edit intent: coherence
Original:  We show that the sample complexity of MC2G meets an information-theoretic limit that is derived using maximum likelihood estimation and is also order-optimal.
Revised:  The expected number of sampled entries required for MC2G meets an information-theoretic limit that is derived using maximum likelihood estimation and is also order-optimal.
Edit intent: clarity
Original:  Finally, experiments on a sub-sampled version of the Netflix dataset show that MC2G significantly outperforms other state-of-the-art matrix completion algorithms that leverage graph side information.
Revised:  Finally, experiments on a sub-sampled version of the Netflix dataset show that MC2G outperforms other state-of-the-art matrix completion algorithms that leverage graph side information.
Edit intent: clarity
Original: We introduce and implement a method to compute ground states of nonlinearSchr {\"o}dinger equations on metric graphs.
Revised: We introduce and implement a method to compute ground states of nonlinear Schr {\"o}dinger equations on metric graphs.
Edit intent: fluency
Original:  Ground states are defined as minimizers of the nonlinear Schr{\"o} dingerenergy at fixed mass.
Revised:  Ground states are defined as minimizers of the nonlinear Schr{\"o} dinger energy at fixed mass.
Edit intent: fluency
Original:  We first prove that, at the continuous level, the normalized gradient flow is well-posed, mass-preserving, energy diminishingand converges (at least locally) toward the ground state.
Revised:  We first prove that, at the continuous level, the normalized gradient flow is well-posed, mass-preserving, energy diminishing and converges (at least locally) toward the ground state.
Edit intent: fluency
Original:  We thenestablish the link between the continuous flow and its discretezedversion .
Revised:  We then establish the link between the continuous flow and its discretezedversion .
Edit intent: fluency
Original:  We thenestablish the link between the continuous flow and its discretezedversion .
Revised:  We thenestablish the link between the continuous flow and its discretized version .
Edit intent: clarity
Original:  We conclude by conducting a series of numericalexperiments in model situations showing the good performance of the discrete flow tocompute the ground state.
Revised:  We conclude by conducting a series of numerical experiments in model situations showing the good performance of the discrete flow tocompute the ground state.
Edit intent: fluency
Original:  We conclude by conducting a series of numericalexperiments in model situations showing the good performance of the discrete flow tocompute the ground state.
Revised:  We conclude by conducting a series of numericalexperiments in model situations showing the good performance of the discrete flow to compute the ground state.
Edit intent: fluency
Original:  Further experiments as well as detailledexplananation of our numerical algorithm will be given in aforthcoming companion paper.
Revised:  Further experiments as well as detailed explanation of our numerical algorithm will be given in aforthcoming companion paper.
Edit intent: fluency
Original:  Further experiments as well as detailledexplananation of our numerical algorithm will be given in aforthcoming companion paper.
Revised:  Further experiments as well as detailledexplananation of our numerical algorithm will be given in a forthcoming companion paper.
Edit intent: fluency
Original: We introduce and implement a method to compute ground states of nonlinear Schr \"o dinger equations on metric graphs.
Revised: We introduce and implement a method to compute stationary states of nonlinear Schr \"o dinger equations on metric graphs.
Edit intent: clarity
Original: We introduce and implement a method to compute ground states of nonlinear Schr \"o dinger equations on metric graphs.
Revised: We introduce and implement a method to compute ground states of nonlinear Schr dinger equations on metric graphs.
Edit intent: fluency
Original: We introduce and implement a method to compute ground states of nonlinear Schr \"o dinger equations on metric graphs.
Revised: We introduce and implement a method to compute ground states of nonlinear Schr \"o \''odinger equations on metric graphs.
Edit intent: fluency
Original:  Ground states are defined as minimizers of the nonlinear Schr \"o dinger energy at fixed mass.
Revised:  Stationary states are obtained as local minimizers of the nonlinear Schr \"o dinger energy at fixed mass.
Edit intent: clarity
Original:  Ground states are defined as minimizers of the nonlinear Schr \"o dinger energy at fixed mass.
Revised:  Ground states are defined as minimizers of the nonlinear Schr dinger energy at fixed mass.
Edit intent: fluency
Original:  Ground states are defined as minimizers of the nonlinear Schr \"o dinger energy at fixed mass.
Revised:  Ground states are defined as minimizers of the nonlinear Schr \"o \''odinger energy at fixed mass.
Edit intent: fluency
Original:  Our method is based on a normalized gradient flow for the energy (i.e. a gradient flowprojected on a fixed mass sphere) adapted to the context of nonlinear quantum graphs.
Revised:  Our method is based on a normalized gradient flow for the energy (i.e. a gradient flow projected on a fixed mass sphere) adapted to the context of nonlinear quantum graphs.
Edit intent: fluency
Original:  We first prove that, at the continuous level, the normalized gradient flow is well-posed, mass-preserving, energy diminishing and converges (at least locally) toward the ground state .
Revised:  We first prove that, at the continuous level, the normalized gradient flow is well-posed, mass-preserving, energy diminishing and converges (at least locally) towards stationary states .
Edit intent: clarity
Original:  We conclude by conducting a series of numerical experiments in model situations showing the good performance of the discrete flow to compute the ground state .
Revised:  We conclude by conducting a series of numerical experiments in model situations showing the good performance of the discrete flow to compute stationary states .
Edit intent: clarity
Original:  Further experiments as well as detailed explanation of our numerical algorithm will be given in a forthcoming companion paper.
Revised:  Further experiments as well as detailed explanation of our numerical algorithm are given in a forthcoming companion paper.
Edit intent: clarity
Original:  Further experiments as well as detailed explanation of our numerical algorithm will be given in a forthcoming companion paper.
Revised:  Further experiments as well as detailed explanation of our numerical algorithm will be given in a companion paper.
Edit intent: clarity
Original:  We initiate a condition-based complexity framework based on the norm of the cube , that is a step in this direction.
Revised:  We initiate a condition-based complexity framework based on the norm of the cube that is a step in this direction.
Edit intent: fluency
Original:  One of the challenges that it poses is to expand the currently limited range of random polynomials that we can handle.
Revised:  Among the challenges that it poses is to expand the currently limited range of random polynomials that we can handle.
Edit intent: fluency
Original:  In both cases, we provide a bound that is polynomial in the size of the input (size of the support plus  logarithm of the degree) for not only on the average , but all higher moments.
Revised:  In both cases, we provide a bound that is polynomial in the size of the input (size of the support plus the logarithm of the degree) for not only on the average , but all higher moments.
Edit intent: fluency
Original:  In both cases, we provide a bound that is polynomial in the size of the input (size of the support plus  logarithm of the degree) for not only on the average , but all higher moments.
Revised:  In both cases, we provide a bound that is polynomial in the size of the input (size of the support plus logarithm of the degree) not only for the average but also for all higher moments.
Edit intent: clarity
Original:  The dimensions of different feature embeddings are often set to a uniform value manually or through grid search, which may yield suboptimal model performance . Existing work applied heuristic methods or reinforcement learning to search for varying embedding dimensions.
Revised:  The dimensions of different feature embeddings are often set to a same value empirically, which limits the predictive performance of latent factor models. Existing works have proposed heuristic or reinforcement learning-based methods to search for varying embedding dimensions.
Edit intent: clarity
Original:  The dimensions of different feature embeddings are often set to a uniform value manually or through grid search, which may yield suboptimal model performance . Existing work applied heuristic methods or reinforcement learning to search for varying embedding dimensions.
Revised:  The dimensions of different feature embeddings are often set to a uniform value manually or through grid search, which may yield suboptimal model performance . Existing work applied heuristic methods or reinforcement learning to search for mixed feature embedding dimensions.
Edit intent: clarity
Original:  However, the embedding dimension per feature is rigidly chosen from a restricted set of candidates due to the scalability issue involved in the optimization process over a large search space .
Revised:  For efficiency concern, these methods typically choose embedding dimensions from a restricted set of candidates due to the scalability issue involved in the optimization process over a large search space .
Edit intent: clarity
Original:  However, the embedding dimension per feature is rigidly chosen from a restricted set of candidates due to the scalability issue involved in the optimization process over a large search space .
Revised:  However, the embedding dimension per feature is rigidly chosen from a restricted set of candidate dimensions. However, this restriction will hurt the flexibility of dimension selection, leading to suboptimal performance of search results .
Edit intent: clarity
Original:  Our method can be seamlessly incorporated with various existing architectures of latent factor models for recommendation.
Revised:  Our method can be seamlessly incorporated with existing latent factor models for recommendation.
Edit intent: clarity
Original:  The results demonstrate that our method achieves the best recommendation performance compared with 3 neural input search approaches over all the model architectures, and can reduce the number of embedding parameters by 2x and 20x on CF and CTR prediction, respectively .
Revised:  The results demonstrate that our method achieves the best predictive performance compared with 3 neural input search approaches over all the model architectures, and can reduce the number of embedding parameters by 2x and 20x on CF and CTR prediction, respectively .
Edit intent: clarity
Original:  The results demonstrate that our method achieves the best recommendation performance compared with 3 neural input search approaches over all the model architectures, and can reduce the number of embedding parameters by 2x and 20x on CF and CTR prediction, respectively .
Revised:  The results demonstrate that our method achieves the best recommendation performance compared with existing neural input search approaches over all the model architectures, and can reduce the number of embedding parameters by 2x and 20x on CF and CTR prediction, respectively .
Edit intent: clarity
Original:  The results demonstrate that our method achieves the best recommendation performance compared with 3 neural input search approaches over all the model architectures, and can reduce the number of embedding parameters by 2x and 20x on CF and CTR prediction, respectively .
Revised:  The results demonstrate that our method achieves the best recommendation performance compared with 3 neural input search approaches with fewer embedding parameters and less time cost .
Edit intent: clarity
Original:  Under the dynamical degree-corrected stochastic block model, in the case of two classes of equal size, we demonstrate and support with extensive simulations that our proposed algorithm is capable of making non-trivial community reconstruction as soon as theoretically possible, thereby reaching the optimal detectability thresholdand provably outperforming competing spectral methods.
Revised:  Under the dynamical degree-corrected stochastic block model, in the case of two classes of equal size, we demonstrate and support with extensive simulations that our proposed algorithm is capable of making non-trivial community reconstruction as soon as theoretically possible, thereby reaching the optimal detectability threshold and provably outperforming competing spectral methods.
Edit intent: fluency
Original: It is commonly believed among the machine learning (ML) community that industry influence on the community itself as well as the scientific process is increasing since tech companies have begun to allocate a large amount of human and monetary resources to ML . However, concrete ethical implications and the quantitative scale  of this influence are rather unknown.
Revised: Industry involvement in the machine learning (ML) community that industry influence on the community itself as well as the scientific process is increasing since tech companies have begun to allocate a large amount of human and monetary resources to ML . However, concrete ethical implications and the quantitative scale of this influence are rather unknown.
Edit intent: clarity
Original: It is commonly believed among the machine learning (ML) community that industry influence on the community itself as well as the scientific process is increasing since tech companies have begun to allocate a large amount of human and monetary resources to ML . However, concrete ethical implications and the quantitative scale  of this influence are rather unknown.
Revised: It is commonly believed among the machine learning (ML) community seems to be increasing . However, concrete ethical implications and the quantitative scale of this influence are rather unknown.
Edit intent: clarity
Original: It is commonly believed among the machine learning (ML) community that industry influence on the community itself as well as the scientific process is increasing since tech companies have begun to allocate a large amount of human and monetary resources to ML . However, concrete ethical implications and the quantitative scale  of this influence are rather unknown.
Revised: It is commonly believed among the machine learning (ML) community that industry influence on the community itself as well as the scientific process is increasing since tech companies have begun to allocate a large amount of human and monetary resources to ML . However, the quantitative scale of this influence are rather unknown.
Edit intent: coherence
Original:  For this purpose  we have not only carried out an informed ethical analysis of the field, but have inspected all papers of the main ML conferences NeurIPS, CVPR  and ICML of the last 5 years - almost 11000 papers in total.
Revised:  For this purpose , we have not only carried out an informed ethical analysis of the field, but have inspected all papers of the main ML conferences NeurIPS, CVPR and ICML of the last 5 years - almost 11000 papers in total.
Edit intent: fluency
Original:  For this purpose  we have not only carried out an informed ethical analysis of the field, but have inspected all papers of the main ML conferences NeurIPS, CVPR  and ICML of the last 5 years - almost 11000 papers in total.
Revised:  For this purpose we have not only carried out an informed ethical analysis of the field, but have inspected all papers of the main ML conferences NeurIPS, CVPR , and ICML of the last 5 years - almost 11000 papers in total.
Edit intent: fluency
Original:  For this purpose  we have not only carried out an informed ethical analysis of the field, but have inspected all papers of the main ML conferences NeurIPS, CVPR  and ICML of the last 5 years - almost 11000 papers in total.
Revised:  For this purpose we have not only carried out an informed ethical analysis of the field, but have inspected all papers of the main ML conferences NeurIPS, CVPR and ICML of the last 5 years - almost 11,000 papers in total.
Edit intent: fluency
Original:  (2) Industry publishes papers about trending ML topics on average two years earlier than academia  . (3) Industry papers are not lagging behind academic papers concerning social impact considerations.
Revised:  (2) Industry publishes papers about trending ML topics on average two years earlier than academia does . (3) Industry papers are not lagging behind academic papers concerning social impact considerations.
Edit intent: clarity
Original:  (2) Industry publishes papers about trending ML topics on average two years earlier than academia  . (3) Industry papers are not lagging behind academic papers concerning social impact considerations.
Revised:  (2) Industry publishes papers about trending ML topics on average two years earlier than academia . (3) Industry papers are not lagging behind academic papers in regard to social impact considerations.
Edit intent: fluency
Original:  We believe that this is a good starting point for further fine-grained discussion.
Revised:  We believe that this work is a starting point for further fine-grained discussion.
Edit intent: clarity
Original:  We utilising them to train a sentiment classifier that achieves an F-1 score of 0.722.
Revised:  We utilised them to train a sentiment classifier that achieves an F-1 score of 0.722.
Edit intent: fluency
Original: Data analysis pipelines are known to be impacted by computational conditions, presumably due to the creation , propagation, and amplification of numerical errors.
Revised: Data analysis pipelines are known to be impacted by computational conditions, presumably due to the creation and propagation of numerical errors.
Edit intent: clarity
Original:  The results illustrate how different levels of pruning affect the learning speed in conjunction to the loss in accuracy in terms of model fitting, and show that aggressive pruning may be required to produce approximate solutions for high complexity problems.
Revised:  The results illustrate how different levels of pruning affect the learning speed relative to the loss in accuracy in terms of model fitting, and show that aggressive pruning may be required to produce approximate solutions for high complexity problems.
Edit intent: clarity
Original: Due to the growing deployment of classification algorithms in various social contexts , developing methods that are fair with respect to protected attributes such as gender or race is an important problem .
Revised: Due to the deployment of classification algorithms in various social contexts , developing methods that are fair with respect to protected attributes such as gender or race is an important problem .
Edit intent: clarity
Original: Due to the growing deployment of classification algorithms in various social contexts , developing methods that are fair with respect to protected attributes such as gender or race is an important problem .
Revised: Due to the growing deployment of classification algorithms in various social contexts , developing methods that are fair with respect to protected attributes such as gender or race is crucial .
Edit intent: clarity