wanyu/IteraTeR_v2 huggingface dataset:
Original:  On one end of the spectrum, 3D face modeling methods provide parametric control but tend to generate unrealistic images, while on the other end , generative 2D models like GANs (Generative Adversarial Networks) output photo-realistic face images, but lack explicit control.
Revised:  On one end of the spectrum, 3D face modeling methods provide parametric control but generates unrealistic images, while on the other end , generative 2D models like GANs (Generative Adversarial Networks) output photo-realistic face images, but lack explicit control.
Edit intent: clarity
Original:  On one end of the spectrum, 3D face modeling methods provide parametric control but tend to generate unrealistic images, while on the other end , generative 2D models like GANs (Generative Adversarial Networks) output photo-realistic face images, but lack explicit control.
Revised:  On one end of the spectrum, 3D face modeling methods provide parametric control but tend to generate unrealistic images, on the other end , generative 2D models like GANs (Generative Adversarial Networks) output photo-realistic face images, but lack explicit control.
Edit intent: coherence
Original:  On one end of the spectrum, 3D face modeling methods provide parametric control but tend to generate unrealistic images, while on the other end , generative 2D models like GANs (Generative Adversarial Networks) output photo-realistic face images, but lack explicit control.
Revised:  On one end of the spectrum, 3D face modeling methods provide parametric control but tend to generate unrealistic images, while on the other hand , generative 2D models like GANs (Generative Adversarial Networks) output photo-realistic face images, but lack explicit control.
Edit intent: clarity
Original:  Trained GANswithout pre-defined control , however, may entangle factors that are hard to undo later.
Revised:  Unconditional GANs , however, may entangle factors that are hard to undo later.
Edit intent: clarity
Original:  To guarantee some disentanglement that provides us with desired kinds of control, we train our generative model conditioned on pre-defined control parameters  . Specifically, we condition StyleGAN2 on FLAME, a generative 3D face model.
Revised:  We condition our generative model conditioned on pre-defined control parameters . Specifically, we condition StyleGAN2 on FLAME, a generative 3D face model.
Edit intent: coherence
Original:  To guarantee some disentanglement that provides us with desired kinds of control, we train our generative model conditioned on pre-defined control parameters  . Specifically, we condition StyleGAN2 on FLAME, a generative 3D face model.
Revised:  To guarantee some disentanglement that provides us with desired kinds of control, we train our generative model on pre-defined control parameters . Specifically, we condition StyleGAN2 on FLAME, a generative 3D face model.
Edit intent: clarity
Original:  To guarantee some disentanglement that provides us with desired kinds of control, we train our generative model conditioned on pre-defined control parameters  . Specifically, we condition StyleGAN2 on FLAME, a generative 3D face model.
Revised:  To guarantee some disentanglement that provides us with desired kinds of control, we train our generative model conditioned on pre-defined control parameters to encourage disentanglement in the generation process . Specifically, we condition StyleGAN2 on FLAME, a generative 3D face model.
Edit intent: clarity
Original:  However, we found out that a naive conditioning on FLAME parameters yields rather unsatisfactory results.
Revised:  While conditioning on FLAME parameters yields rather unsatisfactory results.
Edit intent: coherence
Original:  This gives us a generative 2D face model named GIF (Generative Interpretable Faces) that shares FLAME's parametric control.
Revised:  This gives us a generative 2D face model named GIF (Generative Interpretable Faces) that offers FLAME's parametric control.
Edit intent: clarity
Original:   Given FLAME parameters for shape, pose, and expressions, parameters for appearance and lighting, and an additional style vector, GIF outputs photo-realistic face images.
Revised:  Given FLAME parameters for shape, pose, expressions, parameters for appearance and lighting, and an additional style vector, GIF outputs photo-realistic face images.
Edit intent: fluency
Original:   Given FLAME parameters for shape, pose, and expressions, parameters for appearance and lighting, and an additional style vector, GIF outputs photo-realistic face images.
Revised:  Given FLAME parameters for shape, pose, and expressions, parameters for appearance , lighting, and an additional style vector, GIF outputs photo-realistic face images.
Edit intent: fluency
Original:  To evaluate how well GIF follows its conditioning and the impact of different design choices, we perform a perceptual study .
Revised:  To evaluate how well GIF follows its conditioning .
Edit intent: clarity
Original:  The code  and trained model are publicly available for research purposes at URL
Revised:  The code , data, and trained model are publicly available for research purposes at URL
Edit intent: clarity
Original:  A discussion on several extensions and applications concludes this article.
Revised:  A discussion on several extensions and applications conclude this article.
Edit intent: fluency
Original: We explore the use of policy approximation for reducing the computational cost of learning Nash equilibria in multi-agent reinforcement learning scenarios .
Revised: We explore the use of policy approximations to reduce the computational cost of learning Nash equilibria in multi-agent reinforcement learning scenarios .
Edit intent: fluency
Original: We explore the use of policy approximation for reducing the computational cost of learning Nash equilibria in multi-agent reinforcement learning scenarios .
Revised: We explore the use of policy approximation for reducing the computational cost of learning Nash equilibria in zero-sum stochastic games .
Edit intent: clarity
Original:  We propose a new algorithm for zero-sum stochastic games in which each agent simultaneously learns a Nash policy and an entropy-regularized policy.
Revised:  We propose a new Q-learning type algorithm that uses a sequence of entropy-regularized policy.
Edit intent: clarity
Original:  We demonstrate the possibility of using the proposed algorithm to  transfer previous training experiences to different environments , enabling the agents to adapt quickly to new tasks.
Revised:  We demonstrate the possibility of using the proposed algorithm to transfer previous training experiences , enabling the agents to adapt quickly to new tasks.
Edit intent: clarity
Original:  We also provide a dynamic hyper-parameter scheduling scheme for further expedited convergence.
Revised:  We also provide a dynamic hyper-parameter scheduling scheme to further expedite convergence.
Edit intent: fluency
Original:  Empirical results applied to a number of stochastic games show that the proposed algorithm converges to the Nash equilibrium  while exhibiting a major speed-up over existing algorithms.
Revised:  Empirical results applied to a number of stochastic games verify that the proposed algorithm converges to the Nash equilibrium while exhibiting a major speed-up over existing algorithms.
Edit intent: clarity
Original:  Empirical results applied to a number of stochastic games show that the proposed algorithm converges to the Nash equilibrium  while exhibiting a major speed-up over existing algorithms.
Revised:  Empirical results applied to a number of stochastic games show that the proposed algorithm converges to the Nash equilibrium , while exhibiting a major speed-up over existing algorithms.
Edit intent: fluency
Original: Explainability is one of the key ethical concepts in the design of machine learning systems.
Revised: Explainability is one of the key ethical concepts in the design of AI systems.
Edit intent: clarity
Original:  However, attempts to operationalize this concept thus far have tended to focus on  new software for model interpretability or guidelines with checklists.
Revised:  However, attempts to operationalize this concept thus far have tended to focus on approaches such as new software for model interpretability or guidelines with checklists.
Edit intent: clarity
Original:  We present a set of case studies of a hypothetical machine learning-enabled product, which serves as a pedagogical tool to empower product designers, developers, students, and educators to develop a holistic explainability strategy for their own products.
Revised:  We present a set of case studies of a hypothetical AI-enabled product, which serves as a pedagogical tool to empower product designers, developers, students, and educators to develop a holistic explainability strategy for their own products.
Edit intent: clarity
Original:  From the theoretical analysis, we prove that the needs-drive cooperation in a heterogeneous robot system enables higher group utility compared to a homogeneous robot system.
Revised:  From the theoretical analysis, we prove that the needs-driven cooperation in a heterogeneous robot system enables higher group utility compared to a homogeneous robot system.
Edit intent: fluency
Original:  From the theoretical analysis, we prove that the needs-drive cooperation in a heterogeneous robot system enables higher group utility compared to a homogeneous robot system.
Revised:  From the theoretical analysis, we prove that the needs-drive cooperation in a heterogeneous robot system enables higher group utility than a homogeneous robot system.
Edit intent: fluency
Original:  We also perform simulation experiments to verify the proposed needs-driven cooperation and show that the heterogeneous multi-robot cooperation can achieve better performance and increase system robustness by reducing uncertainty in task execution.
Revised:  We also perform simulation experiments to verify the proposed needs-driven collaboration and show that the heterogeneous multi-robot cooperation can achieve better performance and increase system robustness by reducing uncertainty in task execution.
Edit intent: fluency
Original:  The choice of a good sorting function is a key ingredient for the efficiency of the method.
Revised:  The choice of sorting function is a key ingredient for the efficiency of the method.
Edit intent: clarity
Original: Does the enemy of my enemy become my fiend ? A growing literature on structural analysis of interstate relationships has tackled this old question from the network perspective.
Revised: Does the enemy of my enemy become my friend ? A growing literature on structural analysis of interstate relationships has tackled this old question from the network perspective.
Edit intent: clarity
Original:  Most prominent strategies include: discussing reviews with collaborators and making a resubmission plan, doing a major revision to improve the work, shortening the work, and seeing rejection as a positive learning experience.
Revised:  The most prominent strategies include: discussing reviews with collaborators and making a resubmission plan, doing a major revision to improve the work, shortening the work, and seeing rejection as a positive learning experience.
Edit intent: style
Original:  Since neither Weak K\"onig's Lemma nor the fan theorem are a consequence of just logical axioms or just choice-like axioms assumed in constructive logic, it seems likely that CT is only inconsistent with a combination of classical logic and choice axioms.
Revised:  Since neither Weak K\"onig's Lemma nor the fan theorem are a consequence of just logical axioms or just choice-like axioms assumed in constructive logic, it seems likely that CT is inconsistent only with a combination of classical logic and choice axioms.
Edit intent: fluency
Original:  The new scaling method is then based on the Sinkhorn-Knopp algorithm for scaling a square nonnegative matrix with total support to be doubly stochastic  .
Revised:  The new scaling methods are then based on the Sinkhorn-Knopp algorithm for scaling a square nonnegative matrix with total support to be doubly stochastic .
Edit intent: fluency
Original:  In addition, using results of U. G. Rothblum and H. Schneider (1989), we give sufficient conditions for the existence of diagonal scalings of square nonnegative matrices to be not only doubly stochastic but have any prescribed common vector for the row and column sums.
Revised:  In addition, using results of U. G. Rothblum and H. Schneider (1989), we give sufficient conditions for the existence of diagonal scalings of square nonnegative matrices to have any prescribed common vector for the row and column sums.
Edit intent: clarity
Original:  We illustrate numerically that the new scaling techniques for pencils improve the sensitivity of the computation of their eigenvalues.
Revised:  We illustrate numerically that the new scaling techniques for pencils improve the accuracy of the computation of their eigenvalues.
Edit intent: clarity
Original:  To this end, we first introduce a tensor relational algebra (TRA), which is expressive to encode any tensor operation represented by the Einstein notation ;
Revised:  To this end, we first introduce a tensor relational algebra (TRA), which is expressive to encode any tensor operation that can be written in the Einstein notation ;
Edit intent: clarity
Original:  We consider how TRA expressions can be re-written into an implementation algebra (IA) that enables effective implementation in a distributed environment, as well as how expressions in the IA can be optimized .
Revised:  We consider how TRA expressions can be re-written into an implementation algebra (IA) that enables effective implementation in a distributed environment, and amenable to automatic optimization .
Edit intent: clarity
Original:  Our empirical study shows that the optimized implementation provided by IA can reach or even out-perform carefully engineered HPC or ML systems for large scale tensor manipulations and ML workflows in distributed clusters.
Revised:  Our empirical study shows that the optimized TRA-based back-end can significantly outperform alternatives for running ML workflows in distributed clusters.
Edit intent: clarity
Original:  This gives yet another class of Cayley graphs with properties similar to the ones provided by Huang's results.
Revised:  This gives classes of Cayley graphs with properties similar to the ones provided by Huang's results.
Edit intent: clarity
Original:  We named it the SINC approach.
Revised:  We name it the SINC approach.
Edit intent: fluency
Original:  Results: Here, we relax this constraint by posing the motif finding problem as a graph representation learning and clustering task.
Revised:  Results: Here, we relax these constraints by posing the motif finding problem as a graph representation learning and clustering task.
Edit intent: fluency
Original:  This framing takes advantage of the continuous nature of graph representations to model the flexibility  of RNA motifs while retaining the convenient encoding of RNAs as graphs .
Revised:  This framing takes advantage of the continuous nature of graph representations to model the flexibility of RNA motifs in an efficient manner .
Edit intent: clarity
Original:  We show that our methods are able to retrieve and expand known classes of motifs, but also to identify new motifs.
Edit intent: coherence
Original:  To mitigate this issue, we impose locally non-interfering communication constraints that must be respected by every agent.
Revised:  To mitigate this issue, we impose locally non-interfering connectivity constraints that must be respected by every agent.
Edit intent: clarity
Original: In this manuscript  we study the properties of a family of a second order differential equations with damping, its discretizations and their connections with accelerated optimization algorithms for m-strongly convex and L-smooth functions.
Revised: In this manuscript , we study the properties of a family of a second order differential equations with damping, its discretizations and their connections with accelerated optimization algorithms for m-strongly convex and L-smooth functions.
Edit intent: fluency
Original: In this manuscript  we study the properties of a family of a second order differential equations with damping, its discretizations and their connections with accelerated optimization algorithms for m-strongly convex and L-smooth functions.
Revised: In this manuscript we study the properties of a family of second-order differential equations with damping, its discretizations and their connections with accelerated optimization algorithms for m-strongly convex and L-smooth functions.
Edit intent: fluency
Original:  We propose here an extension of these approaches,  using non-linear slope tomography (rather than standard tomographic migration velocity analysis as in previous publications).
Revised:  We propose an extension of these approaches, using non-linear slope tomography (rather than standard tomographic migration velocity analysis as in previous publications).
Edit intent: clarity
Original:  We derive and discuss the theoretical concepts underlying this approach and compare our derivations with those of previous publications.
Revised:  We derive the theoretical concepts underlying this approach and compare our derivations with those of previous publications.
Edit intent: clarity
Original:  We derive and discuss the theoretical concepts underlying this approach and compare our derivations with those of previous publications.
Revised:  We derive and discuss the theoretical concepts underlying this approach and unify our derivations with those of previous publications.
Edit intent: clarity
Original: This paper introduces WaveGrad, a conditional model for waveform generation through estimating gradients of the data density.
Revised: This paper introduces WaveGrad, a conditional model for waveform generation which estimates gradients of the data density.
Edit intent: clarity
Original:  This model is built on the prior work on score matching and diffusion probabilistic models.
Revised:  The model is built on the prior work on score matching and diffusion probabilistic models.
Edit intent: fluency
Original:  This model is built on the prior work on score matching and diffusion probabilistic models.
Revised:  This model is built on prior work on score matching and diffusion probabilistic models.
Edit intent: fluency
Original:  It starts from  Gaussian white noise  and iteratively refines the signal via a gradient-based sampler conditioned on the mel-spectrogram.
Revised:  It starts from a Gaussian white noise and iteratively refines the signal via a gradient-based sampler conditioned on the mel-spectrogram.
Edit intent: fluency
Original:  It starts from  Gaussian white noise  and iteratively refines the signal via a gradient-based sampler conditioned on the mel-spectrogram.
Revised:  It starts from Gaussian white noise signal and iteratively refines the signal via a gradient-based sampler conditioned on the mel-spectrogram.
Edit intent: clarity
Original:  Empirical experiments reveal WaveGrad to generate high fidelity audio samples matching a strong likelihood-based autoregressive baseline with less sequential operations. 
Revised:  Empirical experiments reveal WaveGrad to generate high fidelity audio samples matching a strong likelihood-based autoregressive baseline using fewer sequential operations. 
Edit intent: fluency
Original: Motivation Machine learning methods for predicting compounds protein interactions (CPIs) are crucial for the in-silico screening step of drug discovery.
Revised: The prediction of protein interactions (CPIs) are crucial for the in-silico screening step of drug discovery.
Edit intent: clarity
Original: Motivation Machine learning methods for predicting compounds protein interactions (CPIs) are crucial for the in-silico screening step of drug discovery.
Revised: Motivation Machine learning methods for predicting compounds protein interactions (CPIs) is crucial for the in-silico screening step of drug discovery.
Edit intent: fluency
Original: Motivation Machine learning methods for predicting compounds protein interactions (CPIs) are crucial for the in-silico screening step of drug discovery.
Revised: Motivation Machine learning methods for predicting compounds protein interactions (CPIs) are crucial for the in-silico screening step in drug discovery.
Edit intent: clarity
Original:  In recent years , many end-to-end representation learning methods using deep neural networks have achieved significantly better performance than traditional machine learning algorithms.
Revised:  Recently , many end-to-end representation learning methods using deep neural networks have achieved significantly better performance than traditional machine learning algorithms.
Edit intent: clarity
Original:  Much effort has been paid on improving the capability of the model by taking advantage of neural attention mechanism , either in learning compound representation, or learning the interaction between protein representation and compound representation .
Revised:  Much effort has focused on the compound representation or the information extraction from the compound-protein interaction to improve the model capability by taking the advantage of the neural attention mechanism , either in learning compound representation, or learning the interaction between protein representation and compound representation .
Edit intent: clarity
Original:  Much effort has been paid on improving the capability of the model by taking advantage of neural attention mechanism , either in learning compound representation, or learning the interaction between protein representation and compound representation .
Revised:  Much effort has been paid on improving the capability of the model by taking advantage of neural attention mechanism .
Edit intent: coherence
Original:  However, seldom has been done to improve the protein representation learning, while current approaches have manifest flaw of lacking the ability of learning amino acids long-distance interactions , which are essential for determine the proteins properties due to protein folding.
Revised:  However, previous studies have paid little attention to representing the protein sequences, in which the long-range interactions of residue pairs are essential for determine the proteins properties due to protein folding.
Edit intent: clarity
Original:  Results The authors propose a novel approach for incorporating self-attention in the protein representation learning module of CPI models, providing the module with the capability of capturing long-distance interaction information within proteins.
Revised:  We incorporate the self-attention in the protein representation learning module of CPI models, providing the module with the capability of capturing long-distance interaction information within proteins.
Edit intent: clarity
Original:  Results The authors propose a novel approach for incorporating self-attention in the protein representation learning module of CPI models, providing the module with the capability of capturing long-distance interaction information within proteins.
Revised:  Results The authors propose a novel approach for incorporating self-attention mechanism into the protein representation learning module of CPI models, providing the module with the capability of capturing long-distance interaction information within proteins.
Edit intent: clarity
Original:  Results The authors propose a novel approach for incorporating self-attention in the protein representation learning module of CPI models, providing the module with the capability of capturing long-distance interaction information within proteins.
Revised:  Results The authors propose a novel approach for incorporating self-attention in the protein representation module for CPI modeling, which aims at capturing the long-range interaction information within proteins.
Edit intent: clarity