wanyu/IteraTeR_v2 huggingface dataset:
Original:  Finally , we show that in a challenging language processing task , the delayed RNN performs within 0.3%DIFDELCMD < \\%%% %DIF < of the accuracy of the bidirectional network while reducing computational costs .
Revised:  Finally , we show that in a challenging language processing task %DIFDELCMD < \\%%% %DIF < of the accuracy of the bidirectional network while reducing computational costs .
Edit intent: clarity
Original: A versatile and effective approach to meta-learning is to infer a gradient-based up-date rule directly from data that promotes rapid learning of new tasks from the same distribution . Current methods rely on backpropagating through the learning process , limiting their scope to few-shot learning .
Revised: Learning an efficient update rule from data that promotes rapid learning of new tasks from the same distribution . Current methods rely on backpropagating through the learning process , limiting their scope to few-shot learning .
Edit intent: clarity
Original: A versatile and effective approach to meta-learning is to infer a gradient-based up-date rule directly from data that promotes rapid learning of new tasks from the same distribution . Current methods rely on backpropagating through the learning process , limiting their scope to few-shot learning .
Revised: A versatile and effective approach to meta-learning is to infer a gradient-based up-date rule directly from data that promotes rapid learning of new tasks from the same distribution . Current methods rely on backpropagating through the learning process , limiting their scope to few-shot task adaptation .
Edit intent: clarity
Original:  In this work, we introduce Warped Gradient Descent (WarpGrad), a family of modular optimisers that can scale to arbitrary adaptation processes.
Revised:  In this work, we propose Warped Gradient Descent (WarpGrad), a family of modular optimisers that can scale to arbitrary adaptation processes.
Edit intent: clarity
Original:  We show empirically that WarpGrad optimisers meta-learn a warped space where gradient descent is well behaved, with faster convergence and better performance in a variety of settings, including few-shot, standard supervised, continual , and reinforcement learning.
Revised:  We show empirically that WarpGrad optimisers meta-learn a warped space where gradient descent is well behaved, with faster convergence and better performance in a variety of settings, including few-shot, standard supervised, continual and reinforcement learning.
Edit intent: fluency
Original:  We also derive the analytic steady-state distributions of the protein abundance  for both the discrete chemical master equation model and its two macroscopic limits .
Revised:  We derive the analytic steady-state distributions of the protein abundance for both the discrete chemical master equation model and its two macroscopic limits .
Edit intent: clarity
Original:  We also derive the analytic steady-state distributions of the protein abundance  for both the discrete chemical master equation model and its two macroscopic limits .
Revised:  We also derive the analytic steady-state distributions of the protein abundance for both the discrete model and its two macroscopic limits .
Edit intent: clarity
Original:  Furthermore, we ] obtain the analytic time-dependent distribution of the protein concentration for the classical Friedman-Cai-Xie random bursting model   .
Revised:  Furthermore, we ]. We also obtain the analytic time-dependent distribution of the protein concentration for the classical Friedman-Cai-Xie random bursting model .
Edit intent: coherence
Original:  Furthermore, we ] obtain the analytic time-dependent distribution of the protein concentration for the classical Friedman-Cai-Xie random bursting model   .
Revised:  Furthermore, we ] obtain the analytic time-dependent protein distribution for the classical Friedman-Cai-Xie random bursting model .
Edit intent: clarity
Original:  Our theory is also applied to study the intrinsic noise structure of stochastic gene expression in coupled gene circuits and a complete decomposition of noise in terms of five different biophysical origins is provided.
Revised:  Our theory is also applied to study the structure of gene circuits and a complete decomposition of noise in terms of five different biophysical origins is provided.
Edit intent: clarity
Original: When the data is distributed across multiple servers, efficient data exchange between the servers (or workers) for solving the distributed learning problem is an important problem and is the focus of this paper.
Revised: When the data is distributed across multiple servers, lowering the communication cost between the servers (or workers) for solving the distributed learning problem is an important problem and is the focus of this paper.
Edit intent: clarity
Original: When the data is distributed across multiple servers, efficient data exchange between the servers (or workers) for solving the distributed learning problem is an important problem and is the focus of this paper.
Revised: When the data is distributed across multiple servers, efficient data exchange between the servers (or workers) while solving the distributed learning problem is an important problem and is the focus of this paper.
Edit intent: clarity
Original:  The proposed algorithm, GADMM, is based on Alternating Direct Method of Multiplier (ADMM) algorithm.
Revised:  The proposed algorithm, GADMM, is based on the Alternating Direction Method of Multipliers (ADMM) algorithm.
Edit intent: fluency
Original:  The key novelty in GADMM is that  each worker exchanges the locally trained model only with two neighboring workers, thereby training a global model with  lower amount of communication  in each exchange.
Revised:  The key novelty in GADMM is that each worker exchanges the locally trained model only with two neighboring workers, thereby training a global model with a lower amount of communication in each exchange.
Edit intent: fluency
Original:  The key novelty in GADMM is that  each worker exchanges the locally trained model only with two neighboring workers, thereby training a global model with  lower amount of communication  in each exchange.
Revised:  The key novelty in GADMM is that each worker exchanges the locally trained model only with two neighboring workers, thereby training a global model with lower amount of communication overhead in each exchange.
Edit intent: clarity
Original:  We prove that GADMM converges faster than the centralized batch gradient descent for convex loss functions, and numerically show that it is faster and more communication-efficient than the state-of-the-art communication-efficient centralized algorithms such as the Lazily Aggregated Gradient (LAG)  , in linear and logistic regression tasks on synthetic and real datasets.
Revised:  We prove that GADMM converges faster than the centralized batch gradient descent for convex loss functions, and numerically show that it converges faster and more communication-efficient than the state-of-the-art communication-efficient centralized algorithms such as the Lazily Aggregated Gradient (LAG) , in linear and logistic regression tasks on synthetic and real datasets.
Edit intent: fluency
Original:  We prove that GADMM converges faster than the centralized batch gradient descent for convex loss functions, and numerically show that it is faster and more communication-efficient than the state-of-the-art communication-efficient centralized algorithms such as the Lazily Aggregated Gradient (LAG)  , in linear and logistic regression tasks on synthetic and real datasets.
Revised:  We prove that GADMM converges faster than the centralized batch gradient descent for convex loss functions, and numerically show that it is faster and more communication-efficient than the state-of-the-art communication-efficient algorithms such as the Lazily Aggregated Gradient (LAG) , in linear and logistic regression tasks on synthetic and real datasets.
Edit intent: clarity
Original:  Furthermore, we propose Dynamic GADMM (D-GADMM), a variant of GADMM, and prove its convergence under  time-varying network topology of the workers.
Revised:  Furthermore, we propose Dynamic GADMM (D-GADMM), a variant of GADMM, and prove its convergence under the time-varying network topology of the workers.
Edit intent: fluency
Original:  We propose a fast, privacy-aware, and communication-efficient decentralized framework to solve the distributed machine learning (DML) problem.
Revised:  In particular, we propose a fast, privacy-aware, and communication-efficient decentralized framework to solve the distributed machine learning (DML) problem.
Edit intent: coherence
Original:  We propose a fast, privacy-aware, and communication-efficient decentralized framework to solve the distributed machine learning (DML) problem.
Revised:  We propose a fast, and communication-efficient decentralized framework to solve the distributed machine learning (DML) problem.
Edit intent: clarity
Original:  The proposed algorithm, GADMM, is based on the Alternating Direction Method of Multipliers (ADMM) algorithm .
Revised:  The proposed algorithm, GADMM, is based on the Alternating Direction Method of Multipliers (ADMM) framework .
Edit intent: clarity
Original:  The key novelty in GADMM is that it solves the problem in a decentralized topology where at most half of the workers are competing  the limited communication resources at any given time.
Revised:  The key novelty in GADMM is that it solves the problem in a decentralized topology where at most half of the workers are competing for the limited communication resources at any given time.
Edit intent: clarity
Original:  We prove that GADMM converges faster than the gradient descent (GD) algorithm for convex loss functions, and numerically show that it converges faster and more communication-efficient than the state-of-the-art communication-efficient algorithms such as the Lazily Aggregated Gradient (LAG) and dual averaging, in linear and logistic regression tasks on synthetic and real datasets.
Revised:  We prove that GADMM converges to the optimal solution for convex loss functions, and numerically show that it converges faster and more communication-efficient than the state-of-the-art communication-efficient algorithms such as the Lazily Aggregated Gradient (LAG) and dual averaging, in linear and logistic regression tasks on synthetic and real datasets.
Edit intent: clarity
Original: Recently, in the paper `` Weight Agnostic Neural Networks '' Gaier \& Ha utilized architecture search to find networks where the topology completely encodes the knowledge.
Revised: Recently, in the paper " Weight Agnostic Neural Networks '' Gaier \& Ha utilized architecture search to find networks where the topology completely encodes the knowledge.
Edit intent: fluency
Original: Recently, in the paper `` Weight Agnostic Neural Networks '' Gaier \& Ha utilized architecture search to find networks where the topology completely encodes the knowledge.
Revised: Recently, in the paper `` Weight Agnostic Neural Networks " Gaier Ha utilized architecture search to find networks where the topology completely encodes the knowledge.
Edit intent: fluency
Original: Machine learning (ML) has proven itself in high-value web applications such as search ranking and is emerging as a powerful tool in a much broader range of enterprise scenarios including voice recognition and conversational understanding for customer support, autotuning for videoconferencing, inteligent feedback loops in largescale sysops, manufacturing and autonomous vehicle management, complex financial predictions, just to name a few.
Revised: Machine learning (ML) has proven itself in high-value web applications such as search ranking and is emerging as a powerful tool in a much broader range of enterprise scenarios including voice recognition and conversational understanding for customer support, autotuning for videoconferencing, intelligent feedback loops in largescale sysops, manufacturing and autonomous vehicle management, complex financial predictions, just to name a few.
Edit intent: fluency
Original: Machine learning (ML) has proven itself in high-value web applications such as search ranking and is emerging as a powerful tool in a much broader range of enterprise scenarios including voice recognition and conversational understanding for customer support, autotuning for videoconferencing, inteligent feedback loops in largescale sysops, manufacturing and autonomous vehicle management, complex financial predictions, just to name a few.
Revised: Machine learning (ML) has proven itself in high-value web applications such as search ranking and is emerging as a powerful tool in a much broader range of enterprise scenarios including voice recognition and conversational understanding for customer support, autotuning for videoconferencing, inteligent feedback loops in large-scale sysops, manufacturing and autonomous vehicle management, complex financial predictions, just to name a few.
Edit intent: fluency
Original:  Moreover,  simple two-sided permutation of the antitriangular form transforms the matrix into  multi-arrowhead matrix.
Revised:  Moreover, a simple two-sided permutation of the antitriangular form transforms the matrix into multi-arrowhead matrix.
Edit intent: fluency
Original:  Moreover,  simple two-sided permutation of the antitriangular form transforms the matrix into  multi-arrowhead matrix.
Revised:  Moreover, simple two-sided permutation of the antitriangular form transforms the matrix into a multi-arrowhead matrix.
Edit intent: fluency
Original:  The first algorithm is similar to the algorithm for the block antitriangular factorization of symmetric matrices, but in the case of skew-symmetric matrices,  antitriangular form is always obtained.
Revised:  The first algorithm is similar to the algorithm for the block antitriangular factorization of symmetric matrices, but in the case of skew-symmetric matrices, an antitriangular form is always obtained.
Edit intent: fluency
Original:  In addition, we show here that the block antitriangular form of the skew-Hermitian matrices has the same structure as the block antitriangular form of the symmetric matrices.
Revised:  In addition, we show that the block antitriangular form of the skew-Hermitian matrices has the same structure as the block antitriangular form of the symmetric matrices.
Edit intent: coherence
Original:  or a cluster of those, performs all non-trivial computation exclusively on the GPUs, utilizes their resources to almost the full extent with data large enough, requires the minimal amount of memory to be reasonably expected, scales satisfactorily with the increase of the number of GPUs available, and guarantees the reproducible, bitwise identical output of the runs repeated over the same input and with the same number of GPUs.
Revised:  or a cluster of those, performs all non-trivial computation exclusively on the GPUs, requires the minimal amount of memory to be reasonably expected, scales satisfactorily with the increase of the number of GPUs available, and guarantees the reproducible, bitwise identical output of the runs repeated over the same input and with the same number of GPUs.
Edit intent: clarity
Original:  or a cluster of those, performs all non-trivial computation exclusively on the GPUs, utilizes their resources to almost the full extent with data large enough, requires the minimal amount of memory to be reasonably expected, scales satisfactorily with the increase of the number of GPUs available, and guarantees the reproducible, bitwise identical output of the runs repeated over the same input and with the same number of GPUs.
Revised:  or a cluster of those, performs all non-trivial computation exclusively on the GPUs, utilizes their resources to almost the full extent with data large enough, requires the minimal amount of memory to be reasonably expected, scales acceptably with the increase of the number of GPUs available, and guarantees the reproducible, bitwise identical output of the runs repeated over the same input and with the same number of GPUs.
Edit intent: clarity
Original: In this paper, we report our discovery on named entity distribution in  general word embedding space, which helps an open definition on multilingual named entity definition rather than previous closed and constraint definition on named entities through a named entity dictionary, which is usually derived from huaman labor and replies on schedual update.
Revised: In this paper, we report our discovery on named entity distribution in a general word embedding space, which helps an open definition on multilingual named entity definition rather than previous closed and constraint definition on named entities through a named entity dictionary, which is usually derived from huaman labor and replies on schedual update.
Edit intent: fluency
Original: In this paper, we report our discovery on named entity distribution in  general word embedding space, which helps an open definition on multilingual named entity definition rather than previous closed and constraint definition on named entities through a named entity dictionary, which is usually derived from huaman labor and replies on schedual update.
Revised: In this paper, we report our discovery on named entity distribution in general word embedding space, which helps an open definition on multilingual named entity definition rather than previous closed and constraint definition on named entities through a named entity dictionary, which is usually derived from human labor and replies on schedual update.
Edit intent: fluency
Original: In this paper, we report our discovery on named entity distribution in  general word embedding space, which helps an open definition on multilingual named entity definition rather than previous closed and constraint definition on named entities through a named entity dictionary, which is usually derived from huaman labor and replies on schedual update.
Revised: In this paper, we report our discovery on named entity distribution in general word embedding space, which helps an open definition on multilingual named entity definition rather than previous closed and constraint definition on named entities through a named entity dictionary, which is usually derived from huaman labor and replies on schedule update.
Edit intent: fluency
Original:  For monolingual case , the proposed named entity model gives an open description on diverse named entity types and different languages.
Revised:  For monolingual cases , the proposed named entity model gives an open description on diverse named entity types and different languages.
Edit intent: fluency
Original:  For monolingual case , the proposed named entity model gives an open description on diverse named entity types and different languages.
Revised:  For monolingual case , the proposed named entity model gives an open description of diverse named entity types and different languages.
Edit intent: fluency
Original:  For cross-lingual case , mapping the proposed named entity model provides a novel way to build  named entity dataset for resource-poor languages.
Revised:  For cross-lingual cases , mapping the proposed named entity model provides a novel way to build named entity dataset for resource-poor languages.
Edit intent: fluency
Original:  For cross-lingual case , mapping the proposed named entity model provides a novel way to build  named entity dataset for resource-poor languages.
Revised:  For cross-lingual case , mapping the proposed named entity model provides a novel way to build a named entity dataset for resource-poor languages.
Edit intent: fluency
Original:  At last, the proposed named entity model may be shown as a very useful clue to significantly enhance state-of-the-art named entity recognition systems generally.
Revised:  At last, the proposed named entity model may be shown as a handy clue to enhance state-of-the-art named entity recognition systems generally.
Edit intent: clarity
Original:  Our process, which we call "homotopic curve shortening" (HCS), starts with a closed curve (which might self-intersect) in the presence of a set P\subset \mathbb R^2 of point obstacles, and evolves in discrete steps, where each step consists of (1) taking shortucts around the obstacles, and (2) reducing the curve to its shortest homotopic equivalent.
Revised:  Our process, which we call "homotopic curve shortening" (HCS), starts with a closed curve (which might self-intersect) in the presence of a set P\subset \mathbb R^2 of point obstacles, and evolves in discrete steps, where each step consists of (1) taking shortcuts around the obstacles, and (2) reducing the curve to its shortest homotopic equivalent.
Edit intent: fluency
Original:  A seller has a set of substitute productswith exogenously-given prices . Each buyer has a ranked list from which she would like to purchaseat most one product.
Revised:  A seller has a set of substitute productswith exogenously-given prices . Each buyer reports a ranked list from which she would like to purchaseat most one product.
Edit intent: clarity
Original:  The buyers report their lists to the seller , who then allocates products to the buyers using a truthful mechanism, subject to constraints on how many products can be allocated.
Revised:  The buyers report their lists to the seller , who then allocates products to buyers using a truthful mechanism, subject to constraints on how many products can be allocated.
Edit intent: fluency
Original:  The buyers report their lists to the seller , who then allocates products to the buyers using a truthful mechanism, subject to constraints on how many products can be allocated.
Revised:  The buyers report their lists to the seller , who then allocates products to the buyers using a truthful mechanism, subject to a supply constraint on the total number of products allocated.
Edit intent: clarity
Original:  The seller collects revenues equal to the prices of the products allocated, and would like to design an auction to maximize total revenue, when the buyers' lists are drawn independently from known distributions.
Revised:  The seller collects revenues equal to the prices of the products allocated, and would like to design a mechanism to maximize total revenue, when the buyers' lists are drawn independently from known distributions.
Edit intent: clarity
Original:  If there is a single buyer, then our problem reduces to the assortment optimization problem, which is solved for Markov Chain choice models .
Revised:  When there is one buyer, our mechanism design problem reduces to the assortment optimization problem, which is solved for Markov Chain choice models .
Edit intent: clarity
Original:  If there is a single buyer, then our problem reduces to the assortment optimization problem, which is solved for Markov Chain choice models .
Revised:  If there is a single buyer, then our problem reduces to the assortment optimization problem, which is solved for Markov Chain choice model .
Edit intent: fluency
Original:  We extend this result and compute the optimal assortment auctionwhen each buyer's list distribution arises from its own Markov chain .
Revised:  We extend this result and compute the optimal auction, when there are multiple buyers with heterogeneous Markov chains .
Edit intent: clarity
Original:  Moreover, we show that the optimal auction is structurally ``Myersonian'', in that each buyer is assigned a virtual valuation based on her list and Markov chain, and then the mechanism maximizes virtual surplus.
Revised:  Moreover, we show that the optimal auction is structurally ``Myersonian'', in that each buyer is assigned a virtual valuation based on her report and distribution, after which the mechanism maximizes virtual surplus.
Edit intent: clarity
Original:  Since Markov Chain choice models capture valuation distributions , our optimal assortment auction generalizes the classical Myerson's auction.
Revised:  Since Markov chains capture the classical notion of a valuation distribution , our optimal assortment auction generalizes the classical Myerson's auction.
Edit intent: clarity