wanyu/IteraTeR_v2 huggingface dataset:
Original:  The current mathematical theory in the lens of regularization theory predicts that SGD with a polynomially decaying stepsize schedule may suffer from an undesirable saturation phenomenon, i.e., the convergence rate does not further improve with the solution regularity index if it is beyond a certain range.
Revised:  The current mathematical theory in the lens of regularization theory predicts that SGD with a polynomially decaying stepsize schedule may suffer from an undesirable saturation phenomenon, i.e., the convergence rate does not further improve with the solution regularity index when it is beyond a certain range.
Edit intent: clarity
Original:  Our result may be interpreted as an affirmative answer to a refined version of  Smale's 17th question, concerned with systems ofstructured polynomial equations .
Revised:  Our result may be interpreted as an affirmative answer to a refined version of Smale's 17th structured polynomial equations .
Edit intent: clarity
Original:  Our result may be interpreted as an affirmative answer to a refined version of  Smale's 17th question, concerned with systems ofstructured polynomial equations .
Revised:  Our result may be interpreted as an affirmative answer to a refined version of Smale's 17th question, concerned with systems of polynomial equations .
Edit intent: clarity
Original:  Our result may be interpreted as an affirmative answer to a refined version of  Smale's 17th question, concerned with systems ofstructured polynomial equations .
Revised:  Our result may be interpreted as an affirmative answer to a refined version of Smale's 17th question, concerned with systems ofstructured problem .
Edit intent: clarity
Original:  The stability analysis is performed in the matrix--vector formulation, and is independent of geometric arguments, which only enter the consistency analysis.
Revised:  The stability analysis is performed in the matrix-vector formulation, and is independent of geometric arguments, which only enter the consistency analysis.
Edit intent: fluency
Original: In academic research, recommender models are often evaluated offline on benchmark datasets.
Revised: In academic research, recommender models are often evaluated on offline datasets.
Edit intent: clarity
Original:  The offline dataset is first split to train and test instances.
Revised:  The offline dataset is first split to training and test instances.
Edit intent: fluency
Original:  All training instances are then modeled in a user-item interaction matrix , and supervised learning modelsare trained .
Revised:  All training instances are then modelled in a user-item interaction matrix , and supervised learning modelsare trained .
Edit intent: fluency
Original:  That is, ignoring the global timeline in offline evaluation makes the performance among recommendation models not comparable .
Revised:  That is, ignoring global timeline in offline evaluation makes the performance among recommendation models not comparable .
Edit intent: fluency
Original: In this work , we propose a novel scheduling algorithm with contiguous frequency-domain resource allocation (FDRA) based on deep reinforcement learning (DRL) that jointly selects users and allocates resource blocks (RBs).
Revised: Scheduling plays a pivotal role in multi-user wireless communications, since the quality of service of various users largely depends upon the allocated radio resources. In this paper , we propose a novel scheduling algorithm with contiguous frequency-domain resource allocation (FDRA) based on deep reinforcement learning (DRL) that jointly selects users and allocates resource blocks (RBs).
Edit intent: coherence
Original:  The state  , action, and reward sets are delicately designed to train the DRL network.
Revised:  The state , and reward sets are delicately designed to train the DRL network.
Edit intent: clarity
Original:  More specifically, the originally quasicontinuous action space, which is inherent to contiguous FDRA, is refined into a finite and discrete action space to obtain a tradeoff between the inference latency and system performance.
Revised:  More specifically, the originally quasi-continuous action space, which is inherent to contiguous FDRA, is refined into a finite and discrete action space to obtain a tradeoff between the inference latency and system performance.
Edit intent: fluency
Original:  More specifically, the originally quasicontinuous action space, which is inherent to contiguous FDRA, is refined into a finite and discrete action space to obtain a tradeoff between the inference latency and system performance.
Revised:  More specifically, the originally quasicontinuous action space, which is inherent to contiguous FDRA, is refined into a finite and discrete action space to obtain a trade-off between the inference latency and system performance.
Edit intent: fluency
Original:  Simulation results show that the proposed DRL-based  algorithm outperforms other representative baseline schemes while having lower online computational complexity.
Revised:  Simulation results show that the proposed DRL-based scheduling algorithm outperforms other representative baseline schemes while having lower online computational complexity.
Edit intent: clarity
Original:  These obstacles have become a serious problem in some fields, such as recording high-quality music  and speech processing.
Revised:  These obstacles have become a serious problem in some fields, such as high-quality music and speech processing.
Edit intent: clarity
Original:  In this paper, a novel approach using a temporal convolutional attention network (TCAN) is proposed to process this problem.
Revised:  In this paper, a novel approach using a temporal convolutional attention network (TCAN) is proposed to tackle this problem.
Edit intent: clarity
Original:  The experimental results show that the use of proposed model can yield good classification performances and outperforms some strong baseline methods, such as the LSTM and TCN based models, by about 3\sim 10\\%DIF < relatively.abstract %DIF > relative improvements.
Revised:  The experimental results show that the use of proposed model can yield better classification performances than some strong baseline methods, such as the LSTM and TCN based models, by about 3\sim 10\\%DIF < relatively.abstract %DIF > relative improvements.
Edit intent: clarity
Original:  The experimental results show that the use of proposed model can yield good classification performances and outperforms some strong baseline methods, such as the LSTM and TCN based models, by about 3\sim 10\\%DIF < relatively.abstract %DIF > relative improvements.
Revised:  The experimental results show that the use of proposed model can yield good classification performances and outperforms some strong baseline methods, such as the LSTM and TCN based models, by approximate 3\sim 10\\%DIF < relatively.abstract %DIF > relative improvements.
Edit intent: clarity
Original: An important problem on graph-structured data is that of quantifying similarity between graphs.
Revised: A fundamental problem on graph-structured data is that of quantifying similarity between graphs.
Edit intent: clarity
Original:  Since the seminal design of Fuzz, which is restricted to \epsilon -differential privacy, a lot of effort has been made to support more advanced variants of differential privacy, like ( \epsilon ,%DIFDELCMD < {%%% \delta )-differential privacy.
Revised:  Since the seminal design of Fuzz, which is restricted to \epsilon -differential privacy, a lot of effort has been made to support more advanced variants of differential privacy, like ( \epsilon ,%DIFDELCMD < {%%% \delta )-differential privacy.
Edit intent: fluency
Original:  Since the seminal design of Fuzz, which is restricted to \epsilon -differential privacy, a lot of effort has been made to support more advanced variants of differential privacy, like ( \epsilon ,%DIFDELCMD < {%%% \delta )-differential privacy.
Revised:  Since the seminal design of Fuzz, which is restricted to \epsilon -differential privacy, a lot of effort has been made to support more advanced variants of differential privacy, like ( ,%DIFDELCMD < {%%% \delta )-differential privacy.
Edit intent: fluency
Original:  Since the seminal design of Fuzz, which is restricted to \epsilon -differential privacy, a lot of effort has been made to support more advanced variants of differential privacy, like ( \epsilon ,%DIFDELCMD < {%%% \delta )-differential privacy.
Revised:  Since the seminal design of Fuzz, which is restricted to \epsilon -differential privacy, a lot of effort has been made to support more advanced variants of differential privacy, like ( \epsilon %DIFDELCMD < {%%% \delta )-differential privacy.
Edit intent: fluency
Original:  Since the seminal design of Fuzz, which is restricted to \epsilon -differential privacy, a lot of effort has been made to support more advanced variants of differential privacy, like ( \epsilon ,%DIFDELCMD < {%%% \delta )-differential privacy.
Revised:  Since the seminal design of Fuzz, which is restricted to \epsilon -differential privacy, a lot of effort has been made to support more advanced variants of differential privacy, like ( \epsilon ,%DIFDELCMD < {%%% \epsilon,\delta )-differential privacy.
Edit intent: fluency
Original:  However, no existing type system supports these advanced privacy variants while also supporting higher-order programming in full generality .
Revised:  However, supporting these advanced privacy variants while also supporting higher-order programming in full generality .
Edit intent: clarity
Original:  However, no existing type system supports these advanced privacy variants while also supporting higher-order programming in full generality .
Revised:  However, no existing type system supports these advanced privacy variants while also supporting higher-order programming in full has been proven to be challenging .
Edit intent: coherence
Original:  We present Jazz, a language and type system which uses linear types and latent contextual effects to support both advanced variants of differential privacy and higher order programming.
Revised:  We present Jazz, a language and type system which uses linear types and latent contextual effects to support both advanced variants of differential privacy and higher-order programming.
Edit intent: fluency
Original:  Even when avoiding advanced variants and higher order programming, our system achieves higher precision than prior work for a large class of programming patterns.
Revised:  Even when avoiding advanced variants and higher-order programming, our system achieves higher precision than prior work for a large class of programming patterns.
Edit intent: fluency
Original:  We trained our prediction networks on the behavioral data from several published psychological experiments of human decision making, and demonstrated a clear advantage over the state-of-the-art methods in predicting human decision making trajectories in both single-agent scenarios such as  Iowa Gambling Task and multi-agent scenarios such as  Iterated Prisoner's Dilemma  .
Revised:  We trained our prediction networks on the behavioral data from these published psychological experiments of human decision making, and demonstrated a clear advantage over the state-of-the-art methods in predicting human decision making trajectories in both single-agent scenarios such as Iowa Gambling Task and multi-agent scenarios such as Iterated Prisoner's Dilemma .
Edit intent: clarity
Original:  We trained our prediction networks on the behavioral data from several published psychological experiments of human decision making, and demonstrated a clear advantage over the state-of-the-art methods in predicting human decision making trajectories in both single-agent scenarios such as  Iowa Gambling Task and multi-agent scenarios such as  Iterated Prisoner's Dilemma  .
Revised:  We trained our prediction networks on the behavioral data from several published psychological experiments of human decision making, and demonstrate a clear advantage over the state-of-the-art methods in predicting human decision making trajectories in both single-agent scenarios such as Iowa Gambling Task and multi-agent scenarios such as Iterated Prisoner's Dilemma .
Edit intent: fluency
Original:  We trained our prediction networks on the behavioral data from several published psychological experiments of human decision making, and demonstrated a clear advantage over the state-of-the-art methods in predicting human decision making trajectories in both single-agent scenarios such as  Iowa Gambling Task and multi-agent scenarios such as  Iterated Prisoner's Dilemma  .
Revised:  We trained our prediction networks on the behavioral data from several published psychological experiments of human decision making, and demonstrated a clear advantage over the state-of-the-art methods in predicting human decision making trajectories in both single-agent scenarios such as the Iowa Gambling Task and multi-agent scenarios such as Iterated Prisoner's Dilemma .
Edit intent: fluency
Original:  We trained our prediction networks on the behavioral data from several published psychological experiments of human decision making, and demonstrated a clear advantage over the state-of-the-art methods in predicting human decision making trajectories in both single-agent scenarios such as  Iowa Gambling Task and multi-agent scenarios such as  Iterated Prisoner's Dilemma  .
Revised:  We trained our prediction networks on the behavioral data from several published psychological experiments of human decision making, and demonstrated a clear advantage over the state-of-the-art methods in predicting human decision making trajectories in both single-agent scenarios such as Iowa Gambling Task and multi-agent scenarios such as the Iterated Prisoner's Dilemma .
Edit intent: fluency
Original:  The method exploits the particular structure of the colleague linearization to achieve quadratic complexity and linear storage requirements.
Revised:  We exploit the particular structure of the colleague linearization to achieve quadratic complexity and linear storage requirements.
Edit intent: clarity
Original:  The (unbalanced) QR iteration used for Chebyshev rootfinding does not guarantee backward stability on the polynomial coefficients, unless the vector of coefficients satisfy %DIFDELCMD < \lVert %DIFDELCMD < %%% p%DIFDELCMD < \rVert %%%  \approx 1, an hypothesis which is almost never verified for polynomials approximating smooth functions.
Revised:  The (unbalanced) QR iteration used for Chebyshev rootfinding does not guarantee backward stability on the polynomial coefficients, unless the vector of coefficients satisfy %DIFDELCMD < \lVert %DIFDELCMD < %%% %DIFDELCMD < \rVert %%% \approx 1, an hypothesis which is almost never verified for polynomials approximating smooth functions.
Edit intent: clarity
Original:  To this end, considering the "width" of a connection, referred to as the s-overlap of neighbors, provides more meaningful insights into how closely the communities or entities interact with each other.
Revised:  To this end, considering the "width" of a connection, referred to as the s-overlap of neighbors, provides more meaningful insights into how closely the communities or entities interact with each other.
Edit intent: fluency
Original:  In addition, s-overlap computation is the fundamental kernel to construct the line graph of a hypergraph, a low-order approximation  which can carry significant information about the original hypergraph.
Revised:  In addition, s-overlap computation is the fundamental kernel to construct the line graph of a hypergraph, a low-order approximation of the hypergraph which can carry significant information about the original hypergraph.
Edit intent: clarity
Original:  ASteiner subgraph of (G, W) is a subgraph of G that contains all vertices of W and there is a path between every pair of vertices of W in the subgraph.
Revised:  A of (G, W) is a subgraph of G that contains all vertices of W and there is a path between every pair of vertices of W in the subgraph.
Edit intent: clarity
Original:  Syst. , 2008].
Revised:  Syst. 2008].
Edit intent: fluency
Original:  Our enumeration algorithm can be extended to other Steiner problems : minimal Steiner forests, minimal terminal Steiner trees,  minimal directed Steiner trees.
Revised:  Our enumeration algorithm can be extended to other Steiner problems , such as minimal Steiner forests, minimal terminal Steiner trees, minimal directed Steiner trees.
Edit intent: clarity
Original:  Our enumeration algorithm can be extended to other Steiner problems : minimal Steiner forests, minimal terminal Steiner trees,  minimal directed Steiner trees.
Revised:  Our enumeration algorithm can be extended to other Steiner problems : minimal Steiner forests, minimal terminal Steiner trees, and minimal directed Steiner trees.
Edit intent: fluency
Original:  As another variant of the minimal Steiner subgraph problem, we study the problem of enumerating minimal induced Steiner subgraphs.
Revised:  As another variant of the minimal Steiner tree enumeration problem, we study the problem of enumerating minimal induced Steiner subgraphs.
Edit intent: clarity
Original:  For instance, human can recognize speech, as well as a peculiar accent of the speech simultaneously.
Revised:  For instance, human can recognize speech, as well as the peculiar accent of the speech simultaneously.
Edit intent: fluency
Original:  In this paper, we propose a multilingual approach to recognizing English speech, as well as the related accent that the speakers convey using DNN-HMM framework.
Revised:  In this paper, we propose a multilingual approach to recognizing English speech, and related accent that the speakers convey using DNN-HMM framework.
Edit intent: coherence
Original:  In this paper, we propose a multilingual approach to recognizing English speech, as well as the related accent that the speakers convey using DNN-HMM framework.
Revised:  In this paper, we propose a multilingual approach to recognizing English speech, as well as the related accent that speaker conveys using DNN-HMM framework.
Edit intent: fluency
Original:  During decoding, we conduct two sets of experiments.
Revised:  During decoding, we conduct two experiments.
Edit intent: clarity
Original:  One is a monolingual Automatic Speech Recognition (ASR) system , with the accent information only embedded at the phone level, realizing word-based accent recognition  , and the other is a multilingual ASR system, with the accent information embedded at both word and phone level, realizing an approximated utterance-based accent recognition .
Revised:  One is a monolingual Automatic Speech Recognition (ASR) system , with the accent information embedded at phone level, realizing word-based accent recognition , and the other is a multilingual ASR system, with the accent information embedded at both word and phone level, realizing an approximated utterance-based accent recognition .
Edit intent: clarity
Original:  One is a monolingual Automatic Speech Recognition (ASR) system , with the accent information only embedded at the phone level, realizing word-based accent recognition  , and the other is a multilingual ASR system, with the accent information embedded at both word and phone level, realizing an approximated utterance-based accent recognition .
Revised:  One is a monolingual Automatic Speech Recognition (ASR) system , with the accent information only embedded at the phone level, realizing word-based accent recognition , and the other is a multilingual ASR-based decoding, realizing an approximated utterance-based accent recognition .
Edit intent: clarity
Original: We investigate to which extent the relevant features of (static) systemic risk measures can be extended to a conditional setting.
Revised: We investigate to which extent the relevant features of (static) Systemic Risk Measures can be extended to a conditional setting.
Edit intent: fluency
Original:  Conceptually, the generalization from static to conditional systemic risk measures can be achieved in a natural way, even though the proofs become more technical than in the unconditional framework.
Revised:  Conceptually, the generalization from static to conditional Systemic Risk Measures can be achieved in a natural way, even though the proofs become more technical than in the unconditional framework.
Edit intent: fluency
Original:  These enhancements aim to efficiently explore the search space tree by pruning subtrees, using a heuristic simulation policy, reducing the domain of variables by eliminating dominated solutions and using a beam width.
Revised:  These enhancements aim to efficiently explore the search space tree by pruning subtrees, using a heuristic simulation policy, reducing the domains of variables by eliminating dominated solutions and using a beam width.
Edit intent: fluency
Original:  These enhancements aim to efficiently explore the search space tree by pruning subtrees, using a heuristic simulation policy, reducing the domain of variables by eliminating dominated solutions and using a beam width.
Revised:  These enhancements aim to efficiently explore the search space tree by pruning subtrees, using a heuristic simulation policy, reducing the domain of variables by eliminating dominated value assignments and using a beam width.
Edit intent: clarity
Original:  They are demonstrated for a specific combinatorial optimization problem :
Revised:  They are demonstrated for two specific combinatorial optimization problem :
Edit intent: clarity
Original:  They are demonstrated for a specific combinatorial optimization problem :
Revised:  They are demonstrated for a specific combinatorial optimization problems :
Edit intent: fluency
Original:  the results also show evidence that the algorithm is able to learn to correct the incorrect choices of a standard heuristic, yielding an average improvement of 10.0 \% with respect to the objective function value of the solution .
Revised:  the results also show evidence that the algorithm is able to learn to correct the incorrect choices made by constructive heuristics .
Edit intent: clarity
Original: The node classification problem is to infer unknown node labels in a graph given its structure and node attributes along with labels for some of the nodes . Approaches for this task typically assume that adjacent nodes have similar attributes and thus, that a node's label can be predicted from the labels of its neighbors .
Revised: The task of node classification is to infer unknown node labels in a graph given its structure and node attributes along with labels for some of the nodes . Approaches for this task typically assume that adjacent nodes have similar attributes and thus, that a node's label can be predicted from the labels of its neighbors .
Edit intent: clarity
Original: The node classification problem is to infer unknown node labels in a graph given its structure and node attributes along with labels for some of the nodes . Approaches for this task typically assume that adjacent nodes have similar attributes and thus, that a node's label can be predicted from the labels of its neighbors .
Revised: The node classification problem is to infer unknown node labels , given the labels for some of the nodes . Approaches for this task typically assume that adjacent nodes have similar attributes and thus, that a node's label can be predicted from the labels of its neighbors .
Edit intent: clarity
Original: The node classification problem is to infer unknown node labels in a graph given its structure and node attributes along with labels for some of the nodes . Approaches for this task typically assume that adjacent nodes have similar attributes and thus, that a node's label can be predicted from the labels of its neighbors .
Revised: The node classification problem is to infer unknown node labels in a graph given its structure and node attributes along with labels for some of the nodes . Approaches for this task typically assume that adjacent nodes have similar attributes and a node's label can be predicted from the labels of its neighbors .
Edit intent: coherence