wanyu/IteraTeR_v2 huggingface dataset:
Original:  and performance modeling . Based on these approaches, we extrapolate potential directions for parallelism in deep learning.
Revised:  and performance modeling . Based on those approaches, we extrapolate potential directions for parallelism in deep learning.
Edit intent: fluency
Original: We propose a theoretical model to measures the fundamental values of cryptocurrency and blockchain technology.
Revised: We propose a theoretical model to measure the fundamental values of cryptocurrency and blockchain technology.
Edit intent: fluency
Original: We propose the rank-regret representative as a way of choosing a small subset of the database guaranteed to contain {\em   at least one of the top-k of any linear ranking function.
Revised: We propose the rank-regret representative as a way of choosing a small subset of the database guaranteed to contain {\em at least one of the top-k of any possible ranking function.
Edit intent: clarity
Original:  The network is trained during testing by minimizing reconstruction loss between observed and synthesized images.
Revised:  The network weights are optimized during testing by minimizing reconstruction loss between observed and synthesized images.
Edit intent: clarity
Original: We propose a metric - called Multi-Scale Relevance (MSR) - to score neurons for their prominence in encoding for the animal's behaviour that is being observed in a multi-electrode array recording experiment.
Revised: We propose a metric -- which we call multiscale relevance (MSR) - to score neurons for their prominence in encoding for the animal's behaviour that is being observed in a multi-electrode array recording experiment.
Edit intent: coherence
Original:  We formulate two results: the first one shows that our modules/wirings definition is complete ;
Revised:  We formulate two results: the first one shows that our modules/wirings definition is complete ,
Edit intent: fluency
Original:  In this work, we attempt to solve the initial CV problem using a data-driven approach inspired by  supervised machine learning literature .
Revised:  In this work, we solve the initial CV problem using a data-driven approach inspired by supervised machine learning literature .
Edit intent: clarity
Original:  In this work, we attempt to solve the initial CV problem using a data-driven approach inspired by  supervised machine learning literature .
Revised:  In this work, we attempt to solve the initial CV problem using a data-driven approach inspired by the filed of supervised machine learning literature .
Edit intent: clarity
Original:  In this work, we attempt to solve the initial CV problem using a data-driven approach inspired by  supervised machine learning literature .
Revised:  In this work, we attempt to solve the initial CV problem using a data-driven approach inspired by supervised machine learning .
Edit intent: clarity
Original:  Previous algebraic studies of transducers have focused on the  structure of the underlying input automaton, disregarding the output.
Revised:  Previous algebraic studies of transducers have focused on the sole structure of the underlying input automaton, disregarding the output.
Edit intent: clarity
Original:  We propose a comparison of the two algebraic approaches through two questions: When are the automaton structure and the continuity properties related, and when does continuity propagate to superclasses?
Revised:  We propose a comparison of the two algebraic approaches . We focus on two questions: When are the automaton structure and the continuity properties related, and when does continuity propagate to superclasses?
Edit intent: clarity
Original: A word-to-word function is continuous for a class of languages  V if its inverse maps V -languages to V.
Revised: A word-to-word function is continuous for a class of languages ~ V if its inverse maps V -languages to V.
Edit intent: fluency
Original: A word-to-word function is continuous for a class of languages  V if its inverse maps V -languages to V.
Revised: A word-to-word function is continuous for a class of languages V if its inverse maps V _languages to~ V.
Edit intent: fluency
Original:  Our case studies demonstrate that operator splitting is surprisingly general, and our results show that it increases the performance of important generated tensor kernels to match hand-optimized code .
Revised:  Our case studies demonstrate that operator splitting is surprisingly general, and our results show that it increases the performance of these kernels on par with state-of-the-art hand-optimized code .
Edit intent: clarity
Original:  For further motivation, we additionally present experimental results for the onset of sliding of an elastomer cap on a smooth glass plate  under low normal loads.
Revised:  For further motivation, we additionally present experimental results for the onset of sliding of a smooth glass plate under low normal loads.
Edit intent: clarity
Original:  Additionally, these systems typically employ static algorithms that ignore the end result ( they do not  " learn from their mistakes" ) . In this paper, we argue that existing deep reinforcement learning techniques can be applied to query planning .
Revised:  Additionally, these systems typically employ static algorithms that ignore the end result ( they do not have a mechanism for " learn from their mistakes" ) . In this paper, we argue that existing deep reinforcement learning techniques can be applied to query planning .
Edit intent: clarity
Original:  Additionally, these systems typically employ static algorithms that ignore the end result ( they do not  " learn from their mistakes" ) . In this paper, we argue that existing deep reinforcement learning techniques can be applied to query planning .
Revised:  Additionally, these systems typically employ static algorithms that ignore the end result ( they do not " learning from their mistakes" ) . In this paper, we argue that existing deep reinforcement learning techniques can be applied to query planning .
Edit intent: fluency
Original:  Additionally, these systems typically employ static algorithms that ignore the end result ( they do not  " learn from their mistakes" ) . In this paper, we argue that existing deep reinforcement learning techniques can be applied to query planning .
Revised:  Additionally, these systems typically employ static algorithms that ignore the end result ( they do not " learn from their mistakes" . In this paper, we argue that existing deep reinforcement learning techniques can be applied to query planning .
Edit intent: fluency
Original:  Additionally, these systems typically employ static algorithms that ignore the end result ( they do not  " learn from their mistakes" ) . In this paper, we argue that existing deep reinforcement learning techniques can be applied to query planning .
Revised:  Additionally, these systems typically employ static algorithms that ignore the end result ( they do not " learn from their mistakes" ) . In this paper, we argue that existing deep reinforcement learning techniques can be applied to address this challenge .
Edit intent: clarity
Original:  However, theoretical results for deep learning in inverse problems are missing so far .
Revised:  However, theoretical results for deep learning in inverse problems are mostly missing .
Edit intent: clarity
Original:  However,  theoretical results for deep learning in inverse problems are mostly missing .
Revised:  However, theoretical results for deep learning in inverse problems .
Edit intent: clarity
Original:  In this paper, we establish such a convergence analysis for the proposed NETT (Network Tikhonov) approach to inverse problems.
Revised:  In this paper, we establish a complete convergence analysis for the proposed NETT (Network Tikhonov) approach to inverse problems.
Edit intent: clarity
Original:  NETT considers regularized solutions having small value of a regularizer defined by a trained neural network.
Revised:  NETT considers data consistent solutions having small value of a regularizer defined by a trained neural network.
Edit intent: clarity
Original:   Numerical results are presented for a tomographic sparse data problem using the l^q-norm of auto-encoder as trained regularizer , which demonstrate good performance of NETT even for unknowns of different type from the training data  .
Revised:  Numerical results are presented for a tomographic sparse data problem , which demonstrate good performance of NETT even for unknowns of different type from the training data .
Edit intent: clarity
Original:  A social network analysis of the faculty hires can reveal patterns that have an impact on the dissemination of educational innovations within a profession .
Revised:  A social network analysis of faculty hires can reveal patterns that have an impact on the dissemination of educational innovations within a profession .
Edit intent: fluency
Original:  We first statistically test for the presence of a linear hierarchy in the network and for its steepness.
Revised:  We first statistically test for the existence of a linear hierarchy in the network and for its steepness.
Edit intent: fluency
Original:  We proceed to find a near linear hierarchical order of the departments,  which we contrast with other indicators of hierarchy, including published rankings.
Revised:  We find a near linear hierarchical order of the departments, which we contrast with other indicators of hierarchy, including published rankings.
Edit intent: coherence
Original:  A single index is not able to capture the  structure of a complex network,  so we next fit a latent variable exponential random graph model for the faculty hires , which is able to reproduce the main observed network characteristics: high incidence of self-hiring, skewed out-degree edge distribution, low density (except at the top of the hierarchy) and clustering.
Revised:  A single index is not capable to capture the structure of a complex network, so we next fit a latent variable exponential random graph model for the faculty hires , which is able to reproduce the main observed network characteristics: high incidence of self-hiring, skewed out-degree edge distribution, low density (except at the top of the hierarchy) and clustering.
Edit intent: clarity
Original:  A single index is not able to capture the  structure of a complex network,  so we next fit a latent variable exponential random graph model for the faculty hires , which is able to reproduce the main observed network characteristics: high incidence of self-hiring, skewed out-degree edge distribution, low density (except at the top of the hierarchy) and clustering.
Revised:  A single index is not able to capture the full structure of a complex network, so we next fit a latent variable exponential random graph model for the faculty hires , which is able to reproduce the main observed network characteristics: high incidence of self-hiring, skewed out-degree edge distribution, low density (except at the top of the hierarchy) and clustering.
Edit intent: clarity
Original:  A single index is not able to capture the  structure of a complex network,  so we next fit a latent variable exponential random graph model for the faculty hires , which is able to reproduce the main observed network characteristics: high incidence of self-hiring, skewed out-degree edge distribution, low density (except at the top of the hierarchy) and clustering.
Revised:  A single index is not able to capture the structure of a complex network, however, so we next fit a latent variable exponential random graph model for the faculty hires , which is able to reproduce the main observed network characteristics: high incidence of self-hiring, skewed out-degree edge distribution, low density (except at the top of the hierarchy) and clustering.
Edit intent: coherence
Original:  A single index is not able to capture the  structure of a complex network,  so we next fit a latent variable exponential random graph model for the faculty hires , which is able to reproduce the main observed network characteristics: high incidence of self-hiring, skewed out-degree edge distribution, low density (except at the top of the hierarchy) and clustering.
Revised:  A single index is not able to capture the structure of a complex network, so we next fit a latent exponential random graph model for the faculty hires , which is able to reproduce the main observed network characteristics: high incidence of self-hiring, skewed out-degree edge distribution, low density (except at the top of the hierarchy) and clustering.
Edit intent: clarity
Original:  A single index is not able to capture the  structure of a complex network,  so we next fit a latent variable exponential random graph model for the faculty hires , which is able to reproduce the main observed network characteristics: high incidence of self-hiring, skewed out-degree edge distribution, low density (except at the top of the hierarchy) and clustering.
Revised:  A single index is not able to capture the structure of a complex network, so we next fit a latent variable exponential random graph model for the faculty hires , which is able to reproduce its main observed characteristics: high incidence of self-hiring, skewed out-degree edge distribution, low density (except at the top of the hierarchy) and clustering.
Edit intent: clarity
Original:  A single index is not able to capture the  structure of a complex network,  so we next fit a latent variable exponential random graph model for the faculty hires , which is able to reproduce the main observed network characteristics: high incidence of self-hiring, skewed out-degree edge distribution, low density (except at the top of the hierarchy) and clustering.
Revised:  A single index is not able to capture the structure of a complex network, so we next fit a latent variable exponential random graph model for the faculty hires , which is able to reproduce the main observed network characteristics: high incidence of self-hiring, skewed out-degree distribution, low density (except at the top of the hierarchy) and clustering.
Edit intent: clarity
Original:  A single index is not able to capture the  structure of a complex network,  so we next fit a latent variable exponential random graph model for the faculty hires , which is able to reproduce the main observed network characteristics: high incidence of self-hiring, skewed out-degree edge distribution, low density (except at the top of the hierarchy) and clustering.
Revised:  A single index is not able to capture the structure of a complex network, so we next fit a latent variable exponential random graph model for the faculty hires , which is able to reproduce the main observed network characteristics: high incidence of self-hiring, skewed out-degree edge distribution, low density and clustering.
Edit intent: clarity
Original:  We discuss the implications of these findings for the flow of education and teaching ideas within the ORIE discipline and compare our findings with those reported for other related disciplines, Computer Science and Business.
Revised:  We contrast our findings with those reported for other related disciplines, Computer Science and Business.
Edit intent: clarity
Original:  It could compress data with acceptable information loss , and could be of great usefulness in areas like Image Processing, Pattern Recognition  and Machine Learning.
Revised:  It could compress data with acceptable information loss and could be of great usefulness in areas like Image Processing, Pattern Recognition and Machine Learning.
Edit intent: fluency
Original:  It could compress data with acceptable information loss , and could be of great usefulness in areas like Image Processing, Pattern Recognition  and Machine Learning.
Revised:  It could compress data with acceptable information loss , and could be of great usefulness in areas like Image Processing, Pattern Recognition , and Machine Learning.
Edit intent: fluency
Original:  In this paper, we explored vector quantization problem  from a new perspective of sparse least square optimization and designed multiple algorithms with their program implementations . Specifically, deriving from a sparse form of coefficient matrix, three types of sparse least squares, with l_0, l_1 , and generalized l_1 + l_2 penalizations, are designed and implemented respectively .
Revised:  In this paper, we explored vector quantization problem from a new perspective , namely sparse least square optimization and designed multiple algorithms with their program implementations . Specifically, deriving from a sparse form of coefficient matrix, three types of sparse least squares, with l_0, l_1 , and generalized l_1 + l_2 penalizations, are designed and implemented respectively .
Edit intent: coherence
Original:  In this paper, we explored vector quantization problem  from a new perspective of sparse least square optimization and designed multiple algorithms with their program implementations . Specifically, deriving from a sparse form of coefficient matrix, three types of sparse least squares, with l_0, l_1 , and generalized l_1 + l_2 penalizations, are designed and implemented respectively .
Revised:  In this paper, we explored vector quantization problem from a new perspective of sparse least square optimization . Specifically, deriving from a sparse form of coefficient matrix, three types of sparse least squares, with l_0, l_1 , and generalized l_1 + l_2 penalizations, are designed and implemented respectively .
Edit intent: coherence
Original:  The paper offers a new perspective to probe the area of vector quantization, while the algorithms proposed could provide more appropriate options for quantization tasks under different circumstances .
Revised:  The paper offers a new perspective to probe the area of vector quantization, and the algorithms proposed could provide more appropriate options for quantization tasks under different circumstances .
Edit intent: coherence
Original: Vector quantization aims to form new vectors/matrices with shared values close to the original.
Revised: Quantization can be used to form new vectors/matrices with shared values close to the original.
Edit intent: clarity
Original:  Specifically, inspired by the property of sparsity of Lasso, a novel quantization algorithm based on l_1 least square is proposedand implemented.
Revised:  Specifically, inspired by the property of sparse least square regression, several quantization algorithms based on l_1 least square is proposedand implemented.
Edit intent: clarity
Original:  Similar schemes with l_1 + l_2 combination penalization and l_0 regularization are simultaneously proposed.
Revised:  Similar schemes with l_1 + l_2 and l_0 regularization are simultaneously proposed.
Edit intent: clarity
Original:  In addition, to produce quantization results with  given amount of quantized values(instead of penalization coefficient \lambda) , this paper proposed an iterative sparse least square method and a cluster-based least squarequantization method.
Revised:  In addition, to produce quantization results with a given amount of quantized values(instead of penalization coefficient \lambda) , this paper proposed an iterative sparse least square method and a cluster-based least squarequantization method.
Edit intent: fluency
Original:  In addition, to produce quantization results with  given amount of quantized values(instead of penalization coefficient \lambda) , this paper proposed an iterative sparse least square method and a cluster-based least squarequantization method.
Revised:  In addition, to produce quantization results with given amount of values/clusters , this paper proposed an iterative sparse least square method and a cluster-based least squarequantization method.
Edit intent: clarity
Original:  In addition, to produce quantization results with  given amount of quantized values(instead of penalization coefficient \lambda) , this paper proposed an iterative sparse least square method and a cluster-based least squarequantization method.
Revised:  In addition, to produce quantization results with given amount of quantized values(instead of penalization coefficient \lambda) , this paper designed an iterative method and a cluster-based least squarequantization method.
Edit intent: clarity
Original:  The algorithms proposed were tested under three scenarios of data and their computational performance , including information loss, time consumption  and the distribution of the value of sparse vectors were compared and analyzed.
Revised:  The algorithms proposed were tested with three types of data and their computational performance , including information loss, time consumption and the distribution of the value of sparse vectors were compared and analyzed.
Edit intent: clarity
Original:  The algorithms proposed were tested under three scenarios of data and their computational performance , including information loss, time consumption  and the distribution of the value of sparse vectors were compared and analyzed.
Revised:  The algorithms proposed were tested under three scenarios of data and their computational performances , including information loss, time consumption and the distribution of the value of sparse vectors were compared and analyzed.
Edit intent: fluency
Original:  The algorithms proposed were tested under three scenarios of data and their computational performance , including information loss, time consumption  and the distribution of the value of sparse vectors were compared and analyzed.
Revised:  The algorithms proposed were tested under three scenarios of data and their computational performance , including information loss, time consumption , and the distribution of the value of sparse vectors were compared and analyzed.
Edit intent: fluency
Original:  The algorithms proposed were tested under three scenarios of data and their computational performance , including information loss, time consumption  and the distribution of the value of sparse vectors were compared and analyzed.
Revised:  The algorithms proposed were tested under three scenarios of data and their computational performance , including information loss, time consumption and the distribution of the values of the sparse vectors, were compared and analyzed.
Edit intent: fluency
Original:  The paper offers a new perspective to probe the area of vector quantization, and the algorithms proposed could offer better performance especially when the required post-quantization value amounts are not on a tiny scale .
Revised:  The paper offers a new perspective to probe the area of quantization, and the algorithms proposed could offer better performance especially when the required post-quantization value amounts are not on a tiny scale .
Edit intent: clarity
Original:  The paper offers a new perspective to probe the area of vector quantization, and the algorithms proposed could offer better performance especially when the required post-quantization value amounts are not on a tiny scale .
Revised:  The paper offers a new perspective to probe the area of vector quantization, and the algorithms proposed could offer better performance especially when the required post-quantization resolution (number of values) is not significantly lower than that of the original vector .
Edit intent: clarity
Original:  In recent years, the popularity of scalar quantization for value-sharing application has been soaring as it has been found huge utilities in reducing the complexity of neural networks.
Revised:  In recent years, the popularity of scalar quantization for value-sharing applications has been soaring as it has been found huge utilities in reducing the complexity of neural networks.
Edit intent: fluency
Original:  Existing clustering-based quantization techniques, while being well-developed, have multiple drawbacks including the dependency of the random seed, empty or out-of-the-range clusters, and high time complexity for  large number of clusters.
Revised:  Existing clustering-based quantization techniques, while being well-developed, have multiple drawbacks including the dependency of the random seed, empty or out-of-the-range clusters, and high time complexity for a large number of clusters.
Edit intent: fluency