wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this paper , we comprehensively investigate NVIDIA's Tensor Core implementation found in Volta and Turing architectures and propose an architectural model for it.
Revised:  In this paper , we comprehensively investigate NVIDIA's Tensor Core implementation found in Volta and Turing architectures . We further propose an architectural model for it.
Edit intent: clarity
Original:  Building upon this we also \\%DIF > correlation versus an NVIDIA Titan~V GPU in terms of average  enable GPGPU-Sim to run NVIDIA's CUTLASS, an open-source CUDA C++ templates library providing customizable GEMM templates including the support for Tensor Cores .
Revised:  Building upon this we also \\%DIF > correlation versus an NVIDIA Titan~V GPU in terms of average enable GPGPU-Sim to run CUTLASS, an open-source CUDA C++ templates library providing customizable GEMM templates including the support for Tensor Cores .
Edit intent: clarity
Original:  Building upon this we also \\%DIF > correlation versus an NVIDIA Titan~V GPU in terms of average  enable GPGPU-Sim to run NVIDIA's CUTLASS, an open-source CUDA C++ templates library providing customizable GEMM templates including the support for Tensor Cores .
Revised:  Building upon this we also \\%DIF > correlation versus an NVIDIA Titan~V GPU in terms of average enable GPGPU-Sim to run NVIDIA's CUTLASS, an open-source CUDA C++ template library providing customizable GEMM templates including the support for Tensor Cores .
Edit intent: fluency
Original:  Building upon this we also \\%DIF > correlation versus an NVIDIA Titan~V GPU in terms of average  enable GPGPU-Sim to run NVIDIA's CUTLASS, an open-source CUDA C++ templates library providing customizable GEMM templates including the support for Tensor Cores .
Revised:  Building upon this we also \\%DIF > correlation versus an NVIDIA Titan~V GPU in terms of average enable GPGPU-Sim to run NVIDIA's CUTLASS, an open-source CUDA C++ templates library providing customizable GEMM templates that utilize tensor cores .
Edit intent: clarity
Original:  In this paper, we address these weaknesses by proposing a privacy-preserving architecture for device-centric and attribute-based authentication based on: (a ) the seamless integration between usable/strong device-centric authentication methods and federated login solutions;
Revised:  In this paper, we address these weaknesses by proposing a privacy-preserving architecture for device-centric and attribute-based authentication based on: 1 ) the seamless integration between usable/strong device-centric authentication methods and federated login solutions;
Edit intent: fluency
Original:  (b ) the separation of the concerns for Authorization, Authentication, Behavioral Authentication and Identification to facilitate incremental deployability, wide adoption and compliance with NIST assurance levels;
Revised:  2 ) the separation of the concerns for Authorization, Authentication, Behavioral Authentication and Identification to facilitate incremental deployability, wide adoption and compliance with NIST assurance levels;
Edit intent: coherence
Original: Recent studies on multi-label image classification have been focusing on designing more complex architectures of deep neural networks such as the use of attention mechanism and region proposal networks.
Revised: Recent studies on multi-label image classification have focused on designing more complex architectures of deep neural networks such as the use of attention mechanism and region proposal networks.
Edit intent: coherence
Original: Recent studies on multi-label image classification have been focusing on designing more complex architectures of deep neural networks such as the use of attention mechanism and region proposal networks.
Revised: Recent studies on multi-label image classification have been focusing on designing more complex architectures of deep neural networks such as the use of attention mechanisms and region proposal networks.
Edit intent: fluency
Original:  Although performance gains have been reported in literature , the backbone deep models of the proposed approaches and the evaluation metrics employed in different works vary, making it difficult to compare with each other fairly.
Revised:  Although performance gains have been reported , the backbone deep models of the proposed approaches and the evaluation metrics employed in different works vary, making it difficult to compare with each other fairly.
Edit intent: clarity
Original:  Although performance gains have been reported in literature , the backbone deep models of the proposed approaches and the evaluation metrics employed in different works vary, making it difficult to compare with each other fairly.
Revised:  Although performance gains have been reported in literature , the backbone deep models of the proposed approaches and the evaluation metrics employed in different works vary, making it difficult to compare each fairly.
Edit intent: clarity
Original:  Moreover, due to the lack of properly investigated baselines, the advantage introduced by the proposed techniques in literature are vague .
Revised:  Moreover, due to the lack of properly investigated baselines, the advantage introduced by the proposed techniques are often ambiguous .
Edit intent: clarity
Original:  With only data augmentation and model ensemble, we achieve better performance than those previously reported on three benchmark datasets .
Revised:  With the use of proper data augmentation techniques and model ensembles, the basic deep architectures can achieve better performance than those previously reported on three benchmark datasets .
Edit intent: clarity
Original: A growing number of learning methods are actually games which optimise multiple, interdependent objectives in parallel -- from GANs and intrinsic curiosity to multi-agent RL.
Revised: A growing number of learning methods are actually which optimise multiple, interdependent objectives in parallel -- from GANs and intrinsic curiosity to multi-agent RL.
Edit intent: clarity
Original:  Opponent shaping is a powerful approach to improve learning dynamics in such games, accounting for the fact that the ' environment' includes agents adapting to one another's updates.
Revised:  Opponent shaping is a powerful approach to improve learning dynamics in these games, accounting for the fact that the ' environment' includes agents adapting to one another's updates.
Edit intent: fluency
Original:  Opponent shaping is a powerful approach to improve learning dynamics in such games, accounting for the fact that the ' environment' includes agents adapting to one another's updates.
Revised:  Opponent shaping is a powerful approach to improve learning dynamics in such games, accounting for player influence on others' updates.
Edit intent: clarity
Original:  Learning with Opponent-Learning Awareness (LOLA) is a recent algorithm which exploits this dynamic response and encourages cooperation in settings like the Iterated Prisoner's Dilemma.
Revised:  Learning with Opponent-Learning Awareness (LOLA) is a recent algorithm that exploits this response and leads to cooperation in settings like the Iterated Prisoner's Dilemma.
Edit intent: clarity
Original:  Although experimentally successful, we show that LOLA  can exhibit 'arrogant' behaviour directly at odds with convergence.
Revised:  Although experimentally successful, we show that LOLA agents can exhibit 'arrogant' behaviour directly at odds with convergence.
Edit intent: clarity
Original:  We prove that LookAhead locally converges and avoids strict saddles in all differentiable games , the strongest results in the field so far . SOS inherits these desirable guarantees, while also shaping the learning of opponents and consistently either matching or outperforming LOLA experimentally.
Revised:  We prove that LookAhead locally converges and avoids strict saddles in , the strongest results in the field so far . SOS inherits these desirable guarantees, while also shaping the learning of opponents and consistently either matching or outperforming LOLA experimentally.
Edit intent: clarity
Original:  We prove that LookAhead locally converges and avoids strict saddles in all differentiable games , the strongest results in the field so far . SOS inherits these desirable guarantees, while also shaping the learning of opponents and consistently either matching or outperforming LOLA experimentally.
Revised:  We prove that LookAhead locally converges and avoids strict saddles in all differentiable games all differentiable games . SOS inherits these desirable guarantees, while also shaping the learning of opponents and consistently either matching or outperforming LOLA experimentally.
Edit intent: clarity
Original:  We prove that LookAhead locally converges and avoids strict saddles in all differentiable games , the strongest results in the field so far . SOS inherits these desirable guarantees, while also shaping the learning of opponents and consistently either matching or outperforming LOLA experimentally.
Revised:  We prove that LookAhead locally converges and avoids strict saddles in all differentiable games , the strongest results in the field so far . SOS inherits these essential guarantees, while also shaping the learning of opponents and consistently either matching or outperforming LOLA experimentally.
Edit intent: clarity
Original:  Although constructive, those tools are not output-efficient, since the time needed to produce the extended formulation also depends on the size of the slack matrix (hence, of the exact description in the original space).
Revised:  Although constructive, those tools are not output-efficient, since the time needed to produce the extended formulation also depends on the number of rows of the slack matrix (hence, of the exact description in the original space).
Edit intent: clarity
Original:  Although constructive, those tools are not output-efficient, since the time needed to produce the extended formulation also depends on the size of the slack matrix (hence, of the exact description in the original space).
Revised:  Although constructive, those tools are not output-efficient, since the time needed to produce the extended formulation also depends on the size of the slack matrix (hence, on the exact description in the original space).
Edit intent: clarity
Original:  For specific classes of polytopes, we give also a direct, efficient construction of those extended formulations .
Revised:  For specific classes of polytopes, we give also a direct, efficient construction of extended formulations arising from protocols .
Edit intent: clarity
Original:  Our first result is to show that a linear number of rounds of SoS applied over the \emph{configuration linear program yields an integrality gap of at least 1.0009.
Revised:  Our first result is to show that a linear number of rounds of SoS applied over the \emph{configuration linear program yields an integrality gap of at least 1.0009.
Edit intent: fluency
Original:  Then, we consider the weaker assignment linear program and add a well chosen set of symmetry breaking inequalities that removes a subset of the machine permutation symmetries.
Revised:  Then, we consider the weaker assignment linear program and add a well chosen set of symmetry breaking inequalities that removes a subset of the machine permutation symmetries.
Edit intent: fluency
Original: The Sum of Squares ( SoS ) hierarchy gives an automatized technique to create a family of increasingly tight convex relaxations for binary programs.
Revised: The Sum of Squares ( ) hierarchy gives an automatized technique to create a family of increasingly tight convex relaxations for binary programs.
Edit intent: clarity
Original:  There are several problems for which a constant number of rounds of the hierarchy give integrality gaps matching the best known approximation algorithm.
Revised:  There are several problems for which a constant number of rounds of this hierarchy give integrality gaps matching the best known approximation algorithm.
Edit intent: clarity
Original:  In many other , however, ad-hoc techniques give significantly better approximation ratios .
Revised:  In many other , however, ad-hoc techniques give better approximation ratios .
Edit intent: clarity
Original:  Notably, the lower bounds instances  , in many cases , are invariant under the action of a large permutation group.
Revised:  Notably, the lower bounds instances , in many cases these instances are invariant under the action of a large permutation group.
Edit intent: clarity
Original:  The main purpose of this paper is to study how the presence of symmetries on a formulation degrades the performance of the relaxation obtained by the SoS hierarchy.
Revised:  This yields the question how symmetries in a formulation degrade the performance of the relaxation obtained by the SoS hierarchy.
Edit intent: clarity
Original:  Our first result is to show that a linear number of rounds of SoS\sos{} applied over the configuration linear program \emph{ yields an integrality gap of at least 1.0009 .
Revised:  Our first result is to show that a linear number of rounds of SoS\sos{} applied over the \emph{ yields an integrality gap of at least 1.0009 .
Edit intent: clarity
Original:  Then, we consider the weaker assignment linear program \emph{ and add a well chosen set of symmetry breaking inequalities that removes a subset of the machine permutation symmetries.
Revised:  Then, we consider the weaker \emph{ and add a well chosen set of symmetry breaking inequalities that removes a subset of the machine permutation symmetries.
Edit intent: clarity
Original:  We show that applying the SoS hierarchy for O_\varepsilon(1) rounds to this(1/\varepsilon^2)} rounds of the SA hierarchy to this stronger } linear program reduces the integrality gap to ( 1+\varepsilon ) . Our results suggest that for this classical problem the symmetries of the natural assignment linear program were the main barrier preventing the SoS hierarchy \sos{} to give relaxations with integrality gap ( 1+\varepsilon ) after a constant number of rounds .
Revised:  We show that applying the SoS hierarchy for O_\varepsilon(1) rounds to this(1/\varepsilon^2)} rounds of the SA hierarchy to this stronger } linear program reduces the integrality gap to 1+\varepsilon ) . Our results suggest that for this classical problem the symmetries of the natural assignment linear program were the main barrier preventing the SoS hierarchy \sos{} to give relaxations with integrality gap ( 1+\varepsilon ) after a constant number of rounds .
Edit intent: fluency
Original:  We show that applying the SoS hierarchy for O_\varepsilon(1) rounds to this(1/\varepsilon^2)} rounds of the SA hierarchy to this stronger } linear program reduces the integrality gap to ( 1+\varepsilon ) . Our results suggest that for this classical problem the symmetries of the natural assignment linear program were the main barrier preventing the SoS hierarchy \sos{} to give relaxations with integrality gap ( 1+\varepsilon ) after a constant number of rounds .
Revised:  We show that applying the SoS hierarchy for O_\varepsilon(1) rounds to this(1/\varepsilon^2)} rounds of the SA hierarchy to this stronger } linear program reduces the integrality gap to ( 1+\varepsilon ) . Our results suggest that for this classical problem , symmetries were the main barrier preventing the SoS hierarchy \sos{} to give relaxations with integrality gap ( 1+\varepsilon ) after a constant number of rounds .
Edit intent: clarity
Original:  We show that applying the SoS hierarchy for O_\varepsilon(1) rounds to this(1/\varepsilon^2)} rounds of the SA hierarchy to this stronger } linear program reduces the integrality gap to ( 1+\varepsilon ) . Our results suggest that for this classical problem the symmetries of the natural assignment linear program were the main barrier preventing the SoS hierarchy \sos{} to give relaxations with integrality gap ( 1+\varepsilon ) after a constant number of rounds .
Revised:  We show that applying the SoS hierarchy for O_\varepsilon(1) rounds to this(1/\varepsilon^2)} rounds of the SA hierarchy to this stronger } linear program reduces the integrality gap to ( 1+\varepsilon ) . Our results suggest that for this classical problem the symmetries of the natural assignment linear program were the main barrier preventing the \sos{} to give relaxations with integrality gap ( 1+\varepsilon ) after a constant number of rounds .
Edit intent: fluency
Original:  We show that applying the SoS hierarchy for O_\varepsilon(1) rounds to this(1/\varepsilon^2)} rounds of the SA hierarchy to this stronger } linear program reduces the integrality gap to ( 1+\varepsilon ) . Our results suggest that for this classical problem the symmetries of the natural assignment linear program were the main barrier preventing the SoS hierarchy \sos{} to give relaxations with integrality gap ( 1+\varepsilon ) after a constant number of rounds .
Revised:  We show that applying the SoS hierarchy for O_\varepsilon(1) rounds to this(1/\varepsilon^2)} rounds of the SA hierarchy to this stronger } linear program reduces the integrality gap to ( 1+\varepsilon ) . Our results suggest that for this classical problem the symmetries of the natural assignment linear program were the main barrier preventing the SoS hierarchy \sos{}/ SA hierarchies to give relaxations with integrality gap ( 1+\varepsilon ) after a constant number of rounds .
Edit intent: clarity
Original:  We show that applying the SoS hierarchy for O_\varepsilon(1) rounds to this(1/\varepsilon^2)} rounds of the SA hierarchy to this stronger } linear program reduces the integrality gap to ( 1+\varepsilon ) . Our results suggest that for this classical problem the symmetries of the natural assignment linear program were the main barrier preventing the SoS hierarchy \sos{} to give relaxations with integrality gap ( 1+\varepsilon ) after a constant number of rounds .
Revised:  We show that applying the SoS hierarchy for O_\varepsilon(1) rounds to this(1/\varepsilon^2)} rounds of the SA hierarchy to this stronger } linear program reduces the integrality gap to ( 1+\varepsilon ) . Our results suggest that for this classical problem the symmetries of the natural assignment linear program were the main barrier preventing the SoS hierarchy \sos{} to give relaxations with integrality gap ( 1+\varepsilon .
Edit intent: clarity
Original:  The implementation only took 2 people 1 month to complete.
Edit intent: coherence
Original:  DiracDeltaNet, as the name suggests, is a ConvNet with only 1x1\times1 convolutions while spatial convolutions are replaced by more efficient shift operations.
Revised:  DiracDeltaNet, as the name suggests, is a ConvNet with only 1\times1 convolutions while spatial convolutions are replaced by more efficient shift operations.
Edit intent: clarity
Original:  In short, DiracDeltaNet's small model size, low computational OP count, ultra-low precision and simplified operators allow us to co-design a highly customized computing unit for an FPGA.
Revised:  In short, DiracDeltaNet's small model size, low computational OP count, low precision and simplified operators allow us to co-design a highly customized computing unit for an FPGA.
Edit intent: clarity
Original:  In addition, the accelerator reaches an inference speed of 96.5 FPS on the ImageNet classification task, surpassing prior works with similar accuracy by at least 16.9 x .
Revised:  In addition, the accelerator reaches an inference speed of 96.5 FPS on the ImageNet classification task, surpassing prior works with similar accuracy by at least 16.9 \times .
Edit intent: clarity
Original:  DiracDeltaNet achieves competitive accuracy on ImageNet (88.7 \% top-5), but with 42\times fewer parameters and 48\times fewer \\%DIF > top-5), but with 42\times fewer parameters and 48\times  OPs than VGG16.
Revised:  DiracDeltaNet achieves competitive accuracy on ImageNet (88.7 \\%DIF > top-5), but with 42\times fewer parameters and 48\times OPs than VGG16.
Edit intent: clarity
Original:  DiracDeltaNet achieves competitive accuracy on ImageNet (88.7 \% top-5), but with 42\times fewer parameters and 48\times fewer \\%DIF > top-5), but with 42\times fewer parameters and 48\times  OPs than VGG16.
Revised:  DiracDeltaNet achieves competitive accuracy on ImageNet (88.7 \% top-5), but with 42\times fewer parameters and 48\times fewer \\%DIF > top-5), but with 42\times fewer parameters and 48\times fewer OPs than VGG16.
Edit intent: fluency
Original:  Our accelerator's final top-5 accuracy of 88.1 \% on \\%DIF > on ImageNet, is higher than all the previously reported embedded FPGA accelerators.
Revised:  Our accelerator's final top-5 accuracy of 88.1 \\%DIF > on ImageNet, is higher than all the previously reported embedded FPGA accelerators.
Edit intent: fluency
Original:  There are rules generalized for when the process favors the mutants, where many of the rules indicate that clustering plays a heavy role in the expansion of mutants.
Revised:  There are rules generalized for when the process favors the mutants, where many of the expansion of mutants.
Edit intent: clarity
Original:  Nevertheless, as mutants are separated, the more dispersed they are, the greater  chance of invasion is.
Revised:  Nevertheless, as mutants are separated, the more dispersed mutants are, the greater chance of invasion is.
Edit intent: clarity
Original:  Nevertheless, as mutants are separated, the more dispersed they are, the greater  chance of invasion is.
Revised:  Nevertheless, as mutants are separated, the more dispersed they are, the greater the chance of invasion is.
Edit intent: fluency
Original: Predictive modelling of functional connectomes from structural connectomes explores commonalities across multimodal imaging data.
Revised: In this study we adopt predictive modelling to identify simultaneously commonalities and differences in multi-modal brain networks acquired within subjects. Typically, predictive modelling of functional connectomes from structural connectomes explores commonalities across multimodal imaging data.
Edit intent: coherence
Original:   The class internally uses two main approaches to achieve efficient execution: (i) a hybrid storage framework, which automatically and seamlessly switches between three underlying storage formats (compressed sparse column, Red-Black tree, coordinate list) depending on which format is best suited and/or available for specific operations, and (ii)  template-based meta-programming  to automatically detect and optimise execution of common expression patterns.
Revised:  The class internally uses two main approaches to achieve efficient execution: (i) a hybrid storage framework, which automatically and seamlessly switches between three underlying storage formats (compressed sparse column, Red-Black tree, coordinate list) depending on which format is best suited and/or available for specific operations, and (ii) a template-based meta-programming to automatically detect and optimise execution of common expression patterns.
Edit intent: fluency
Original:   The class internally uses two main approaches to achieve efficient execution: (i) a hybrid storage framework, which automatically and seamlessly switches between three underlying storage formats (compressed sparse column, Red-Black tree, coordinate list) depending on which format is best suited and/or available for specific operations, and (ii)  template-based meta-programming  to automatically detect and optimise execution of common expression patterns.
Revised:  The class internally uses two main approaches to achieve efficient execution: (i) a hybrid storage framework, which automatically and seamlessly switches between three underlying storage formats (compressed sparse column, Red-Black tree, coordinate list) depending on which format is best suited and/or available for specific operations, and (ii) template-based meta-programming framework to automatically detect and optimise execution of common expression patterns.
Edit intent: fluency