wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our contributions include (1) a language close to the mathematics of deep learning called Tensor Comprehensions offering both imperative and declarative styles , (2) a polyhedral Just-In-Time compiler to convert a mathematical description of a deep learning DAG into a CUDA kernel with delegated memory management and synchronization, also providing optimizations such as operator fusion and specialization for specific sizes, (3) a compilation cache populated by an autotuner.
Revised:  Our contributions include (1) a language close to the mathematics of deep learning called Tensor Comprehensions , (2) a polyhedral Just-In-Time compiler to convert a mathematical description of a deep learning DAG into a CUDA kernel with delegated memory management and synchronization, also providing optimizations such as operator fusion and specialization for specific sizes, (3) a compilation cache populated by an autotuner.
Edit intent: clarity
Original:  However, these frameworks are optimized for a narrow range of server-class GPUs and deploying workloads to other platforms such as mobile phones, embedded devices, and specialized accelerators (e.g., FPGAs, ASICs) requires laborious manual effort.
Revised:  However, these frameworks are optimized for a narrow range of server-class GPUs . Deploying workloads to new platforms such as mobile phones, embedded devices, and specialized accelerators (e.g., FPGAs, ASICs) requires laborious manual effort.
Edit intent: clarity
Original:  However, these frameworks are optimized for a narrow range of server-class GPUs and deploying workloads to other platforms such as mobile phones, embedded devices, and specialized accelerators (e.g., FPGAs, ASICs) requires laborious manual effort.
Revised:  However, these frameworks are optimized for a narrow range of server-class GPUs and deploying workloads to other platforms such as mobile phones, embedded devices, and accelerators (e.g., FPGAs, ASICs) requires laborious manual effort.
Edit intent: clarity
Original:  However, these frameworks are optimized for a narrow range of server-class GPUs and deploying workloads to other platforms such as mobile phones, embedded devices, and specialized accelerators (e.g., FPGAs, ASICs) requires laborious manual effort.
Revised:  However, these frameworks are optimized for a narrow range of server-class GPUs and deploying workloads to other platforms such as mobile phones, embedded devices, and specialized accelerators (e.g., FPGAs, ASICs) requires significant manual effort.
Edit intent: clarity
Original:  We discuss the optimization challenges specific to deep learning that TVM solves: high-level operator fusion, low-level memory reuse across threads, mapping to arbitrary hardware primitives, and memory latency hiding.
Revised:  TVM solves optimization challenges specific to deep learning that TVM solves: high-level operator fusion, low-level memory reuse across threads, mapping to arbitrary hardware primitives, and memory latency hiding.
Edit intent: clarity
Original:  We discuss the optimization challenges specific to deep learning that TVM solves: high-level operator fusion, low-level memory reuse across threads, mapping to arbitrary hardware primitives, and memory latency hiding.
Revised:  We discuss the optimization challenges specific to deep learning such as high-level operator fusion, low-level memory reuse across threads, mapping to arbitrary hardware primitives, and memory latency hiding.
Edit intent: clarity
Original:  We discuss the optimization challenges specific to deep learning that TVM solves: high-level operator fusion, low-level memory reuse across threads, mapping to arbitrary hardware primitives, and memory latency hiding.
Revised:  We discuss the optimization challenges specific to deep learning that TVM solves: high-level operator fusion, mapping to arbitrary hardware primitives, and memory latency hiding.
Edit intent: clarity
Original:  We also demonstrate TVM's ability to target new hardware accelerator back-ends by targeting an FPGA-based generic deep learning accelerator.
Revised:  We also demonstrate TVM's ability to target new accelerator back-ends by targeting an FPGA-based generic deep learning accelerator.
Edit intent: clarity
Original:  Deploying workloads to new platforms  such as mobile phones, embedded devices, and accelerators (e.g., FPGAs, ASICs)  requires significant manual effort.
Revised:  Deploying workloads to new platforms -- such as mobile phones, embedded devices, and accelerators (e.g., FPGAs, ASICs) requires significant manual effort.
Edit intent: fluency
Original:  Deploying workloads to new platforms  such as mobile phones, embedded devices, and accelerators (e.g., FPGAs, ASICs)  requires significant manual effort.
Revised:  Deploying workloads to new platforms such as mobile phones, embedded devices, and accelerators (e.g., FPGAs, ASICs) -- requires significant manual effort.
Edit intent: fluency
Original:  TVM solves optimization challenges specific to deep learning  such as high-level operator fusion, mapping to arbitrary hardware primitives, and memory latency hiding.
Revised:  TVM solves optimization challenges specific to deep learning , such as high-level operator fusion, mapping to arbitrary hardware primitives, and memory latency hiding.
Edit intent: fluency
Original:  TVM also offers automated optimization of low-level programs to hardware characteristics by employing a novel  learning-based cost modeling method for rapid exploration of code optimizations.
Revised:  It also automates optimization of low-level programs to hardware characteristics by employing a novel learning-based cost modeling method for rapid exploration of code optimizations.
Edit intent: clarity
Original:  TVM also offers automated optimization of low-level programs to hardware characteristics by employing a novel  learning-based cost modeling method for rapid exploration of code optimizations.
Revised:  TVM also offers automated optimization of low-level programs to hardware characteristics by employing a novel , learning-based cost modeling method for rapid exploration of code optimizations.
Edit intent: fluency
Original:  Experimental results demonstrate that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art  hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs.
Revised:  Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs.
Edit intent: clarity
Original:  Experimental results demonstrate that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art  hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs.
Revised:  Experimental results demonstrate that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art , hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs.
Edit intent: fluency
Original:  We also demonstrate TVM's ability to target new accelerator back-ends by targeting an FPGA-based generic deep learning accelerator.
Revised:  We also demonstrate TVM's ability to target new accelerator back-ends , such as the FPGA-based generic deep learning accelerator.
Edit intent: clarity
Original: Exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services.
Revised: An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services.
Edit intent: fluency
Original:  Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access network .
Revised:  Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks .
Edit intent: fluency
Original:  In this work , we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE.
Revised:  In this review paper , we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE.
Edit intent: clarity
Original: The paper is concerned with an in-body system gathering data for telemedical purposes.
Revised: The paper is concerned with an in-body system gathering data for medical purposes.
Edit intent: clarity
Original:  Foerster Resonance Energy Transfer (FRET) is considered as a mechanism for communication between the system components.
Revised:  Foerster Resonance Energy Transfer (FRET) considered as a mechanism for communication between the system components.
Edit intent: coherence
Original: The past few years have witnessed growth in the size and computational requirements for training deep convolutional neural networks.
Revised: The past few years have witnessed growth in the computational requirements for training deep convolutional neural networks.
Edit intent: clarity
Original:  Current approaches parallelize the training process onto multiple devices by applying a single parallelization strategy (e.g., data or model parallelism) to all layers in a network.
Revised:  Current approaches parallelize training onto multiple devices by applying a single parallelization strategy (e.g., data or model parallelism) to all layers in a network.
Edit intent: clarity
Original:  Although easy to reason about, this design results in suboptimal runtime performance in large-scale distributed training, since different layers in a network may prefer different parallelization strategies.
Revised:  Although easy to reason about, these approaches result in suboptimal runtime performance in large-scale distributed training, since different layers in a network may prefer different parallelization strategies.
Edit intent: clarity
Original:  Our experiments show that layer-wise parallelism outperforms current parallelization approaches by increasing training speed , reducing communication costs, achieving better scalability to multiple GPUs, while maintaining the same network accuracy.
Revised:  Our evaluation shows that layer-wise parallelism outperforms current parallelization approaches by increasing training speed , reducing communication costs, achieving better scalability to multiple GPUs, while maintaining the same network accuracy.
Edit intent: clarity
Original:  Our experiments show that layer-wise parallelism outperforms current parallelization approaches by increasing training speed , reducing communication costs, achieving better scalability to multiple GPUs, while maintaining the same network accuracy.
Revised:  Our experiments show that layer-wise parallelism outperforms state-of-the-art approaches by increasing training speed , reducing communication costs, achieving better scalability to multiple GPUs, while maintaining the same network accuracy.
Edit intent: clarity
Original:  Our experiments show that layer-wise parallelism outperforms current parallelization approaches by increasing training speed , reducing communication costs, achieving better scalability to multiple GPUs, while maintaining the same network accuracy.
Revised:  Our experiments show that layer-wise parallelism outperforms current parallelization approaches by increasing training throughput , reducing communication costs, achieving better scalability to multiple GPUs, while maintaining the same network accuracy.
Edit intent: clarity
Original:  Our experiments show that layer-wise parallelism outperforms current parallelization approaches by increasing training speed , reducing communication costs, achieving better scalability to multiple GPUs, while maintaining the same network accuracy.
Revised:  Our experiments show that layer-wise parallelism outperforms current parallelization approaches by increasing training speed , reducing communication costs, achieving better scalability to multiple GPUs, while maintaining original network accuracy.
Edit intent: clarity
Original: In this paper, we develop a new postprocessing technique for Raviart--Thomas mixed elements on irregular triangular grids by using the idea of local least-squares fittings.
Revised: In this paper, we develop a new postprocessing technique for Raviart--Thomas mixed elements on triangular grids by using the idea of local least-squares fittings.
Edit intent: clarity
Original:  Combining the boundedness and supercloseness results, we prove that the postprocessed solution superconverges to the exact solution in the L ^{2 -norm for elliptic equations as well as 2D Maxwell's equations on quasi-uniform triangular meshes, where most pairs of adjacent triangles form approximate parallelograms .
Revised:  Combining the boundedness and supercloseness results, we prove that the postprocessed solution superconverges to the exact solution in the L ^2 -norm for elliptic equations as well as 2D Maxwell's equations on quasi-uniform triangular meshes, where most pairs of adjacent triangles form approximate parallelograms .
Edit intent: fluency
Original:  Combining the boundedness and supercloseness results, we prove that the postprocessed solution superconverges to the exact solution in the L ^{2 -norm for elliptic equations as well as 2D Maxwell's equations on quasi-uniform triangular meshes, where most pairs of adjacent triangles form approximate parallelograms .
Revised:  Combining the boundedness and supercloseness results, we prove that the postprocessed solution superconverges to the exact solution in the L ^{2 -norm on mildly structured meshes .
Edit intent: clarity
Original: For the second lowest order Raviart--Thomas mixed method, we prove that the canonical interpolant and finite element solution for the vector variable in elliptic problems are superclose in the H(div)-norm on mildly structured meshes, where most pairs of adjacent triangles form approximate parallelograms.
Revised: For the second lowest-order Raviart--Thomas mixed method, we prove that the canonical interpolant and finite element solution for the vector variable in elliptic problems are superclose in the H(div)-norm on mildly structured meshes, where most pairs of adjacent triangles form approximate parallelograms.
Edit intent: fluency
Original:  Scientists from History and Philosophy stand out in direct knowledge transfer.
Revised:  Scientists from History stand out in direct knowledge transfer.
Edit intent: clarity
Original:  Conversely , we find that in heavy-tailed networks, global cascade probability is reduced by the presence of highly connected blocking nodes .
Revised:  Conversely , we find that the presence of highly connected blocking nodes .
Edit intent: clarity
Original: The score function estimator is widely used for estimating gradients of stochastic objectives in Stochastic Computation Graphs (SCG), eg . in reinforcement learning and meta-learning.
Revised: The score function estimator is widely used for estimating gradients of stochastic objectives in stochastic computation graphs (SCG), eg . in reinforcement learning and meta-learning.
Edit intent: fluency
Original: The score function estimator is widely used for estimating gradients of stochastic objectives in Stochastic Computation Graphs (SCG), eg . in reinforcement learning and meta-learning.
Revised: The score function estimator is widely used for estimating gradients of stochastic objectives in Stochastic Computation Graphs (SCG), eg , in reinforcement learning and meta-learning.
Edit intent: fluency
Original:  While deriving the first-order gradient estimators by differentiating a surrogate loss (SL) objective is computationally and conceptually simple, using the same approach for higher-order gradients is more challenging.
Revised:  While deriving the first-order gradient estimators by differentiating a surrogate loss (SL) objective is computationally and conceptually simple, using the same approach for higher-order derivatives is more challenging.
Edit intent: clarity
Original:  Secondly, repeatedly applying SL to construct new objectives for each order gradient involves increasingly cumbersome graph manipulations.
Revised:  Secondly, repeatedly applying SL to construct new objectives for each order derivative involves increasingly cumbersome graph manipulations.
Edit intent: clarity
Original:  Lastly, to match the first-order gradient under differentiation, SL treats part of the cost as a fixed sample, which we show leads to missing and wrong terms for  higher-order gradient estimators .
Revised:  Lastly, to match the first-order gradient under differentiation, SL treats part of the cost as a fixed sample, which we show leads to missing and wrong terms for estimators of higher-order gradient estimators .
Edit intent: clarity
Original:  Lastly, to match the first-order gradient under differentiation, SL treats part of the cost as a fixed sample, which we show leads to missing and wrong terms for  higher-order gradient estimators .
Revised:  Lastly, to match the first-order gradient under differentiation, SL treats part of the cost as a fixed sample, which we show leads to missing and wrong terms for higher-order derivatives .
Edit intent: clarity
Original:  We verify the correctness of DiCE both through a proof and through numerical evaluation of the DiCE gradient estimates.
Revised:  We verify the correctness of DiCE both through a proof and numerical evaluation of the DiCE gradient estimates.
Edit intent: fluency
Original: Online algorithms are known model that is investigated with respect to a competitive ratiotypically.
Revised: We consider online algorithms with respect to a competitive ratiotypically.
Edit intent: clarity
Original:  Additionally , we show that  quantum online algorithm with a constant number of qubits can be better than  deterministic online algorithm even if deterministic one has a constant number of advice bits and unlimited computational power.
Revised:  Furthermore , we show that quantum online algorithm with a constant number of qubits can be better than deterministic online algorithm even if deterministic one has a constant number of advice bits and unlimited computational power.
Edit intent: fluency
Original:  Additionally , we show that  quantum online algorithm with a constant number of qubits can be better than  deterministic online algorithm even if deterministic one has a constant number of advice bits and unlimited computational power.
Revised:  Additionally , we show that a quantum online algorithm with a constant number of qubits can be better than deterministic online algorithm even if deterministic one has a constant number of advice bits and unlimited computational power.
Edit intent: fluency
Original:  Additionally , we show that  quantum online algorithm with a constant number of qubits can be better than  deterministic online algorithm even if deterministic one has a constant number of advice bits and unlimited computational power.
Revised:  Additionally , we show that quantum online algorithm with a constant number of qubits can be better than any deterministic online algorithm even if deterministic one has a constant number of advice bits and unlimited computational power.
Edit intent: clarity
Original:  Additionally , we show that  quantum online algorithm with a constant number of qubits can be better than  deterministic online algorithm even if deterministic one has a constant number of advice bits and unlimited computational power.
Revised:  Additionally , we show that quantum online algorithm with a constant number of qubits can be better than deterministic online algorithm with a constant number of advice bits and unlimited computational power.
Edit intent: clarity
Original: Over the past few years  new "fringe" communities have been created and gained traction on the Web at a rapid rate.
Revised: Over the past few years , a number of new "fringe" communities have been created and gained traction on the Web at a rapid rate.
Edit intent: clarity
Original:  Very often little is known about how they evolve and what kinds of activities they attract, despite recent research has shown that they do influence how misinformation reaches mainstream communities.
Revised:  Very often little is known about how they evolve or what kind of activities they attract, despite recent research has shown that they do influence how misinformation reaches mainstream communities.
Edit intent: fluency
Original:  Very often little is known about how they evolve and what kinds of activities they attract, despite recent research has shown that they do influence how misinformation reaches mainstream communities.
Revised:  Very often little is known about how they evolve and what kinds of activities they attract, despite recent research has shown that they influence how false information reaches mainstream communities.
Edit intent: clarity
Original:  This motivates the need to monitor them and analyze their impact on the Web's information ecosystem.
Revised:  This motivates the need to monitor these communities and analyze their impact on the Web's information ecosystem.
Edit intent: clarity
Original:  It positions itself as putting "people and free speech first  ", welcoming users banned or suspended from other social networks.
Revised:  It positions itself as putting "people and free speech first ' ", welcoming users banned or suspended from other social networks.
Edit intent: fluency
Original:  We also measure the prevalence of hate speech on the platform, finding it to be much higher than on Twitter, but lower than 4chan's Politically Incorrect board.
Revised:  We also measure the prevalence of hate speech on the platform, finding it to be much higher than Twitter, but lower than 4chan's Politically Incorrect board.
Edit intent: fluency
Original: We present a deep recurrent neural network model with soft visual attention that learns to generate LaTeX markup of  real-world math formulas given their images.
Revised: We present a deep recurrent neural network model with soft visual attention that learns to generate LaTeX markup of a real-world math formulas given their images.
Edit intent: fluency
Original:  Applying neural sequence generation techniques that have been very successful in the fields of machine translation and image/handwriting/speech captioning, recognition, transcription and synthesis, we construct an image-to-markup model that learns to produce syntactically and semantically correct LaTeX markup code of over 150 words long and achieves a BLEU score of 89\%;
Revised:  Applying neural sequence generation techniques that have been very successful across modalities such as natural language, image, handwriting, speech and audio; we construct an image-to-markup model that learns to produce syntactically and semantically correct LaTeX markup code of over 150 words long and achieves a BLEU score of 89\%;
Edit intent: clarity
Original:  Applying neural sequence generation techniques that have been very successful in the fields of machine translation and image/handwriting/speech captioning, recognition, transcription and synthesis, we construct an image-to-markup model that learns to produce syntactically and semantically correct LaTeX markup code of over 150 words long and achieves a BLEU score of 89\%;
Revised:  Applying neural sequence generation techniques that have been very successful in the fields of machine translation and image/handwriting/speech captioning, recognition, transcription and synthesis, we construct an image-to-markup model that learns to produce syntactically and semantically correct LaTeX markup code over 150 words long and achieves a BLEU score of 89\%;
Edit intent: fluency
Original:  the best reported so far for the Im2Latex problem.
Revised:  improving upon the previous state-of-art for the Im2Latex problem.
Edit intent: clarity