wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This paper appraises approaches to address this problem, highlighting a common underlying structure  and challenges of aggregation at relevant geographical scales , and investigates improvements on the literature state of the art.
Revised:  This paper appraises approaches to address this problem, highlighting a common underlying structure , conditions for lossless aggregation, and challenges of aggregation at relevant geographical scales , and investigates improvements on the literature state of the art.
Edit intent: meaning-changed
Original:  This paper appraises approaches to address this problem, highlighting a common underlying structure  and challenges of aggregation at relevant geographical scales , and investigates improvements on the literature state of the art. We also demonstrate a novel decomposition scheme to avoid temporal aggregation for applications where longer runtimes are permissible .
Revised:  This paper appraises approaches to address this problem, highlighting a common underlying structure and challenges of aggregation at relevant geographical scales . We then investigate solutions to the modeling problem including a decomposition scheme to avoid temporal aggregation for applications where longer runtimes are permissible .
Edit intent: clarity
Original: Deep Neural Networks (DNNs) have gained significant interest in the recent past for plethora of applications such as imageand videoanalytics, language translation, and medical diagnosis. High memory bandwidth is required to keep up with the needs of data-intensive DNN applications when implemented on a von-Neumann hardware architecture as majority of the data resides in the main memory.
Revised: Deep Neural Networks (DNNs) have transformed the field of machine learning and are widely deployed in many applications involving image, video, speech and natural language processing. The increasing compute demands of DNNs have been widely addressed through Graphics Processing Units (GPUs) and specialized accelerators. However, as model sizes grow, these von Neumann architectures require very high memory bandwidth to keep the processing elements utilized as a majority of the data resides in the main memory.
Edit intent: clarity
Original:  In this work, we propose a  DRAM-based processing-in-memory (PIM) multiplication primitive coupled with intra-bank accumulation to accelerate matrix vector operations in ML workloads.
Revised:  In this work, we propose a new DRAM-based processing-in-memory (PIM) multiplication primitive coupled with intra-bank accumulation to accelerate matrix vector operations in ML workloads.
Edit intent: clarity
Original:  Moreover, we propose a processing-in-memory DRAM bank architecture, data mapping and dataflow based on the proposed primitive .
Revised:  The proposed multiplication primitive adds < 1\% area overhead and does not require any change in the DRAM peripherals. Therefore, the proposed multiplication can be easily adopted in commodity DRAM chips. Subsequently, we design a DRAM-based PIM architecture, data mapping and dataflow based on the proposed primitive .
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Original:  System evaluations performed on networks like AlexNet, VGG16 and ResNet18 show that the proposed architecture, mapping, and data flow can provide up to 23x and 6.5x benefits over a GPU and an ideal conventional (non-PIM) baseline architecture with infinite compute bandwidth, respectively .
Revised:  System evaluations performed on networks like AlexNet, VGG16 and ResNet18 show that the proposed architecture, mapping, and data flow can provide up to 23x speedup over an NVIDIA Titan Xp GPU. Furthermore, it achieves upto 6.5x benefits over a GPU and an ideal conventional (non-PIM) baseline architecture with infinite compute bandwidth, respectively .
Edit intent: meaning-changed
Original:  System evaluations performed on networks like AlexNet, VGG16 and ResNet18 show that the proposed architecture, mapping, and data flow can provide up to 23x and 6.5x benefits over a GPU and an ideal conventional (non-PIM) baseline architecture with infinite compute bandwidth, respectively .
Revised:  System evaluations performed on networks like AlexNet, VGG16 and ResNet18 show that the proposed architecture, mapping, and data flow can provide up to 23x and 6.5x speedup over an ideal von Neumann architecture with infinite compute bandwidth, respectively .
Edit intent: meaning-changed
Original:  System evaluations performed on networks like AlexNet, VGG16 and ResNet18 show that the proposed architecture, mapping, and data flow can provide up to 23x and 6.5x benefits over a GPU and an ideal conventional (non-PIM) baseline architecture with infinite compute bandwidth, respectively .
Revised:  System evaluations performed on networks like AlexNet, VGG16 and ResNet18 show that the proposed architecture, mapping, and data flow can provide up to 23x and 6.5x benefits over a GPU and an ideal conventional (non-PIM) baseline architecture with infinite computational throughput, highlighting the need to overcome the memory bottleneck in future generations of DNN hardware .
Edit intent: meaning-changed
Original: We study the classical problem of moment estimation of an underlying vector whose n coordinates are implicitly defined through a series of updates in a data stream. We show that if the updates to the vector arrive in the random-order insertion-only model , then there exist space efficient algorithms with improved dependencies on the approximation parameter \varepsilon .
Revised: In the adversarially robust streaming model, a stream of elements is presented to an algorithm and is allowed to depend on the output of the algorithm at earlier times during the stream. In the classic insertion-only model , then there exist space efficient algorithms with improved dependencies on the approximation parameter \varepsilon .
Edit intent: meaning-changed
Original:  We show that if the updates to the vector arrive in the random-order insertion-only model , then there exist space efficient algorithms with improved dependencies on the approximation parameter \varepsilon . In particular, for any real p > 2, we first obtain an algorithm for F_p moment estimation using%DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\varepsilon^{4/p}}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% bits of memory.
Revised:  We show that if the updates to the vector arrive in the random-order insertion-only model %DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\varepsilon^{4/p}}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% bits of memory.
Edit intent: coherence
Original:  In particular, for any real p > 2, we first obtain an algorithm for F_p moment estimation using%DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\varepsilon^{4/p}}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% bits of memory.
Revised:  In particular, for any real p > 2, we first obtain an algorithm for F_p moment estimation using%DIFDELCMD < \mathcal{O}\left(%%% {\varepsilon^{4/p}}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% bits of memory.
Edit intent: coherence
Original:  In particular, for any real p > 2, we first obtain an algorithm for F_p moment estimation using%DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\varepsilon^{4/p}}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% bits of memory. Our techniques also give algorithms for F_p moment estimationwith p>2 on arbitrary order insertion-only and turnstile streams, using%DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\varepsilon^{4/p}}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% bits of space and two passes, which is the first optimal multi-pass F_p estimation algorithm up to \log n factors.
Revised:  In particular, for any real p > 2, we first obtain an algorithm for F_p moment estimation using%DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\varepsilon^{4/p}}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% %DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\varepsilon^{4/p}}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% bits of space and two passes, which is the first optimal multi-pass F_p estimation algorithm up to \log n factors.
Edit intent: meaning-changed
Original:  Our techniques also give algorithms for F_p moment estimationwith p>2 on arbitrary order insertion-only and turnstile streams, using%DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\varepsilon^{4/p}}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% bits of space and two passes, which is the first optimal multi-pass F_p estimation algorithm up to \log n factors.
Revised:  Our techniques also give algorithms for F_p moment estimationwith p>2 on arbitrary order insertion-only and turnstile streams, using%DIFDELCMD < \mathcal{O}\left(%%% {\varepsilon^{4/p}}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% bits of space and two passes, which is the first optimal multi-pass F_p estimation algorithm up to \log n factors.
Edit intent: clarity
Original:  Our techniques also give algorithms for F_p moment estimationwith p>2 on arbitrary order insertion-only and turnstile streams, using%DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\varepsilon^{4/p}}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% bits of space and two passes, which is the first optimal multi-pass F_p estimation algorithm up to \log n factors.
Revised:  Our techniques also give algorithms for F_p moment estimationwith p>2 on arbitrary order insertion-only and turnstile streams, using%DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\varepsilon^{4/p}}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% of data streams, Ben-Eliezer et. al. (PODS 2020, best paper award) show how to convert a non-robust algorithm into a robust one with a roughly 1/\varepsilon factor overhead. This was subsequently improved to a 1/\sqrt{\varepsilon the first optimal multi-pass F_p estimation algorithm up to \log n factors.
Edit intent: meaning-changed
Original:  Our techniques also give algorithms for F_p moment estimationwith p>2 on arbitrary order insertion-only and turnstile streams, using%DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\varepsilon^{4/p}}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% bits of space and two passes, which is the first optimal multi-pass F_p estimation algorithm up to \log n factors. Finally, we give an improved lower bound of \Omega%DIFDELCMD < \left(%%% \frac{1{\varepsilon^2}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% for one-pass insertion-only streams.
Revised:  Our techniques also give algorithms for F_p moment estimationwith p>2 on arbitrary order insertion-only and turnstile streams, using%DIFDELCMD < \mathcal{O}\left(%%% \frac{1{\varepsilon^{4/p}}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% bits of space and two passes, which is the first optimal %DIFDELCMD < \left(%%% \frac{1{\varepsilon^2}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% for one-pass insertion-only streams.
Edit intent: meaning-changed
Original:  Finally, we give an improved lower bound of \Omega%DIFDELCMD < \left(%%% \frac{1{\varepsilon^2}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% for one-pass insertion-only streams.
Revised:  Finally, we give an improved lower bound of \Omega%DIFDELCMD < \left(%%% {\varepsilon^2}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% for one-pass insertion-only streams.
Edit intent: meaning-changed
Original:  Finally, we give an improved lower bound of \Omega%DIFDELCMD < \left(%%% \frac{1{\varepsilon^2}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% for one-pass insertion-only streams. Our results separate the complexity of this problem both between random and non-random orders, as well as one-pass and multi-pass streams.
Revised:  Finally, we give an improved lower bound of \Omega%DIFDELCMD < \left(%%% \frac{1{\varepsilon^2}\cdot n^{1-2/p}}%DIFDELCMD < \right) %%% dependence on the approximation factor. As in previous work, we obtain a general transformation that applies to any non-robust streaming algorithm and depends on the so-called flip number. However, the key technical innovation is that we apply the transformation to what we call a difference estimator for the streaming problem, rather than an estimator for the streaming problem itself. We then develop the first difference estimators for a wide range of problems. Our difference estimator methodology is not only applicable to the adversarially robust model, but to other streaming models where temporal properties of the data play a central role. (Abstract shortened to meet arXiv limit.)
Edit intent: meaning-changed
Original: In the adversarially robust streaming model, a stream of elements is presented to an algorithm and is allowed to depend on the output of the algorithm at earlier times during the stream. In the classic insertion-only model of data streams, Ben-Eliezer et. al. (PODS 2020, best paper award) show how to convert a non-robust algorithm into a robust one with a roughly 1/%DIFDELCMD < \varepsilonfactor %%% overhead.
Revised: We study the classical problem of moment estimation of an underlying vector whose n coordinates are implicitly defined through a series of updates in a data stream. We show that if the updates to the vector arrive in the random-order insertion-only model of data streams, Ben-Eliezer et. al. (PODS 2020, best paper award) show how to convert a non-robust algorithm into a robust one with a roughly 1/%DIFDELCMD < \varepsilonfactor %%% overhead.
Edit intent: meaning-changed
Original:  In the classic insertion-only model of data streams, Ben-Eliezer et. al. (PODS 2020, best paper award) show how to convert a non-robust algorithm into a robust one with a roughly 1/%DIFDELCMD < \varepsilonfactor %%% overhead.
Revised:  In the classic insertion-only model %DIFDELCMD < \varepsilonfactor %%% overhead.
Edit intent: clarity
Original:  In the classic insertion-only model of data streams, Ben-Eliezer et. al. (PODS 2020, best paper award) show how to convert a non-robust algorithm into a robust one with a roughly 1/%DIFDELCMD < \varepsilonfactor %%% overhead. This was subsequently improved to a 1/\sqrt{\varepsilon\mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right) \mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right)  the first optimal dependence on the approximation factor.
Revised:  In the classic insertion-only model of data streams, Ben-Eliezer et. al. (PODS 2020, best paper award) show how to convert a non-robust algorithm into a robust one with a roughly 1/%DIFDELCMD < \varepsilonfactor %%% , then there exist space efficient algorithms with improved dependencies on the approximation parameter \varepsilon. In particular, for any real p > 2, we first obtain an algorithm for F_p moment estimation using\mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right) \mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right) the first optimal dependence on the approximation factor.
Edit intent: meaning-changed
Original:  This was subsequently improved to a 1/\sqrt{\varepsilon\mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right) \mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right)  the first optimal dependence on the approximation factor.
Revised:  This was subsequently improved to a 1/\sqrt{\varepsilon\mathcal{O}\left(\frac{1{\varepsilon^{4/p}}\cdot n^{1-2/p}}\right) \mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right) the first optimal dependence on the approximation factor.
Edit intent: meaning-changed
Original:  This was subsequently improved to a 1/\sqrt{\varepsilon\mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right) \mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right)  the first optimal dependence on the approximation factor.
Revised:  This was subsequently improved to a 1/\sqrt{\varepsilon\mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right) bits of memory. Our techniques also give algorithms for F_p moment estimation with p>2 on arbitrary order insertion-only and turnstile streams, using\mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right) the first optimal dependence on the approximation factor.
Edit intent: meaning-changed
Original:  This was subsequently improved to a 1/\sqrt{\varepsilon\mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right) \mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right)  the first optimal dependence on the approximation factor.
Revised:  This was subsequently improved to a 1/\sqrt{\varepsilon\mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right) \mathcal{O}\left(\frac{1{\varepsilon^{4/p}}\cdot n^{1-2/p}}\right) the first optimal dependence on the approximation factor.
Edit intent: meaning-changed
Original:  This was subsequently improved to a 1/\sqrt{\varepsilon\mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right) \mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right)  the first optimal dependence on the approximation factor.
Revised:  This was subsequently improved to a 1/\sqrt{\varepsilon\mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right) \mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right) bits of space and two passes, which is the first optimal dependence on the approximation factor.
Edit intent: meaning-changed
Original:  This was subsequently improved to a 1/\sqrt{\varepsilon\mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right) \mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right)  the first optimal dependence on the approximation factor. As in previous work, we obtain a general transformation that applies to any non-robust streaming algorithm and depends on the so-called flip number. However, the key technical innovation is that we apply the transformation to what we call a difference estimator for the streaming problem, rather than an estimator for the streaming problem itself. We then develop the first difference estimators for a wide range of problems. Our difference estimator methodology is not only applicable to the adversarially robust model, but to other streaming models where temporal properties of the data play a central role.(Abstract shortened to meet arXiv limit.)\left({\varepsilon^2}\cdot n^{1-2/p}}\right) 
Revised:  This was subsequently improved to a 1/\sqrt{\varepsilon\mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right) \mathcal{O}\left({\varepsilon^{4/p}}\cdot n^{1-2/p}}\right) the first optimal multi-pass F_p estimation algorithm up to \log n factors. Finally, we give an improved lower bound of \Omega\left({\varepsilon^2}\cdot n^{1-2/p}}\right) 
Edit intent: meaning-changed
Original: )\left({\varepsilon^2}\cdot n^{1-2/p}}\right) 
Revised: )\left({\varepsilon^2}\cdot n^{1-2/p}}\right) for one-pass insertion-only streams. Our results separate the complexity of this problem both between random and non-random orders, as well as one-pass and multi-pass streams.
Edit intent: meaning-changed
Original:  Perhaps most surprisingly, we find that replacing the self-attention sublayer in a Transformer encoder with a standard, unparameterized Fourier Transform achieves 92 \% of the accuracy of BERT  on the GLUE benchmark, but pre-trains and runs up to seven times faster on GPUs and twice as fast on TPUs.
Revised:  Most surprisingly, we find that replacing the self-attention sublayer in a Transformer encoder with a standard, unparameterized Fourier Transform achieves 92 \% of the accuracy of BERT on the GLUE benchmark, but pre-trains and runs up to seven times faster on GPUs and twice as fast on TPUs.
Edit intent: coherence
Original:  Perhaps most surprisingly, we find that replacing the self-attention sublayer in a Transformer encoder with a standard, unparameterized Fourier Transform achieves 92 \% of the accuracy of BERT  on the GLUE benchmark, but pre-trains and runs up to seven times faster on GPUs and twice as fast on TPUs.
Revised:  Perhaps most surprisingly, we find that replacing the self-attention sublayer in a Transformer encoder with a standard, unparameterized Fourier Transform achieves 92-97 \% of the accuracy of BERT on the GLUE benchmark, but pre-trains and runs up to seven times faster on GPUs and twice as fast on TPUs.
Edit intent: meaning-changed
Original:  Perhaps most surprisingly, we find that replacing the self-attention sublayer in a Transformer encoder with a standard, unparameterized Fourier Transform achieves 92 \% of the accuracy of BERT  on the GLUE benchmark, but pre-trains and runs up to seven times faster on GPUs and twice as fast on TPUs.
Revised:  Perhaps most surprisingly, we find that replacing the self-attention sublayer in a Transformer encoder with a standard, unparameterized Fourier Transform achieves 92 \% of the accuracy of BERT counterparts on the GLUE benchmark, but pre-trains and runs up to seven times faster on GPUs and twice as fast on TPUs.
Edit intent: meaning-changed
Original:  Perhaps most surprisingly, we find that replacing the self-attention sublayer in a Transformer encoder with a standard, unparameterized Fourier Transform achieves 92 \% of the accuracy of BERT  on the GLUE benchmark, but pre-trains and runs up to seven times faster on GPUs and twice as fast on TPUs.
Revised:  Perhaps most surprisingly, we find that replacing the self-attention sublayer in a Transformer encoder with a standard, unparameterized Fourier Transform achieves 92 \% of the accuracy of BERT on the GLUE benchmark, but trains nearly seven times faster on GPUs and twice as fast on TPUs.
Edit intent: clarity
Original:  The resulting model, which we name FNet, scales very efficiently to long inputs , matching the accuracy of the most accurate "efficient" Transformers on the Long Range Arena benchmark, but training and running faster across all sequence lengths on GPUs and relatively shorter sequence lengths on TPUs  . Finally, FNet has a light memory footprint and is particularly efficient at smaller model sizes: for a fixed speed and accuracy budget, small FNet models outperform Transformer counterparts.
Revised:  The resulting model, FNet, also scales very efficiently to long inputs , matching the accuracy of the most accurate "efficient" Transformers on the Long Range Arena benchmark, but training and running faster across all sequence lengths on GPUs and relatively shorter sequence lengths on TPUs . Finally, FNet has a light memory footprint and is particularly efficient at smaller model sizes: for a fixed speed and accuracy budget, small FNet models outperform Transformer counterparts.
Edit intent: clarity
Original:  The resulting model, which we name FNet, scales very efficiently to long inputs , matching the accuracy of the most accurate "efficient" Transformers on the Long Range Arena benchmark, but training and running faster across all sequence lengths on GPUs and relatively shorter sequence lengths on TPUs  . Finally, FNet has a light memory footprint and is particularly efficient at smaller model sizes: for a fixed speed and accuracy budget, small FNet models outperform Transformer counterparts.
Revised:  The resulting model, which we name FNet, scales very efficiently to long inputs . Specifically, when compared to the "efficient" Transformers on the Long Range Arena benchmark, but training and running faster across all sequence lengths on GPUs and relatively shorter sequence lengths on TPUs . Finally, FNet has a light memory footprint and is particularly efficient at smaller model sizes: for a fixed speed and accuracy budget, small FNet models outperform Transformer counterparts.
Edit intent: clarity
Original:  The resulting model, which we name FNet, scales very efficiently to long inputs , matching the accuracy of the most accurate "efficient" Transformers on the Long Range Arena benchmark, but training and running faster across all sequence lengths on GPUs and relatively shorter sequence lengths on TPUs  . Finally, FNet has a light memory footprint and is particularly efficient at smaller model sizes: for a fixed speed and accuracy budget, small FNet models outperform Transformer counterparts.
Revised:  The resulting model, which we name FNet, scales very efficiently to long inputs , matching the accuracy of the most accurate "efficient" Transformers on the Long Range Arena benchmark, FNet matches the accuracy of the most accurate models, but is faster than the fastest models across all sequence lengths on GPUs and relatively shorter sequence lengths on TPUs . Finally, FNet has a light memory footprint and is particularly efficient at smaller model sizes: for a fixed speed and accuracy budget, small FNet models outperform Transformer counterparts.
Edit intent: clarity