wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Specifically, for any c%DIFDELCMD < \ge %%% 1, \epsilon > 0, we propose a single-pass , deterministic streaming algorithm with ratio 1/(4c)-\epsilon, query complexity \lceil n / c \rceil + c , memory complexity O(k \log k), and O(n) total running time. As k%DIFDELCMD < \to %%% \infty, the ratio converges to (1 - 1/e)/(c + 1).
Revised:  Specifically, for any c%DIFDELCMD < \ge %%% 1, \epsilon > 0, we propose a single-pass , deterministic streaming algorithm with ratio 1/(4c)-\epsilon, query complexity \lceil n / c \rceil + c %DIFDELCMD < \to %%% \infty, the ratio converges to (1 - 1/e)/(c + 1).
Edit intent: coherence
Original: \ge  In addition, we propose a deterministic, multi-pass streaming algorithm with O(1 / \epsilon) passes that achieves ratio 1-1/e - \epsilon in O(n/\epsilon) queries, O(k \log (k)) memory, and O(n) time .
Revised: \ge 1. In addition, we propose a deterministic, multi-pass streaming algorithm with O(1 / \epsilon) passes that achieves ratio 1-1/e - \epsilon in O(n/\epsilon) queries, O(k \log (k)) memory, and O(n) time .
Edit intent: meaning-changed
Original: \ge  In addition, we propose a deterministic, multi-pass streaming algorithm with O(1 / \epsilon) passes that achieves ratio 1-1/e - \epsilon in O(n/\epsilon) queries, O(k \log (k)) memory, and O(n) time .
Revised: \ge In addition, we propose a deterministic, multi-pass streaming algorithm with a constant number of passes that achieves ratio 1-1/e - \epsilon in O(n/\epsilon) queries, O(k \log (k)) memory, and O(n) time .
Edit intent: clarity
Original: \ge  In addition, we propose a deterministic, multi-pass streaming algorithm with O(1 / \epsilon) passes that achieves ratio 1-1/e - \epsilon in O(n/\epsilon) queries, O(k \log (k)) memory, and O(n) time .
Revised: \ge In addition, we propose a deterministic, multi-pass streaming algorithm with O(1 / \epsilon) passes that achieves nearly the optimal ratio with linear query and time complexities .
Edit intent: meaning-changed
Original: Through the years, Sequential Convex Programming (SCP) has gained great interest as an efficient tool for non-convex optimal control . Despite the large number of existing algorithmic frameworks, only a few are accompanied by rigorous convergence analysis, which are often only tailored to discrete-time problem formulations.
Revised: Through the years, Sequential Convex Programming (SCP) has recently gained significant popularity as an effective method for solving optimal control problems and has been successfully applied in several different domains. However, the theoretical analysis of SCP has received comparatively limited attention, and it is often restricted to discrete-time problem formulations.
Edit intent: coherence
Original:  In this paper, we present a unifying theoretical analysis of a fairly general class of SCP procedures which is applied to the original continuous-time formulation. Besides the extension of classical convergence guarantees to continuous-time settings , our analysis reveals two new features inherited by SCP-type methods .
Revised:  In this paper, we present a unifying theoretical analysis of a fairly general class of SCP procedures which is applied to the original continuous-time optimal control problems. In addition to the derivation of convergence guarantees in a continuous-time settings , our analysis reveals two new features inherited by SCP-type methods .
Edit intent: clarity
Original:  Second, we demonstrate how the theoretical analysis may be leveraged to devise an accelerated implementation of SCP based on indirect methods . Detailed numerical experiments are provided to show the key benefits of a continuous-time analysis to improve performance .
Revised:  Second, we show how our theoretical analysis can be leveraged to devise an accelerated implementation of SCP based on indirect methods . Detailed numerical experiments are provided to show the key benefits of a continuous-time analysis to improve performance .
Edit intent: clarity
Original: We consider inference for high-dimensional  exchangeable arrays where the dimension may be much larger than the cluster sizes. Specifically, we consider separately and jointly exchangeable arraysthat correspond to multiway clustered and polyadic data, respectively. Such exchangeable arrays have seen a surge of applications in empirical economics. However, both exchangeability concepts induce highly complicated dependence structures, which poses a significant challenge for inference in high dimensions. In this paper, we first derive high-dimensional central limit theorems (CLTs) over the rectangles for the exchangeable arrays.
Revised: We consider inference for high-dimensional exchangeable arrays where the dimension may be much larger than the sample sizes. For both exchangeable arrays, we first derive high-dimensional central limit theorems (CLTs) over the rectangles for the exchangeable arrays.
Edit intent: coherence
Original:  In this paper, we first derive high-dimensional central limit theorems (CLTs) over the rectangles for the exchangeable arrays. Building on the high-dimensional CLTs, we develop novel multiplier bootstraps for the exchangeable arrays and derive their finite sample error bounds in high dimensions.
Revised:  In this paper, we first derive high-dimensional central limit theorems (CLTs) over the rectangles and subsequently develop novel multiplier bootstraps for the exchangeable arrays and derive their finite sample error bounds in high dimensions.
Edit intent: coherence
Original:  Building on the high-dimensional CLTs, we develop novel multiplier bootstraps for the exchangeable arrays and derive their finite sample error bounds in high dimensions. The derivations of these theoretical results rely on new technical tools such as Hoeffding-type decomposition and maximal inequalities for the degenerate components in the Hoeffiding-type decomposition for the exchangeable arrays.
Revised:  Building on the high-dimensional CLTs, we develop novel multiplier bootstraps with theoretical guarantees. These theoretical results rely on new technical tools such as Hoeffding-type decomposition and maximal inequalities for the degenerate components in the Hoeffiding-type decomposition for the exchangeable arrays.
Edit intent: clarity
Original:  We illustrate applications of our bootstrap methods to robust inference in demand analysis, robust inference in extended gravity analysis, uniform confidence bands for density estimation with network data, and penalty choice for \ell_1-penalized regression under multiway cluster sampling .
Revised:  We exhibit applications of our bootstrap methods to robust inference in demand analysis, robust inference in extended gravity analysis, uniform confidence bands for density estimation with network data, and penalty choice for \ell_1-penalized regression under multiway cluster sampling .
Edit intent: clarity
Original:  We illustrate applications of our bootstrap methods to robust inference in demand analysis, robust inference in extended gravity analysis, uniform confidence bands for density estimation with network data, and penalty choice for \ell_1-penalized regression under multiway cluster sampling .
Revised:  We illustrate applications of our methods to uniform confidence bands for density estimation with network data, and penalty choice for \ell_1-penalized regression under multiway cluster sampling .
Edit intent: clarity
Original:  We illustrate applications of our bootstrap methods to robust inference in demand analysis, robust inference in extended gravity analysis, uniform confidence bands for density estimation with network data, and penalty choice for \ell_1-penalized regression under multiway cluster sampling .
Revised:  We illustrate applications of our bootstrap methods to robust inference in demand analysis, robust inference in extended gravity analysis, uniform confidence bands for density estimation under joint exchangeability and penalty choice for \ell_1-penalized regression under multiway cluster sampling .
Edit intent: meaning-changed
Original:  We illustrate applications of our bootstrap methods to robust inference in demand analysis, robust inference in extended gravity analysis, uniform confidence bands for density estimation with network data, and penalty choice for \ell_1-penalized regression under multiway cluster sampling .
Revised:  We illustrate applications of our bootstrap methods to robust inference in demand analysis, robust inference in extended gravity analysis, uniform confidence bands for density estimation with network data, and penalty choice for \ell_1-penalized regression under separate exchangeability. Extensive simulations demonstrate precise uniform coverage rates. We illustrate by constructing uniform confidence bands for international trade network densities .
Edit intent: meaning-changed
Original:  However, when applied to zero-shot cross-lingual transfer tasks, most existing methods use only single-language input for LM finetuning, without leveraging the intrinsic cross-lingual alignment between different languages that is essential for multilingual tasks.
Revised:  However, when applied to zero-shot cross-lingual transfer tasks, most existing methods use only single-language input for LM finetuning, without leveraging the intrinsic cross-lingual alignment between different languages that proves essential for multilingual tasks.
Edit intent: clarity
Original:  Specifically, FILTER first encodes text input in the source language and its translation in the target language independently in the shallow layers, then performs cross-lingual fusion to extract multilingual knowledge in the intermediate layers, and finally performs further language-specific encoding.
Revised:  Specifically, FILTER first encodes text input in the source language and its translation in the target language independently in the shallow layers, then performs cross-language fusion to extract multilingual knowledge in the intermediate layers, and finally performs further language-specific encoding.
Edit intent: fluency
Original: We propose a novel method to sparsify attention in the Transformer model by learning to select the most-informative token representations , thus leveraging the model's information bottleneck with twofold strength. A careful analysis shows that the contextualization of encoded representations in our model is significantly more effective than in the original Transformer. We achieve a notable reduction in memory usage due to an improved differentiable top-k operator , making the model suitable to process long documents, as shown on an example of a summarization task .
Revised: We propose a novel method to sparsify attention in the Transformer model by learning to select the most-informative token representations during the training process, thus focusing on task-specific parts of the input. A reduction of quadratic time and memory complexity to sublinear was achieved due to a robust differentiable top-k operator , making the model suitable to process long documents, as shown on an example of a summarization task .
Edit intent: clarity
Original:  We achieve a notable reduction in memory usage due to an improved differentiable top-k operator , making the model suitable to process long documents, as shown on an example of a summarization task .
Revised:  We achieve a notable reduction in memory usage due to an improved differentiable top-k operator . For example, our experiments on a challenging summarization task of long documents show that our method is much faster and up to 16 times more memory efficient while significantly outperforming both dense and state-of-the-art sparse transformer models. The method can be effortlessly applied to many models used in NLP and CV, simultaneously with other improvements since representation pooling addresses a different aspect of the attention's complexity problem .
Edit intent: meaning-changed
Original: Image captioning involves identifying semantic concepts in the scene and describing them in fluent natural language. Recent approaches do not explicitly model the semantic concepts and train the model only for the end goal of caption generation . Such models lack interpretability and controllability, primarily due to sub-optimal content selection .
Revised: Training large-scale image captioning (IC) models demands access to a rich and diverse set of training examples, gathered from the wild, often from noisy alt-text data. However, recent modeling approaches to IC often fall short in terms of performance in this case, because they assume a clean annotated dataset (as opposed to the noisier alt-text--based annotations), and employ an end-to-end generation approach, which often lacks both controllability and interpretability .
Edit intent: meaning-changed
Original:  We approach the former as a weakly supervised task, using a simple off-the-shelf language syntax parser and avoiding the need for additional human annotations; the latter uses a supervised-learning approach. We investigate three methods of conditioning the caption on skeleton in the encoder, decoder and both. Our compositional model generates significantly better quality captions on out of domain test images, as judged by human annotators. Additionally, we demonstrate the cross-language effectiveness of the English skeleton to other languages including  French, Italian, German, Spanish and Hindi.
Revised:  Specifically, we show that selecting content words as skeletons French, Italian, German, Spanish and Hindi.
Edit intent: coherence
Original:  Additionally, we demonstrate the cross-language effectiveness of the English skeleton to other languages including  French, Italian, German, Spanish and Hindi.
Revised:  Additionally, we demonstrate the cross-language effectiveness of the English skeleton to other languages including helps in generating improved and denoised captions when leveraging rich yet noisy alt-text--based uncurated datasets. We also show that the predicted English skeletons can be further cross-lingually leveraged to generate non-English captions, and present experimental results covering caption generation in French, Italian, German, Spanish and Hindi.
Edit intent: meaning-changed
Original:  This compositional nature of captioning exhibits the potential of unpaired image captioning, thereby reducing the dependence on expensive image-caption pairs. Furthermore, we investigate the use of skeletons as a knob to control certain properties of the generated image caption , such as length, content, and gender expression  .
Revised:  We also show that skeleton-based prediction allows for better control of certain caption properties , such as length, content, and gender expression .
Edit intent: clarity
Original:  Furthermore, we investigate the use of skeletons as a knob to control certain properties of the generated image caption , such as length, content, and gender expression  .
Revised:  Furthermore, we investigate the use of skeletons as a knob to control certain properties of the generated image caption , such as length, content, and gender expression , providing a handle to perform human-in-the-loop semi-automatic corrections .
Edit intent: meaning-changed
Original:  It discusses the extension of the Empirical Roofline Toolkit for more data precision support and Tensor Core support and introduces an Nsight Compute based method to accurately collect application performance information.
Revised:  It discusses the extension of the Empirical Roofline Toolkit for broader support of a range of data precisions and Tensor Core support and introduces an Nsight Compute based method to accurately collect application performance information.
Edit intent: meaning-changed
Original:  We will use two versions of the code, in TensorFlow and PyTorch respectively, to demonstrate the use and effectiveness of this methodology , and some insights will be highlighted on how the application utilizes the compute and memory capabilities on the GPU and how the implementation and performance differs in two deep learning frameworks.
Revised:  We use two versions of the code, in TensorFlow and PyTorch respectively, to demonstrate the use and effectiveness of this methodology , and some insights will be highlighted on how the application utilizes the compute and memory capabilities on the GPU and how the implementation and performance differs in two deep learning frameworks.
Edit intent: fluency
Original:  We will use two versions of the code, in TensorFlow and PyTorch respectively, to demonstrate the use and effectiveness of this methodology , and some insights will be highlighted on how the application utilizes the compute and memory capabilities on the GPU and how the implementation and performance differs in two deep learning frameworks.
Revised:  We will use two versions of the code, in TensorFlow and PyTorch respectively, to demonstrate the use and effectiveness of this methodology . We highlight how the application utilizes the compute and memory capabilities on the GPU and how the implementation and performance differs in two deep learning frameworks.
Edit intent: coherence
Original:  We will use two versions of the code, in TensorFlow and PyTorch respectively, to demonstrate the use and effectiveness of this methodology , and some insights will be highlighted on how the application utilizes the compute and memory capabilities on the GPU and how the implementation and performance differs in two deep learning frameworks.
Revised:  We will use two versions of the code, in TensorFlow and PyTorch respectively, to demonstrate the use and effectiveness of this methodology , and some insights will be highlighted on how the application utilizes the compute and memory capabilities on the GPU and how the implementation and performance differ in two deep learning frameworks.
Edit intent: fluency
Original: We consider strategy-proof mechanisms to solve allocation problems where agents can choose outside options if they wish. Mechanisms could return an allocation or a randomization over allocations. We prove two useful theorems, relying on an invariance property of the allocations found by strategy-proof mechanisms when agents vary the ranking of outside options in their preferences .
Revised: Strategy-proof mechanisms are widely used in market design. In an abstract allocation framework where outside options are available to agents, we obtain two results for strategy-proof mechanisms when agents vary the ranking of outside options in their preferences .
Edit intent: clarity
Original:  Our algorithm converges robustly and exactly to the correct backpropagation gradients, requires only a single type of neuron , utilises only a single backwards phase, and can perform credit assignment on arbitrary computation graphs.
Revised:  Our algorithm converges robustly and exactly to the correct backpropagation gradients, requires only a single type of computational unit , utilises only a single backwards phase, and can perform credit assignment on arbitrary computation graphs.
Edit intent: clarity
Original: The second edition of "Semantic Relations Between Nominals" ( by Vivi Nastase, Stan Szpakowicz, Preslav Nakov and Diarmuid \'O S\'eaghdha ) will be published  by URLan & Claypool .
Revised: The second edition of "Semantic Relations Between Nominals" by Vivi Nastase, Stan Szpakowicz, Preslav Nakov and Diarmuid \'O S\'eaghdha ) will be published by URLan & Claypool .
Edit intent: fluency
Original: The second edition of "Semantic Relations Between Nominals" ( by Vivi Nastase, Stan Szpakowicz, Preslav Nakov and Diarmuid \'O S\'eaghdha ) will be published  by URLan & Claypool .
Revised: The second edition of "Semantic Relations Between Nominals" ( by Vivi Nastase, Stan Szpakowicz, Preslav Nakov and Diarmuid \'O S\'eaghdha will be published by URLan & Claypool .
Edit intent: fluency
Original: The second edition of "Semantic Relations Between Nominals" ( by Vivi Nastase, Stan Szpakowicz, Preslav Nakov and Diarmuid \'O S\'eaghdha ) will be published  by URLan & Claypool .
Revised: The second edition of "Semantic Relations Between Nominals" ( by Vivi Nastase, Stan Szpakowicz, Preslav Nakov and Diarmuid \'O S\'eaghdha ) will be published early in 2021 by URLan & Claypool .
Edit intent: meaning-changed
Original: The second edition of "Semantic Relations Between Nominals" ( by Vivi Nastase, Stan Szpakowicz, Preslav Nakov and Diarmuid \'O S\'eaghdha ) will be published  by URLan & Claypool .
Revised: The second edition of "Semantic Relations Between Nominals" ( by Vivi Nastase, Stan Szpakowicz, Preslav Nakov and Diarmuid \'O S\'eaghdha ) will be published by URLan & Claypool URLanclaypool.com/toc/hlt/1/1).
Edit intent: meaning-changed
Original: The second edition of "Semantic Relations Between Nominals" by Vivi Nastase, Stan Szpakowicz, Preslav Nakov and Diarmuid \'O S\'eaghdha will be published early in 2021 by URLan & Claypool URLanclaypool .com/ toc / hlt/1/1 ). A new Chapter 5 of the book, by Vivi Nastase and Stan Szpakowicz, discusses relation classification/extraction in the deep-learning paradigm which arose after the first edition appeared.
Revised: The second edition of "Semantic Relations Between Nominals" by Vivi Nastase, Stan Szpakowicz, Preslav Nakov and Diarmuid \'O S\'eaghdha has been published in April 2021 by URLan & Claypool URLanclaypool .com/ toc / hlt/1/1 ). A new Chapter 5 of the book, by Vivi Nastase and Stan Szpakowicz, discusses relation classification/extraction in the deep-learning paradigm which arose after the first edition appeared.
Edit intent: clarity
Original: The second edition of "Semantic Relations Between Nominals" by Vivi Nastase, Stan Szpakowicz, Preslav Nakov and Diarmuid \'O S\'eaghdha will be published early in 2021 by URLan & Claypool URLanclaypool .com/ toc / hlt/1/1 ). A new Chapter 5 of the book, by Vivi Nastase and Stan Szpakowicz, discusses relation classification/extraction in the deep-learning paradigm which arose after the first edition appeared.
Revised: The second edition of "Semantic Relations Between Nominals" by Vivi Nastase, Stan Szpakowicz, Preslav Nakov and Diarmuid \'O S\'eaghdha will be published early in 2021 by URLan & Claypool URLanclaypoolpublishers .com/ toc / hlt/1/1 ). A new Chapter 5 of the book, by Vivi Nastase and Stan Szpakowicz, discusses relation classification/extraction in the deep-learning paradigm which arose after the first edition appeared.
Edit intent: clarity
Original: The second edition of "Semantic Relations Between Nominals" by Vivi Nastase, Stan Szpakowicz, Preslav Nakov and Diarmuid \'O S\'eaghdha will be published early in 2021 by URLan & Claypool URLanclaypool .com/ toc / hlt/1/1 ). A new Chapter 5 of the book, by Vivi Nastase and Stan Szpakowicz, discusses relation classification/extraction in the deep-learning paradigm which arose after the first edition appeared.
Revised: The second edition of "Semantic Relations Between Nominals" by Vivi Nastase, Stan Szpakowicz, Preslav Nakov and Diarmuid \'O S\'eaghdha will be published early in 2021 by URLan & Claypool URLanclaypool .com/ catalog_Orig / hlt/1/1 ). A new Chapter 5 of the book, by Vivi Nastase and Stan Szpakowicz, discusses relation classification/extraction in the deep-learning paradigm which arose after the first edition appeared.
Edit intent: meaning-changed
Original: The second edition of "Semantic Relations Between Nominals" by Vivi Nastase, Stan Szpakowicz, Preslav Nakov and Diarmuid \'O S\'eaghdha will be published early in 2021 by URLan & Claypool URLanclaypool .com/ toc / hlt/1/1 ). A new Chapter 5 of the book, by Vivi Nastase and Stan Szpakowicz, discusses relation classification/extraction in the deep-learning paradigm which arose after the first edition appeared.
Revised: The second edition of "Semantic Relations Between Nominals" by Vivi Nastase, Stan Szpakowicz, Preslav Nakov and Diarmuid \'O S\'eaghdha will be published early in 2021 by URLan & Claypool URLanclaypool .com/ toc / product_info.php?products_id=1627 ). A new Chapter 5 of the book, by Vivi Nastase and Stan Szpakowicz, discusses relation classification/extraction in the deep-learning paradigm which arose after the first edition appeared.
Edit intent: meaning-changed