wanyu/IteraTeR_full_sent huggingface dataset:
Original: While Curriculum learning (CL) has recently gained traction in Natural language Processing Tasks, it still isn't being analyzed adequately . Previous works only show their effectiveness but fail short to fully explain and interpret the internal workings  .
Revised: While Curriculum learning (CL) has recently gained traction in Natural language Processing Tasks, it still isn't being analyzed adequately . Previous works only show their effectiveness but fail short to fully explain and interpret the internal workings fully .
Edit intent: clarity
Original:  Within curriculum pacing, we see that One-Pass curriculum strategies suffer from catastrophic URLetting and attention movement visualization  shows that curriculum learning breaks down the  main task into easier sub-tasks which the model solves easily .
Revised:  Within curriculum pacing, we see that One-Pass curriculum strategies suffer from catastrophic URLetting and attention movement visualization within curriculum pacing. This shows that curriculum learning breaks down the main task into easier sub-tasks which the model solves easily .
Edit intent: coherence
Original:  Within curriculum pacing, we see that One-Pass curriculum strategies suffer from catastrophic URLetting and attention movement visualization  shows that curriculum learning breaks down the  main task into easier sub-tasks which the model solves easily .
Revised:  Within curriculum pacing, we see that One-Pass curriculum strategies suffer from catastrophic URLetting and attention movement visualization shows that curriculum learning breaks down the challenging main task into easier sub-tasks which the model solves easily .
Edit intent: clarity
Original:  In the (standard) centralized setting, Bansal and Umboh [BU17] gave an O(\alpha)-approximation LP rounding algorithm   .
Revised:  In the (standard) centralized setting, Bansal and Umboh [BU17] gave an O(\alpha)-approximation LP rounding algorithm , which translates into a near-linear time algorithm using general-purpose approximation results for explicit covering LPs .
Edit intent: meaning-changed
Original:  In the (standard) centralized setting, Bansal and Umboh [BU17] gave an O(\alpha)-approximation LP rounding algorithm   .
Revised:  In the (standard) centralized setting, Bansal and Umboh [BU17] gave an O(\alpha)-approximation LP rounding algorithm KY14, You14, AZO19, Qua20 .
Edit intent: meaning-changed
Original:  There is a similar situation in the distributed setting: While there are \text{poly}%DIFDELCMD < \logn%%% -round LP-based O(\alpha)-approximation algorithms [KMW06, DKM19], the best non-LP-based algorithm by Lenzen and Wattenhofer [LW10] is an implementation of their centralized algorithm, providing an O(\alpha^2)-approximation within O(\log n) rounds with high probability.
Revised:  There is a similar situation in the distributed setting: While there }%DIFDELCMD < \logn%%% -round LP-based O(\alpha)-approximation algorithms [KMW06, DKM19], the best non-LP-based algorithm by Lenzen and Wattenhofer [LW10] is an implementation of their centralized algorithm, providing an O(\alpha^2)-approximation within O(\log n) rounds with high probability.
Edit intent: coherence
Original:  There is a similar situation in the distributed setting: While there are \text{poly}%DIFDELCMD < \logn%%% -round LP-based O(\alpha)-approximation algorithms [KMW06, DKM19], the best non-LP-based algorithm by Lenzen and Wattenhofer [LW10] is an implementation of their centralized algorithm, providing an O(\alpha^2)-approximation within O(\log n) rounds with high probability.
Revised:  There is a similar situation in the distributed setting: While there are \text{poly}%DIFDELCMD < \logn%%% is an O(\log^2 n)-round LP-based O(\alpha)-approximation algorithms [KMW06, DKM19], the best non-LP-based algorithm by Lenzen and Wattenhofer [LW10] is an implementation of their centralized algorithm, providing an O(\alpha^2)-approximation within O(\log n) rounds with high probability.
Edit intent: fluency
Original:  We address the question of whether one can achieve a simple, elementary O(\alpha)-approximation algorithm  not based on any LP-based methods, either in the centralized setting or in the distributed setting.
Revised:  We address the question of whether one can achieve a simple, elementary O(\alpha)-approximation algorithm that is elementary, i.e., not based on any LP-based methods, either in the centralized setting or in the distributed setting.
Edit intent: meaning-changed
Original:  We resolve these questions in the affirmative . More specifically, our contribution is two-fold : 1. In the centralized setting, we provide a surprisingly simple combinatorial algorithm that is asymptotically optimal in terms of both approximation factor and running time : an O(\alpha)-approximation in linear time.
Revised:  We resolve these questions in the affirmative , and en route achieve algorithms that are faster than the state-of-the-art LP-based algorithms : 1. In the centralized setting, we provide a surprisingly simple combinatorial algorithm that is asymptotically optimal in terms of both approximation factor and running time : an O(\alpha)-approximation in linear time.
Edit intent: meaning-changed
Original:  More specifically, our contribution is two-fold : 1. In the centralized setting, we provide a surprisingly simple combinatorial algorithm that is asymptotically optimal in terms of both approximation factor and running time : an O(\alpha)-approximation in linear time.
Revised:  More specifically, our contribution is two-fold : 1. In the centralized setting, we provide a surprisingly simple combinatorial algorithm that is asymptotically optimal in terms of both approximation factor and runtime : an O(\alpha)-approximation in linear time.
Edit intent: clarity
Original:   2. Based on our centralized algorithm, we design a distributed combinatorial O(\alpha)-approximation algorithm in the \mathsf{CONGEST} model that runs in O(\alpha\log n) rounds with high probability .
Revised:  The previous best O(\alpha)-approximation algorithms are LP-based and have super-linear running time. 2. Based on our centralized algorithm, we design a distributed combinatorial O(\alpha)-approximation algorithm in the \mathsf{CONGEST} model that runs in O(\alpha\log n) rounds with high probability .
Edit intent: meaning-changed
Original:   2. Based on our centralized algorithm, we design a distributed combinatorial O(\alpha)-approximation algorithm in the \mathsf{CONGEST} model that runs in O(\alpha\log n) rounds with high probability . Our round complexity outperforms the best LP-based distributed algorithm for a wide range of parameters .
Revised:  2. Based on our centralized algorithm, we design a distributed combinatorial O(\alpha)-approximation algorithm in the \mathsf{CONGEST} model that runs in O(\alpha\log n) rounds with high probability .
Edit intent: meaning-changed
Original:  In this work, we introduce a rapid multi-physics simulation framework for electro-thermal origami robotic systems that can  capture: thermo-mechancially coupled actuation, inter panel contact, heat transfer, large deformation folding, and other complex loading applied onto the origami.
Revised:  In this work, we introduce a rapid multi-physics simulation framework for electro-thermally actuated origami systems that can capture: thermo-mechancially coupled actuation, inter panel contact, heat transfer, large deformation folding, and other complex loading applied onto the origami.
Edit intent: clarity
Original:  In this work, we introduce a rapid multi-physics simulation framework for electro-thermal origami robotic systems that can  capture: thermo-mechancially coupled actuation, inter panel contact, heat transfer, large deformation folding, and other complex loading applied onto the origami.
Revised:  In this work, we introduce a rapid multi-physics simulation framework for electro-thermal origami robotic systems that can simultaneously capture: thermo-mechancially coupled actuation, inter panel contact, heat transfer, large deformation folding, and other complex loading applied onto the origami.
Edit intent: meaning-changed
Original:  Specifically, ALTO achieves a geometric mean speedup of 8X over the best mode-agnostic format , while delivering a geometric mean compression ratio of more than 4X relative to the best mode-specific format .
Revised:  Specifically, ALTO achieves a geometric mean speedup of 8X over the best mode-agnostic (coordinate and hierarchical coordinate) formats , while delivering a geometric mean compression ratio of more than 4X relative to the best mode-specific format .
Edit intent: meaning-changed
Original:  Specifically, ALTO achieves a geometric mean speedup of 8X over the best mode-agnostic format , while delivering a geometric mean compression ratio of more than 4X relative to the best mode-specific format .
Revised:  Specifically, ALTO achieves a geometric mean speedup of 8X over the best mode-agnostic format , while delivering a geometric mean compression ratio of 4.3X relative to the best mode-specific format .
Edit intent: meaning-changed
Original:  Specifically, ALTO achieves a geometric mean speedup of 8X over the best mode-agnostic format , while delivering a geometric mean compression ratio of more than 4X relative to the best mode-specific format .
Revised:  Specifically, ALTO achieves a geometric mean speedup of 8X over the best mode-agnostic format , while delivering a geometric mean compression ratio of more than 4X relative to the best mode-specific (compressed sparse fiber) formats .
Edit intent: meaning-changed
Original:  The algorithmic aspects of CPGs are also discussed, including optimal policy factorisations, their complexity , and the use of conditioning to efficiently scale to extremely large, concurrent tasks. The performance advantages for two variants of the algorithm are demonstrated on large-scale bandit and concurrent inventory management problems .
Revised:  The algorithmic aspects of CPGs are also discussed, including optimal policy factorisations, their complexity , and the implications for the bias-variance trade-off of incorrectly specifying the network. Finally, we demonstrate the performance advantages of our algorithm on large-scale bandit and concurrent inventory management problems .
Edit intent: meaning-changed
Original:  In this paper, we aim to improve the data efficiency of image captioning. We propose VisualGPT, a data-efficient image captioning model that leverages the linguistic knowledge from a large pretrained language model(LM). A crucial challenge is to balance between the use of visual information in the image and prior linguistic knowledge acquired from pretraining.
Revised:  In this paper, we propose a data-efficient image captioning model that leverages the linguistic knowledge from a large pretrained language model(LM). A crucial challenge is to balance between the use of visual information in the image and prior linguistic knowledge acquired from pretraining.
Edit intent: coherence
Original:  We propose VisualGPT, a data-efficient image captioning model that leverages the linguistic knowledge from a large pretrained language model(LM). A crucial challenge is to balance between the use of visual information in the image and prior linguistic knowledge acquired from pretraining.
Revised:  We propose VisualGPT, a data-efficient image captioning model , VisualGPT, which leverages the linguistic knowledge from a large pretrained language model(LM). A crucial challenge is to balance between the use of visual information in the image and prior linguistic knowledge acquired from pretraining.
Edit intent: coherence
Original:  When trained on 0.1\%, 0.5\% and 1\% of MSCOCO and Conceptual Captions , the proposed model, VisualGPT, surpasses strong image captioning baselines.
Revised:  We train the proposed model, VisualGPT, on 0.1\%, 0.5\% and 1\% of MSCOCO and Conceptual Captions , the proposed model, VisualGPT, surpasses strong image captioning baselines.
Edit intent: fluency
Original:  When trained on 0.1\%, 0.5\% and 1\% of MSCOCO and Conceptual Captions , the proposed model, VisualGPT, surpasses strong image captioning baselines. VisualGPT outperforms the best baseline model by up to 10.8\% CIDEr on MS COCO and up to 5.4\% CIDEr on Conceptual Captions.
Revised:  When trained on 0.1\%, 0.5\% and 1\% of MSCOCO and Conceptual Captions training data. Under these conditions, we outperform the best baseline model by up to 10.8\% CIDEr on MS COCO and up to 5.4\% CIDEr on Conceptual Captions.
Edit intent: clarity
Original:  VisualGPT outperforms the best baseline model by up to 10.8\% CIDEr on MS COCO and up to 5.4\% CIDEr on Conceptual Captions.
Revised:  VisualGPT outperforms the best baseline model by up to 10.8\% CIDEr on MS COCO and upto 5.4\% CIDEr on Conceptual Captions.
Edit intent: fluency
Original: The graph convolutional network (GCN) is a go-to solution for machine learning on graphs, but its training is notoriously difficult to scale  in terms of both the size of the graph and the number of model parameters.
Revised: The graph convolutional network (GCN) is a go-to solution for machine learning on graphs, but its training is notoriously difficult to scale both in terms of both the size of the graph and the number of model parameters.
Edit intent: fluency
Original: The graph convolutional network (GCN) is a go-to solution for machine learning on graphs, but its training is notoriously difficult to scale  in terms of both the size of the graph and the number of model parameters.
Revised: The graph convolutional network (GCN) is a go-to solution for machine learning on graphs, but its training is notoriously difficult to scale in terms of graph size and the number of model parameters.
Edit intent: clarity
Original:  These limitations are in stark contrast to the increasing scale (in data size and model size) of experiments in deep learning research.In this work, we propose GIST, a novel distributed approach that enables efficient training of wide (overparameterized) GCNs on large graphs.
Revised:  Although some work has explored training on large-scale graphs (e.g., GraphSAGE, ClusterGCN, etc.), we pioneer efficient training of wide (overparameterized) GCNs on large graphs.
Edit intent: meaning-changed
Original: In this work, we propose GIST, a novel distributed approach that enables efficient training of wide (overparameterized) GCNs on large graphs. GIST is a hybrid layer and graph sampling method, which disjointly partitions the global model into several, smaller sub-GCNs that are independently trained across multiple GPUs in parallel.
Revised: In this work, we propose GIST, a novel distributed approach that enables efficient training of large-scale GCN models (i.e., ultra-wide, overparameterized models) with the proposal of a novel, distributed training framework. Our proposed training methodology, called GIST, disjointly partitions the global model into several, smaller sub-GCNs that are independently trained across multiple GPUs in parallel.
Edit intent: clarity
Original:  GIST is a hybrid layer and graph sampling method, which disjointly partitions the global model into several, smaller sub-GCNs that are independently trained across multiple GPUs in parallel.
Revised:  GIST is a hybrid layer and graph sampling method, which disjointly partitions the parameters of a GCN model into several, smaller sub-GCNs that are independently trained across multiple GPUs in parallel.
Edit intent: clarity
Original:  This distributed framework improves model performance and significantly decreases wall-clock training time . GIST seeks to enable large-scale GCN experimentation with the goal of bridging the existing gap in scale between graph machine learning and deep learning .
Revised:  In addition to being compatible with any GCN architecture, GIST improves model performance and significantly decreases wall-clock training time . GIST seeks to enable large-scale GCN experimentation with the goal of bridging the existing gap in scale between graph machine learning and deep learning .
Edit intent: meaning-changed
Original:  This distributed framework improves model performance and significantly decreases wall-clock training time . GIST seeks to enable large-scale GCN experimentation with the goal of bridging the existing gap in scale between graph machine learning and deep learning .
Revised:  This distributed framework improves model performance , scales to training on arbitrarily large graphs, significantly decreases wall-clock training time . GIST seeks to enable large-scale GCN experimentation with the goal of bridging the existing gap in scale between graph machine learning and deep learning .
Edit intent: meaning-changed
Original:  This distributed framework improves model performance and significantly decreases wall-clock training time . GIST seeks to enable large-scale GCN experimentation with the goal of bridging the existing gap in scale between graph machine learning and deep learning .
Revised:  This distributed framework improves model performance and significantly decreases wall-clock training time , and enables the training of markedly overparameterized GCN models. Remarkably, with GIST, we train an astonishgly-wide 32,768-dimensional GraphSAGE model, which exceeds the capacity of a single GPU by a factor of 8X, to SOTA performance on the Amazon2M dataset .
Edit intent: meaning-changed
Original:  With this, we offer new insights into understanding the action selection mechanisms in RL . During the analysis of the robotic environment, we present various small modifications on the very same schema that lead to different optimal policies . Finally, we emphasize that beyond building efficient RL algorithms, only the proper design of the environment can ensure the desired results .
Revised:  With this, we offer new insights into understanding the action selection mechanisms in RL . During the analysis of the robotic environment, we present various small modifications on the very same schema that lead to different optimal policies .
Edit intent: coherence
Original:  We find that CheXseg improves upon the performance (mIoU) of fully-supervised methods that use only pixel-level expert annotations by 13.4 \% and weakly-supervised methods that use only DNN-generated saliency maps by 91.2\%.
Revised:  We find that CheXseg improves upon the performance (mIoU) of fully-supervised methods that use only pixel-level expert annotations by 9.7 \% and weakly-supervised methods that use only DNN-generated saliency maps by 91.2\%.
Edit intent: meaning-changed
Original:  We find that CheXseg improves upon the performance (mIoU) of fully-supervised methods that use only pixel-level expert annotations by 13.4 \% and weakly-supervised methods that use only DNN-generated saliency maps by 91.2\%. Furthermore, we implement a semi-supervised method using knowledge distillation and find that though it is outperformed by CheXseg, it exceeds the performance (mIoU) of the best fully-supervised method by 4.83\%.
Revised:  We find that CheXseg improves upon the performance (mIoU) of fully-supervised methods that use only pixel-level expert annotations by 13.4 \% and weakly-supervised methods that use only DNN-generated saliency maps by 73.1\%.
Edit intent: meaning-changed
Original:  The extension is based on the approach of filling the missing elements to minimise the eigenvalue of the incomplete matrix . It means that the well-established values of the random index , a crucial component of the consistency ratio for which the famous threshold of 0.1 provides the condition for the acceptable level of inconsistency, cannot be directly adopted.
Revised:  The extension is based on the approach of filling the missing elements to minimise the eigenvalue of the incomplete matrix is minimised. Consequently, the well-established values of the random index , a crucial component of the consistency ratio for which the famous threshold of 0.1 provides the condition for the acceptable level of inconsistency, cannot be directly adopted.
Edit intent: meaning-changed
Original:  It means that the well-established values of the random index , a crucial component of the consistency ratio for which the famous threshold of 0.1 provides the condition for the acceptable level of inconsistency, cannot be directly adopted. The inconsistency of random matrices turns out to be the function of matrix size and the number of missing elements, with a nearly linear dependence in the case of the latter variable.
Revised:  It means that the well-established values of the random index cannot be adopted: the inconsistency of random matrices turns out to be the function of matrix size and the number of missing elements, with a nearly linear dependence in the case of the latter variable.
Edit intent: clarity
Original: Computing the expectation of some kernel function is ubiquitous in machine learning, from the classical theory of support vector machines , to exploiting kernel embeddings of distributions in applications ranging from probabilistic modeling, statistical inference, casual discovery, and deep learning.
Revised: Computing the expectation of kernel functions is a ubiquitous task in machine learning, from the classical theory of support vector machines , to exploiting kernel embeddings of distributions in applications ranging from probabilistic modeling, statistical inference, casual discovery, and deep learning.
Edit intent: clarity
Original: Computing the expectation of some kernel function is ubiquitous in machine learning, from the classical theory of support vector machines , to exploiting kernel embeddings of distributions in applications ranging from probabilistic modeling, statistical inference, casual discovery, and deep learning.
Revised: Computing the expectation of some kernel function is ubiquitous in machine learning, with applications from classical support vector machines , to exploiting kernel embeddings of distributions in applications ranging from probabilistic modeling, statistical inference, casual discovery, and deep learning.
Edit intent: clarity
Original: Computing the expectation of some kernel function is ubiquitous in machine learning, from the classical theory of support vector machines , to exploiting kernel embeddings of distributions in applications ranging from probabilistic modeling, statistical inference, casual discovery, and deep learning.
Revised: Computing the expectation of some kernel function is ubiquitous in machine learning, from the classical theory of support vector machines to exploiting kernel embeddings of distributions in applications ranging from probabilistic modeling, statistical inference, casual discovery, and deep learning.
Edit intent: fluency
Original: Computing the expectation of some kernel function is ubiquitous in machine learning, from the classical theory of support vector machines , to exploiting kernel embeddings of distributions in applications ranging from probabilistic modeling, statistical inference, casual discovery, and deep learning.
Revised: Computing the expectation of some kernel function is ubiquitous in machine learning, from the classical theory of support vector machines , to exploiting kernel embeddings of distributions in probabilistic modeling, statistical inference, casual discovery, and deep learning.
Edit intent: clarity
Original: Computing the expectation of some kernel function is ubiquitous in machine learning, from the classical theory of support vector machines , to exploiting kernel embeddings of distributions in applications ranging from probabilistic modeling, statistical inference, casual discovery, and deep learning.
Revised: Computing the expectation of some kernel function is ubiquitous in machine learning, from the classical theory of support vector machines , to exploiting kernel embeddings of distributions in applications ranging from probabilistic modeling, statistical inference, causal discovery, and deep learning.
Edit intent: clarity