wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We address this scenario by studying a variant of  stochastic susceptible-exposed-infected-recovered (SEIR) model on arbitrary network instances using an analytical framework based on the method of dynamic message-passing.
Revised:  We address this scenario by studying a variant of a stochastic susceptible-exposed-infected-recovered (SEIR) model on arbitrary network instances using an analytical framework based on the method of dynamic message-passing.
Edit intent: fluency
Original:  These have clear implications on different containment strategies through topological (reducing contacts) and infection parameter changes (e.g., social distancing and wearing face masks), with relevance to the recent COVID-19 pandemic . Our studies show quantitatively the deficiency of using the reproduction number as a measure for predicting the spreading rate in some topologies, how effective isolation reduces the need in strict social distancing measures, and the importance of mass testing in scenarios with long asymptomatic exposure since isolation of symptomatic individuals is insufficient to mitigate the spread .
Revised:  These have clear implications on different containment strategies through topological (reducing contacts) and infection parameter changes (e.g., social distancing and wearing face masks), with relevance to the recent COVID-19 pandemic .
Edit intent: coherence
Original: We propose a methodology for effectively modeling individual heterogeneity using deep learning while still retaining the interpretability and economic discipline of classical models. We pair a transparent, interpretable modeling structure with rich data environments and machine learning methods to estimate heterogeneous parameters based on potentially high dimensional or complex observable characteristics.
Revised: We develop methodology for estimation and inference using machine learning to enrich economic models. Our framework takes a standard economic model and recasts the parameters as fully flexible nonparametric functions, to capture the rich heterogeneity based on potentially high dimensional or complex observable characteristics.
Edit intent: clarity
Original:  Our framework is widely-applicable, covering numerous settings of economic interest.
Revised:  These "parameter functions" retain the interpretability, economic meaning, and discipline of classical parameters. Deep learning is particularly well-suited to structured modeling of heterogeneity in economics. We show how to design the network architecture to match the structure of the economic model, delivering novel methodology that moves deep learning beyond prediction. We prove convergence rates for the estimated parameter functions. These functions are the key inputs into the finite-dimensional parameter of inferential interest.
Edit intent: meaning-changed
Original:  We recover, as special cases, well-known examples such as average treatment effects and parametric components of partially linear models. However, we also seamlessly deliver new results for diverse examples such as price elasticities, willingness-to-pay, and surplus measures in  choice models, average marginal and partial effects of continuous treatment variables, fractional outcome models, count data, heterogeneous production function components , and more.
Revised:  We obtain inference based on a novel influence function calculation that covers any second-stage parameter and any machine-learning-enriched model that uses a smooth per-observation loss function. No additional derivations are required. The score can be taken directly to data, using automatic differentiation if needed. The researcher need only define the original model and define the parameter of interest. A key insight is that we need not write down the influence function in order to evaluate it on the data. Our framework gives new results for diverse examples such as price elasticities, willingness-to-pay, and surplus measures in choice models, average marginal and partial effects of continuous treatment variables, fractional outcome models, count data, heterogeneous production function components , and more.
Edit intent: meaning-changed
Original:  However, we also seamlessly deliver new results for diverse examples such as price elasticities, willingness-to-pay, and surplus measures in  choice models, average marginal and partial effects of continuous treatment variables, fractional outcome models, count data, heterogeneous production function components , and more.
Revised:  However, we also seamlessly deliver new results for a host of contexts, covering such diverse examples as price elasticities, willingness-to-pay, and surplus measures in choice models, average marginal and partial effects of continuous treatment variables, fractional outcome models, count data, heterogeneous production function components , and more.
Edit intent: clarity
Original:  However, we also seamlessly deliver new results for diverse examples such as price elasticities, willingness-to-pay, and surplus measures in  choice models, average marginal and partial effects of continuous treatment variables, fractional outcome models, count data, heterogeneous production function components , and more.
Revised:  However, we also seamlessly deliver new results for diverse examples such as price elasticities, willingness-to-pay, and surplus measures in binary or multinomial choice models, average marginal and partial effects of continuous treatment variables, fractional outcome models, count data, heterogeneous production function components , and more.
Edit intent: meaning-changed
Original:  However, we also seamlessly deliver new results for diverse examples such as price elasticities, willingness-to-pay, and surplus measures in  choice models, average marginal and partial effects of continuous treatment variables, fractional outcome models, count data, heterogeneous production function components , and more.
Revised:  However, we also seamlessly deliver new results for diverse examples such as price elasticities, willingness-to-pay, and surplus measures in choice models, effects of continuous treatment variables, fractional outcome models, count data, heterogeneous production function components , and more.
Edit intent: coherence
Original:  However, we also seamlessly deliver new results for diverse examples such as price elasticities, willingness-to-pay, and surplus measures in  choice models, average marginal and partial effects of continuous treatment variables, fractional outcome models, count data, heterogeneous production function components , and more.
Revised:  However, we also seamlessly deliver new results for diverse examples such as price elasticities, willingness-to-pay, and surplus measures in choice models, average marginal and partial effects of continuous treatment variables, fractional outcome models, count data, heterogeneous production functions , and more.
Edit intent: clarity
Original:  Deep neural networks are well-suited to structured modeling of heterogeneity: we show how the network architecture can be designed to match the global structure of the economic model, giving novel methodology for deep learning as well as, more formally, improved rates of convergence. Our results on deep learning have consequences for other structured modeling environments and applications, such as for additive models. Our inference results are based on an influence function we derive, which we show to be flexible enough to to encompass all settings with a single, unified calculation, removing any requirement for case-by-case derivations. The usefulness of the methodology in economics is shown in two empirical applications: the response of 410(k) participation rates to firm matching and the impact of prices on subscription choices for an online service. Extensions to instrumental variables and multinomial choices are shown .
Revised:  We apply our methodology to a large scale advertising experiment for short-term loans. We show how economically meaningful estimates and inferences can be made that would be unavailable without our results .
Edit intent: meaning-changed
Original: Robotics research has found numerous important applications of Riemannian geometry , despite the background mathematical material being strikingly challenging. This material is often remains inaccessible to roboticists, and despite many natural generalizations within the mathematical literature, there remain very few applications of modern highly-relevant geometric generalizations such as spray or Finsler geometry .
Revised: Robotics research has found numerous important applications of Riemannian geometry . Despite that, the concept remain challenging to many roboticists because the background material is complex and strikingly foreign. Beyond Riemannian geometry, there are many natural generalizations within the mathematical literature, there remain very few applications of modern highly-relevant geometric generalizations such as spray or Finsler geometry .
Edit intent: meaning-changed
Original:  This material is often remains inaccessible to roboticists, and despite many natural generalizations within the mathematical literature, there remain very few applications of modern highly-relevant geometric generalizations such as spray or Finsler geometry .
Revised:  This material is often remains inaccessible to roboticists, and despite many natural generalizations in the mathematical literature---areas such as Finsler geometry and spray geometry---but those generalizations are largely inaccessible, and as a result there remain few applications within robotics .
Edit intent: meaning-changed
Original:  This paper presents an entire re-derivation of generalized spray and Finsler geometries focusing on building the ideas from familiar concepts in advanced calculus and the calculus of variations.
Revised:  This paper presents an entire re-derivation of generalized spray and Finsler geometries , critical for the development of our recent work on geometric fabrics, which builds the ideas from familiar concepts in advanced calculus and the calculus of variations.
Edit intent: meaning-changed
Original:  We focus on the pragmatic and calculable results and avoid the use of tensor notation to appeal to a broader audience . We have already used these derivations for important advances in our own work (for instance, geometric fabrics); it is our hope that they will contribute to an increased understanding of these ideas within the robotics community and inspire future research into applications of generalized nonlinear, Finsler, and even Riemannian geometries .
Revised:  We focus on the pragmatic and calculable results and avoid the use of tensor notation to appeal to a broader audience and emphasizing geometric path consistency over ideas around connections and curvature. It is our hope that they will contribute to an increased understanding of these ideas within the robotics community and inspire future research into applications of generalized nonlinear, Finsler, and even Riemannian geometries .
Edit intent: meaning-changed
Original:  it is our hope that they will contribute to an increased understanding of these ideas within the robotics community and inspire future research into applications of generalized nonlinear, Finsler, and even Riemannian geometries .
Revised:  it is our hope that they will contribute to an increased understanding generalized nonlinear, and even classical Riemannian, geometry within the robotics community and inspire future research into applications of generalized nonlinear, Finsler, and even Riemannian geometries .
Edit intent: meaning-changed
Original:  Beyond Riemannian {\em  geometry, there are many natural generalizations in the mathematical literature---areas such as Finsler geometry and spray geometry---but those generalizations are largely inaccessible, and as a result there remain few applications within robotics.
Revised:  Beyond {\em geometry, there are many natural generalizations in the mathematical literature---areas such as Finsler geometry and spray geometry---but those generalizations are largely inaccessible, and as a result there remain few applications within robotics.
Edit intent: meaning-changed
Original:  Beyond Riemannian {\em  geometry, there are many natural generalizations in the mathematical literature---areas such as Finsler geometry and spray geometry---but those generalizations are largely inaccessible, and as a result there remain few applications within robotics.
Revised:  Beyond Riemannian {\em Riemannian geometry, there are many natural generalizations in the mathematical literature---areas such as Finsler geometry and spray geometry---but those generalizations are largely inaccessible, and as a result there remain few applications within robotics.
Edit intent: meaning-changed
Original:  Beyond Riemannian {\em  geometry, there are many natural generalizations in the mathematical literature---areas such as Finsler geometry and spray geometry---but those generalizations are largely inaccessible, and as a result there remain few applications within robotics.
Revised:  Beyond Riemannian {\em geometry, there are many natural generalizations in the mathematical literature -- areas such as Finsler geometry and spray geometry---but those generalizations are largely inaccessible, and as a result there remain few applications within robotics.
Edit intent: fluency
Original:  Beyond Riemannian {\em  geometry, there are many natural generalizations in the mathematical literature---areas such as Finsler geometry and spray geometry---but those generalizations are largely inaccessible, and as a result there remain few applications within robotics.
Revised:  Beyond Riemannian {\em geometry, there are many natural generalizations in the mathematical literature---areas such as Finsler geometry and spray geometry -- but those generalizations are largely inaccessible, and as a result there remain few applications within robotics.
Edit intent: fluency
Original:  This paper presents a re-derivation of spray and Finsler geometries , critical for the development of our recent work on geometric fabrics, which builds the ideas from familiar concepts in advanced calculus and the calculus of variations  .
Revised:  This paper presents a re-derivation of spray and Finsler geometries we found critical for the development of our recent work on geometric fabrics, which builds the ideas from familiar concepts in advanced calculus and the calculus of variations .
Edit intent: meaning-changed
Original:  This paper presents a re-derivation of spray and Finsler geometries , critical for the development of our recent work on geometric fabrics, which builds the ideas from familiar concepts in advanced calculus and the calculus of variations  .
Revised:  This paper presents a re-derivation of spray and Finsler geometries , critical for the development of our recent work on a powerful behavioral design tool we call geometric fabrics. These derivations build from basic tools in advanced calculus and the calculus of variations .
Edit intent: clarity
Original:  This paper presents a re-derivation of spray and Finsler geometries , critical for the development of our recent work on geometric fabrics, which builds the ideas from familiar concepts in advanced calculus and the calculus of variations  .
Revised:  This paper presents a re-derivation of spray and Finsler geometries , critical for the development of our recent work on geometric fabrics, which builds the ideas from familiar concepts in advanced calculus and the calculus of variations making them more accessible to a robotics audience than standard presentations .
Edit intent: meaning-changed
Original:  We describe a suite of mathematical modeling tools that practitioners can employ in practice and demonstrate both how to mitigate system complexity by constructing behaviors layer-wise and how to employ these tools to design robust  strongly-generalizing  policies that solve practical problems one would expect to find in industry applications.
Revised:  We describe a suite of mathematical modeling tools that practitioners can employ in practice and demonstrate both how to mitigate system complexity by constructing behaviors layer-wise and how to employ these tools to design robust , strongly-generalizing policies that solve practical problems one would expect to find in industry applications.
Edit intent: fluency
Original:  We describe a suite of mathematical modeling tools that practitioners can employ in practice and demonstrate both how to mitigate system complexity by constructing behaviors layer-wise and how to employ these tools to design robust  strongly-generalizing  policies that solve practical problems one would expect to find in industry applications.
Revised:  We describe a suite of mathematical modeling tools that practitioners can employ in practice and demonstrate both how to mitigate system complexity by constructing behaviors layer-wise and how to employ these tools to design robust strongly-generalizing , policies that solve practical problems one would expect to find in industry applications.
Edit intent: fluency
Original: The ability to preserve local geometry of highly nonlinear manifolds in high dimensional spaces and properly unfold them into lower dimensional hyperplanes is the key to the success of manifold computing, nonlinear dimensionality reduction (NLDR) and visualization.
Revised: The ability to preserve local geometry of highly nonlinear manifolds in high dimensional spaces and properly unfold them into lower dimensional hyperplanes is the key to the success of manifold computing, nonlinear dimensionality reduction (NLDR) plays an important role in feature extraction and visualization of high dimensional data. It transforms patterns of interest, or manifolds, in data to regions in a lower-dimensional latent space. When the manifolds are highly complicated, the NLDR transformation has to provide sufficient nonlinearity for downstream tasks.
Edit intent: meaning-changed
Original:  This paper proposes a novel method, called elastic locally isometric smoothness (ELIS {\em   ), to empower deep neural networks with such an ability. ELIS requires that a desired metric between points should be preserved across layers in order to preserve local geometry; such a smoothness constraint effectively regularizes vector-based transformations to become well-behaved local metric-preserving homeomorphisms. Moreover, ELIS requires that the smoothness should be imposed in a way to render sufficient flexibility for tackling complicated nonlinearity and non-Euclideanity; this is achieved layer-wisely via nonlinearity in both the similarity and activation functions. The ELIS method incorporates a class of suitable nonlinear similarity functions into a two-way divergence loss and uses hyperparameter continuation in finding optimal solutions . Extensive experiments, comparisons, and ablation study demonstrate that ELIS can deliver results not only superior to UMAP and t-SNE for and visualization but also better than other leading counterparts of manifold and autoencoder learning for NLDR and manifold data generation .
Revised:  This paper proposes a novel method, called elastic locally isometric smoothness (ELIS {\em ), to tackle this problem. As a multi-layer neural network, DMT can support a higher degree of nonlinearity than single-layer methods. Continuation strategy is applied during training to address the local minimum problem common in manifold learning algorithms. Such a learned DMT network can generalize to unseen data whereas traditional manifold learning methods only provide an embedding of the training data . Extensive experiments, comparisons, and ablation study demonstrate that ELIS can deliver results not only superior to UMAP and t-SNE for and visualization but also better than other leading counterparts of manifold and autoencoder learning for NLDR and manifold data generation .
Edit intent: coherence
Original:  The ELIS method incorporates a class of suitable nonlinear similarity functions into a two-way divergence loss and uses hyperparameter continuation in finding optimal solutions . Extensive experiments, comparisons, and ablation study demonstrate that ELIS can deliver results not only superior to UMAP and t-SNE for and visualization but also better than other leading counterparts of manifold and autoencoder learning for NLDR and manifold data generation .
Revised:  The ELIS method incorporates a class of suitable nonlinear similarity functions into a two-way divergence loss and uses hyperparameter continuation in finding optimal solutions . Extensive experiments, comparisons, and ablation studies demonstrate that DMT can deliver results not only superior to UMAP and t-SNE for and visualization but also better than other leading counterparts of manifold and autoencoder learning for NLDR and manifold data generation .
Edit intent: meaning-changed
Original:  The ELIS method incorporates a class of suitable nonlinear similarity functions into a two-way divergence loss and uses hyperparameter continuation in finding optimal solutions . Extensive experiments, comparisons, and ablation study demonstrate that ELIS can deliver results not only superior to UMAP and t-SNE for and visualization but also better than other leading counterparts of manifold and autoencoder learning for NLDR and manifold data generation .
Revised:  The ELIS method incorporates a class of suitable nonlinear similarity functions into a two-way divergence loss and uses hyperparameter continuation in finding optimal solutions . Extensive experiments, comparisons, and ablation study demonstrate that ELIS can deliver results superior to UMAP and t-SNE for and visualization but also better than other leading counterparts of manifold and autoencoder learning for NLDR and manifold data generation .
Edit intent: clarity
Original:  The ELIS method incorporates a class of suitable nonlinear similarity functions into a two-way divergence loss and uses hyperparameter continuation in finding optimal solutions . Extensive experiments, comparisons, and ablation study demonstrate that ELIS can deliver results not only superior to UMAP and t-SNE for and visualization but also better than other leading counterparts of manifold and autoencoder learning for NLDR and manifold data generation .
Revised:  The ELIS method incorporates a class of suitable nonlinear similarity functions into a two-way divergence loss and uses hyperparameter continuation in finding optimal solutions . Extensive experiments, comparisons, and ablation study demonstrate that ELIS can deliver results not only superior to UMAP and t-SNE , and other leading manifold-based NLDR methods .
Edit intent: clarity
Original:  It transforms patterns of interest, or manifolds, in datato regions in a lower-dimensional latent space. When the manifolds are highly complicated, the NLDR transformation has to provide sufficient nonlinearity for downstream tasks. This paperproposes a novel method, called%DIFDELCMD < {\em %%%  deep manifold transformation (DMT) , to tackle this problem.
Revised:  %DIFDELCMD < {\em %%% deep manifold transformation (DMT) , to tackle this problem.
Edit intent: coherence
Original:  This paperproposes a novel method, called%DIFDELCMD < {\em %%%  deep manifold transformation (DMT) , to tackle this problem.
Revised:  This paperproposes a novel method, called%DIFDELCMD < {\em %%% In this paper, we propose a deep manifold learning framework, called deep manifold transformation (DMT) , to tackle this problem.
Edit intent: meaning-changed