wanyu/IteraTeR_full_sent huggingface dataset:
Original:  DNN models such as the Multi-layer Perceptron (MLP)  have shown remarkable success in modelling complex data accurately, but recent studies have consistently demonstrated that their black-box properties are incompatible with discrete choice analysis for the purpose of interpreting decision making behaviour.
Revised:  DNN models such as the Multi-layer Perceptron (MLP) have shown remarkable success in modelling complex and noisy behavioural data. However, econometric studies have argued that machine learning techniques are a ` black-box properties are incompatible with discrete choice analysis for the purpose of interpreting decision making behaviour.
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Original:  DNN models such as the Multi-layer Perceptron (MLP)  have shown remarkable success in modelling complex data accurately, but recent studies have consistently demonstrated that their black-box properties are incompatible with discrete choice analysis for the purpose of interpreting decision making behaviour.Our proposed machine learning choice model is a departure from the conventional feed-forward MLP framework by using a dynamic residual neural network learning based approach. Our proposed method can be formulated as a Generalized Extreme Value (GEV) random utility maximization model for greater flexibility in capturing unobserved heterogeneity.It can generate choice model structures where the covariance between random utilities is estimated and incorporated into the random error terms, allowing for a richer set of higher-order substitution patterns than a standard logit might be able to achieve .
Revised:  DNN models such as the Multi-layer Perceptron (MLP) have shown remarkable success in modelling complex data accurately, but recent studies have consistently demonstrated that their black-box ' and difficult to interpret for use in the choice analysis.We develop a data-driven choice model that extends the systematic utility function to incorporate non-linear cross-effects using a series of residual layers and using skipped connections to handle model identifiability in estimating a large number of parameters.The model structure accounts for cross-effects and choice heterogeneity arising from substitution, interactions with non-chosen alternatives and other effects in a non-linear manner .
Edit intent: clarity
Original: We describe the process of our model estimation and examine the relative empirical performance and econometric implications on two mode choice experiments. We analyzed the behavioural and theoretical properties of our methodology.We showed how model interpretability is possible, while also capturing the underlying complex and unobserved behavioural heterogeneity effects in the residual covariance matrices .
Revised: We describe the process of our model estimation and examine the relative empirical performance and econometric implications of our proposed model.We present an illustrative example of the model on a classic red/blue bus choice scenario example. For a real-world application, we use a travel mode choice dataset to analyze the model characteristics compared to traditional neural networks and Logit formulations.Our findings show that our ResLogit approach significantly outperforms MLP models while providing similar interpretability as a Multinomial Logit model .
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Original:  We consider the presence of additive noise in a deterministic SIS model over a random graph sampled from a piecewise Lipschitz graphon with finite rank. We derive a noise index to evaluate the deviation from the disease-free state due to noise.
Revised:  We consider the presence of additive noise in a linearized SIS model and we derive a noise index to evaluate the deviation from the disease-free state due to noise.
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Original:  For finite graphs, the index depends on the adjacency eigenvalues of the graphs. Using the eigenvalues of the associated graphon operator, we are able to find an approximation of the index that is tight when the network size goes to infinity.
Revised:  For finite graphs, the index depends on the adjacency eigenvalues of its graph. We then assume that the graph is a random sample from a piecewise Lipschitz graphon with finite rank and, using the eigenvalues of the associated graphon operator, we are able to find an approximation of the index that is tight when the network size goes to infinity.
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Original:  Moving beyond a conventional RNN based language model that ignores long-range word dependencies and sentence order, the proposed model captures not only intra-sentence word dependencies, but also temporal transitions between sentences and inter-sentence topic dependences .
Revised:  Moving beyond a conventional RNN-based language model that ignores long-range word dependencies and sentence order, the proposed model captures not only intra-sentence word dependencies, but also temporal transitions between sentences and inter-sentence topic dependences .
Edit intent: fluency
Original:  Moving beyond a conventional RNN based language model that ignores long-range word dependencies and sentence order, the proposed model captures not only intra-sentence word dependencies, but also temporal transitions between sentences and inter-sentence topic dependences .
Revised:  Moving beyond a conventional RNN based language model that ignores long-range word dependencies and sentence order, the proposed model captures not only intra-sentence word dependencies, but also temporal transitions between sentences and inter-sentence topic dependencies .
Edit intent: fluency
Original:  Experimental results on a variety of real-world text corpora demonstrate that the proposed model not only outperforms state-of-the-art larger-context RNN-based language models, but also learns interpretable recurrent multilayer topics and generates diverse sentences and paragraphs that are syntactically correct and semantically coherent.
Revised:  Experimental results on a variety of real-world text corpora demonstrate that the proposed model not only outperforms larger-context RNN-based language models, but also learns interpretable recurrent multilayer topics and generates diverse sentences and paragraphs that are syntactically correct and semantically coherent.
Edit intent: clarity
Original:  The probabilities of links are obtained by fusing information at multiple levels of resolution: time series models are used to score connections at the edge level, and spectral methods provide estimates of latent positions for each node  .
Revised:  The probabilities of links are obtained by fusing information at multiple levels of resolution: time series models are used to score connections at the edge level, and spectral methods provide estimates of latent positions for each node , and time series models are used to capture temporal dynamics .
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Original:  Each iteration in the EPL sequence is consistent and asymptotically efficient, and iterating to convergence improves finite sample performance. For dynamic single-agent models, we show that Aguirregabiria and Mira's (2002, 2007) Nested Pseudo-Likelihood (NPL) estimator arises as a special case of EPL. In dynamic games, EPL maintains its efficiency properties , although NPL does not . And a convenient change of variable in the equilibrium fixed point equation ensures EPL iterationshave the same computational simplicity as NPL iterations .
Revised:  Each iteration in the EPL sequence is consistent and asymptotically efficient, so the first-order asymptotic properties do not vary across iterations .
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Original:  Furthermore, EPL iterations are stable and locally convergent to the finite-sample maximum likelihood estimator at a nearly-quadratic rate for all regular Markov perfect equilibria, including unstable equilibria where NPL encounters convergence problems.
Revised:  Furthermore, we show the sequence achieves higher-order equivalence to the finite-sample maximum likelihood estimator at a nearly-quadratic rate for all regular Markov perfect equilibria, including unstable equilibria where NPL encounters convergence problems.
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Original:  Furthermore, EPL iterations are stable and locally convergent to the finite-sample maximum likelihood estimator at a nearly-quadratic rate for all regular Markov perfect equilibria, including unstable equilibria where NPL encounters convergence problems.
Revised:  Furthermore, EPL iterations are stable and locally convergent to the finite-sample maximum likelihood estimator with iteration and that the sequence of estimators converges almost surely to the maximum likelihood estimator at a nearly-superlinear rate when the data are generated by any regular Markov perfect equilibria, including unstable equilibria where NPL encounters convergence problems.
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Original:  Furthermore, EPL iterations are stable and locally convergent to the finite-sample maximum likelihood estimator at a nearly-quadratic rate for all regular Markov perfect equilibria, including unstable equilibria where NPL encounters convergence problems.
Revised:  Furthermore, EPL iterations are stable and locally convergent to the finite-sample maximum likelihood estimator at a nearly-quadratic rate for all regular Markov perfect equilibrium, including equilibria that lead to inconsistency of other sequential estimators. When utility is linear in parameters, k-EPL iterations are computationally simple, only requiring that the researcher solve linear systems of equations to generate pseudo-regressors which are used in a static logit/probit regression.
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Original:  Disentangling the contributions of inertial terms, dissipation and noise to the dynamical behavior is crucial to describe their behaviour, cohesion and motion. Reliable methods are needed to extract that information from experimental data  .
Revised:  Limited experimental access to the configurational degrees of freedom results in data that appears to be generated by a non-Markovian process. This poses a challenge in the quantitative reconstruction of the model from experimental data .
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Original:  We first show, using the analytically tractable stochastic harmonic oscillator, that a naive inference approach based on a measurement of velocities through finite differences does not retrieve the effective parameters of the underlying dynamics , regardless of the time resolution and the length of the sampled trajectories.
Revised:  We first discuss the failure of naive inference approaches based on the estimation of derivatives through finite differences does not retrieve the effective parameters of the underlying dynamics , regardless of the time resolution and the length of the sampled trajectories.
Edit intent: clarity
Original:  We first show, using the analytically tractable stochastic harmonic oscillator, that a naive inference approach based on a measurement of velocities through finite differences does not retrieve the effective parameters of the underlying dynamics , regardless of the time resolution and the length of the sampled trajectories.
Revised:  We first show, using the analytically tractable stochastic harmonic oscillator, that a naive inference approach based on a measurement of velocities through finite differences , regardless of the time resolution and the length of the sampled trajectories.
Edit intent: clarity
Original:  Tagging, or using gates, has been used to enable translation models to distinguish between synthetic and authentic data, improving standard back-translation and also enabling the use of iterative back-translation on language pairs that under-performed using standard back-translation.
Revised:  Tagging, or using gates, has been used to enable translation models to distinguish between synthetic and authentic data, improving standard back-translation and also enabling the use of iterative back-translation on language pairs that underperformed using standard back-translation.
Edit intent: fluency
Original:  This workpresents  pre-training and fine-tuning as a simplified but more effective approach of differentiating between the two data. The approach - tag-less back-translation - trains the model on the synthetic data and fine-tunes it on the authentic data.
Revised:  This workpresents pre-training and fine-tuning, the translation model is shown to be able to learn more efficiently from them during training. Experimental results have shown that the approach outperforms the standard and tagged back-translation - trains the model on the synthetic data and fine-tunes it on the authentic data.
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Original:  The approach - tag-less back-translation - trains the model on the synthetic data and fine-tunes it on the authentic data. Experiments have shown the approach to outperform the baseline and standard back-translation by 4.0 and 0.7 BLEU respectively on low resource English-Vietnamese NMT. While the need for tagging (noising) the dataset has been removed, the technique outperformed tagged back-translation by 0.4 BLEU.
Revised:  The approach - tag-less back-translation approaches on low resource English-Vietnamese NMT. While the need for tagging (noising) the dataset has been removed, the technique outperformed tagged back-translation by 0.4 BLEU.
Edit intent: clarity
Original:  Experiments have shown the approach to outperform the baseline and standard back-translation by 4.0 and 0.7 BLEU respectively on low resource English-Vietnamese NMT. While the need for tagging (noising) the dataset has been removed, the technique outperformed tagged back-translation by 0.4 BLEU. The approach reached the best scores in less training time than the standard and tagged back-translation approaches .
Revised:  Experiments have shown the approach to outperform the baseline and standard back-translation by 4.0 and 0.7 BLEU respectively on low resource English-Vietnamese and English-German neural machine translation .
Edit intent: clarity
Original:  We examine their application to the stylistic task of authorship attribution , and show that they can substantially outperform both classification- and existing similarity-based approaches on datasets with large numbers of authors .
Revised:  We examine their application to the stylistic task of authorship attribution on datasets with large numbers of authors, looking at multiple energy functions and neural network architectures, and show that they can substantially outperform both classification- and existing similarity-based approaches on datasets with large numbers of authors .
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Original:  We examine their application to the stylistic task of authorship attribution , and show that they can substantially outperform both classification- and existing similarity-based approaches on datasets with large numbers of authors .
Revised:  We examine their application to the stylistic task of authorship attribution , and show that they can substantially outperform both classification- and existing similarity-based approaches . We also find an unexpected relationship between choice of energy function and number of authors, in terms of performance .
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Original:  Classification-based approaches work well for small numbers of candidate authors, but only similarity-based methods  are applicable for larger numbers of authors or for authors beyond the training set . While deep learning methodshave been applied to classification-based approaches, applications to similarity-based applications have been limited, and most similarity-based methods only embody static notions of similarity .
Revised:  Classification-based approaches work well for small numbers of candidate authors, but only similarity-based methods are applicable for larger numbers of authors or for authors beyond the training set ; these existing similarity-based applications have been limited, and most similarity-based methods only embody static notions of similarity .
Edit intent: clarity
Original:  While deep learning methodshave been applied to classification-based approaches, applications to similarity-based applications have been limited, and most similarity-based methods only embody static notions of similarity .
Revised:  While deep learning methodshave been applied to classification-based approaches, applications to similarity-based methods have only embodied static notions of similarity. Deep learning methods, which blur the boundaries between classification-based and similarity-based approaches, are promising in terms of ability to learn a notion of similarity, but have previously only been used in a conventional small-closed-class classification setup .
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Original:  We examine their application to the stylistic task of authorship attribution on datasets with large numbers of authors, looking at multiple energy functions and neural network architectures, and show that they can substantially outperform both classification- and existing similarity-based approaches. We also find an unexpected relationship between choice of energy function and number of authors, in terms of performance .
Revised:  We examine their application to the stylistic task of authorship attribution on datasets with large numbers of authors, looking at multiple energy functions and neural network architectures, and show that they can substantially outperform previous approaches .
Edit intent: clarity
Original:  Here we develop a new data reduction method for genome-wide open chromatin data toward cell type classification. Regarding the genome as a string of 1s and 0s based on a systematically optimized set of peaks and calculating the Hamming distance enables us to quantitatively evaluate differences between samples of hematopoietic cells , classify cell types , and infer the origin of leukemic cells , potentially leading to a better understanding of leukemia pathogenesis.
Revised:  Here we develop a new data reduction method for genome-wide open chromatin data using a new data reduction method. This method regards the genome as a string of 1s and 0s based on a systematically optimized set of peaks and calculating the Hamming distance enables us to quantitatively evaluate differences between samples of hematopoietic cells , classify cell types , and infer the origin of leukemic cells , potentially leading to a better understanding of leukemia pathogenesis.
Edit intent: clarity
Original:  Regarding the genome as a string of 1s and 0s based on a systematically optimized set of peaks and calculating the Hamming distance enables us to quantitatively evaluate differences between samples of hematopoietic cells , classify cell types , and infer the origin of leukemic cells , potentially leading to a better understanding of leukemia pathogenesis.
Revised:  Regarding the genome as a string of 1s and 0s based on a set of peaks and calculating the Hamming distance enables us to quantitatively evaluate differences between samples of hematopoietic cells , classify cell types , and infer the origin of leukemic cells , potentially leading to a better understanding of leukemia pathogenesis.
Edit intent: clarity
Original:  Regarding the genome as a string of 1s and 0s based on a systematically optimized set of peaks and calculating the Hamming distance enables us to quantitatively evaluate differences between samples of hematopoietic cells , classify cell types , and infer the origin of leukemic cells , potentially leading to a better understanding of leukemia pathogenesis.
Revised:  Regarding the genome as a string of 1s and 0s based on a systematically optimized set of peaks and calculates the Hamming distances between the strings. This algorithm with the systematically optimized set of peaks enables us to quantitatively evaluate differences between samples of hematopoietic cells , classify cell types , and infer the origin of leukemic cells , potentially leading to a better understanding of leukemia pathogenesis.
Edit intent: meaning-changed
Original:  Regarding the genome as a string of 1s and 0s based on a systematically optimized set of peaks and calculating the Hamming distance enables us to quantitatively evaluate differences between samples of hematopoietic cells , classify cell types , and infer the origin of leukemic cells , potentially leading to a better understanding of leukemia pathogenesis.
Revised:  Regarding the genome as a string of 1s and 0s based on a systematically optimized set of peaks and calculating the Hamming distance enables us to quantitatively evaluate differences between samples of hematopoietic cells and classify cell types , and infer the origin of leukemic cells , potentially leading to a better understanding of leukemia pathogenesis.
Edit intent: fluency
Original:  Regarding the genome as a string of 1s and 0s based on a systematically optimized set of peaks and calculating the Hamming distance enables us to quantitatively evaluate differences between samples of hematopoietic cells , classify cell types , and infer the origin of leukemic cells , potentially leading to a better understanding of leukemia pathogenesis.
Revised:  Regarding the genome as a string of 1s and 0s based on a systematically optimized set of peaks and calculating the Hamming distance enables us to quantitatively evaluate differences between samples of hematopoietic cells , classify cell types , potentially leading to a better understanding of leukemia pathogenesis.
Edit intent: clarity
Original:  From the probability perspective, IC is a linear combination of the components of a directionally projected degenerated random vector. We deduct a solution to its optimization in expectation and obtain the maximum. Their closed-form expressions are given by directional statisticsin a specific condition.
Revised:  Our primary research first clearly defines IC with the high-dimensional directional statistics, discussing its first two moments. We derive the closed-form expressions are given by directional statisticsin a specific condition.
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Original:  Their closed-form expressions are given by directional statisticsin a specific condition. Simulation analysis discloses the influence of market information, such as the number of stocks, on IC.
Revised:  Their closed-form expressions of the directional statistics' covariance matrix and IC's variance in a homoscedastic condition. Also, we solve the optimization of IC's maximum expectation and minimum variance. Simulation intuitively characterizes the standardized directional statistics and IC's p.d.f..
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Original:  The stability and quantification of the rate of convergence of the Mapper to the Reeb space has been studied a lot in recent works ~\mbox{%DIFAUXCMD Brown2019, Carriere2018a, Carriere2018, Munch2016  focusing on the case where a scalar-valued filter is used for the computation of Mapper.
Revised:  The stability and quantification of the rate of convergence of the Mapper to the Reeb space has been studied a lot in recent works BBMW19, CO17, CMO18, MW16 focusing on the case where a scalar-valued filter is used for the computation of Mapper.
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Original:  The stability and quantification of the rate of convergence of the Mapper to the Reeb space has been studied a lot in recent works ~\mbox{%DIFAUXCMD Brown2019, Carriere2018a, Carriere2018, Munch2016  focusing on the case where a scalar-valued filter is used for the computation of Mapper.
Revised:  The stability and quantification of the rate of convergence of the Mapper to the Reeb space has been studied a lot in recent works ~\mbox{%DIFAUXCMD Brown2019, Carriere2018a, Carriere2018, Munch2016 , focusing on the case where a scalar-valued filter is used for the computation of Mapper.
Edit intent: fluency