wanyu/IteraTeR_full_sent huggingface dataset:
Original: We introduce sentenceMIM, a probabilistic auto-encoder for language modelling , trained with Mutual Information Machine (MIM) learning \ie . Previous attempts to learn variational auto-encoders for language data have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the occurrence of posterior collapsewith VAEs.
Revised: We introduce sentenceMIM, a probabilistic auto-encoder for language modelling , trained with Mutual Information Machine (MIM) learning \ie, similar to VAE) . Previous attempts to learn variational auto-encoders for language data have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the occurrence of posterior collapsewith VAEs.
Edit intent: meaning-changed
Original: We introduce sentenceMIM, a probabilistic auto-encoder for language modelling , trained with Mutual Information Machine (MIM) learning \ie . Previous attempts to learn variational auto-encoders for language data have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the occurrence of posterior collapsewith VAEs.
Revised: We introduce sentenceMIM, a probabilistic auto-encoder for language modelling , trained with Mutual Information Machine (MIM) learning \ie . Previous attempts to learn VAEs for language data have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the occurrence of posterior collapsewith VAEs.
Edit intent: clarity
Original: We introduce sentenceMIM, a probabilistic auto-encoder for language modelling , trained with Mutual Information Machine (MIM) learning \ie . Previous attempts to learn variational auto-encoders for language data have had mixed success, with empirical performance well below state-of-the-art auto-regressive models, a key barrier being the occurrence of posterior collapsewith VAEs. The recently proposed MIM framework encourages high mutual information between observations and latent variables, and is more robust against posterior collapse.
Revised: We introduce sentenceMIM, a probabilistic auto-encoder for language modelling , trained with Mutual Information Machine (MIM) learning \ie . Previous attempts to learn variational auto-encoders for language data faced challenges due to posterior collapse. MIM learning encourages high mutual information between observations and latent variables, and is more robust against posterior collapse.
Edit intent: clarity
Original:  This paper formulates a MIM model for text data, along with a corresponding learning algorithm. We demonstrate excellent perplexity (PPL) results on several datasets, and show that the framework learns a rich latent space, allowing for interpolation between sentences of different lengths with a fixed-dimensional latent representation.
Revised:  As such, it learns informative representations whose dimension can be an order of magnitude higher than existing language VAEs. Importantly, the SentenceMIM loss has no hyper-parameters, simplifying optimization. We compare sentenceMIM with VAE, and AE on multiple datasets. SentenceMIM yields excellent reconstruction, comparable to AEs, with a rich structured latent space, allowing for interpolation between sentences of different lengths with a fixed-dimensional latent representation.
Edit intent: meaning-changed
Original:  We demonstrate excellent perplexity (PPL) results on several datasets, and show that the framework learns a rich latent space, allowing for interpolation between sentences of different lengths with a fixed-dimensional latent representation.
Revised:  We demonstrate excellent perplexity (PPL) results on several datasets, and show that the framework learns a rich latent space, comparable to VAEs. The structured latent representation is demonstrated with interpolation between sentences of different lengths with a fixed-dimensional latent representation.
Edit intent: meaning-changed
Original:  We demonstrate excellent perplexity (PPL) results on several datasets, and show that the framework learns a rich latent space, allowing for interpolation between sentences of different lengths with a fixed-dimensional latent representation. We also demonstrate the versatility of sentenceMIM by utilizing a trained model for question-answering , a transfer learningtask , without fine-tuning .
Revised:  We demonstrate excellent perplexity (PPL) results on several datasets, and show that the framework learns a rich latent space, allowing for interpolation between sentences of different lengths . We demonstrate the versatility of sentenceMIM by utilizing a trained model for question-answering , a transfer learningtask , without fine-tuning .
Edit intent: clarity
Original:  We also demonstrate the versatility of sentenceMIM by utilizing a trained model for question-answering , a transfer learningtask , without fine-tuning . To the best of our knowledge, this is the first latent variable model (LVM) for text modelling that achieves competitive performance with non-LVM models .
Revised:  We also demonstrate the versatility of sentenceMIM by utilizing a trained model for question-answering , a transfer learningtask , without fine-tuning , outperforming VAE and AE with similar architectures .
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Original:  Thus, we analyze graph properties and the relationships between the properties using Erdos-R\'enyi graphs, which model well the underlying graph space . Specifically, we focus on thirteen properties of interest in graph theory and graph mining.
Revised:  We experimentally show that graphs generated by the Erdos-R\'enyi graphs, which model well the underlying graph space . Specifically, we focus on thirteen properties of interest in graph theory and graph mining.
Edit intent: clarity
Original:  Thus, we analyze graph properties and the relationships between the properties using Erdos-R\'enyi graphs, which model well the underlying graph space . Specifically, we focus on thirteen properties of interest in graph theory and graph mining.
Revised:  Thus, we analyze graph properties and the relationships between the properties using Erdos-R\'enyi graph generator with p = 1/2 models well the underlying graph space . Specifically, we focus on thirteen properties of interest in graph theory and graph mining.
Edit intent: meaning-changed
Original:  Thus, we analyze graph properties and the relationships between the properties using Erdos-R\'enyi graphs, which model well the underlying graph space . Specifically, we focus on thirteen properties of interest in graph theory and graph mining. We numerically study the behavior of these properties, as the number of vertices increase, for the Erd\H{os-R\'enyi model and compare the results with known theoretical bounds, where applicable.
Revised:  Thus, we analyze graph properties and the relationships between the properties using Erdos-R\'enyi graphs, which model well the underlying graph space of all labeled graphs with fixed number of vertices increase, for the Erd\H{os-R\'enyi model and compare the results with known theoretical bounds, where applicable.
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Original:  We also }  use linear and non-linear models to predict a specific property based on the other properties .
Revised:  We also } use linear and non-linear models to predict a specific property based on the others and find the most predictive subset. We also develop a model to classify graphs obtained by different graph generators and again identify the most predictive subset of properties. For both experiments the results show that pairs and triples of properties have high predictive power .
Edit intent: meaning-changed
Original:  I show that the risk of externalization increases the likelihood of a peaceful outcome, but only if the government is sufficiently powerful relative to the rebels, and if the risk of externalization is sufficiently high  .
Revised:  I show that the risk of externalization increases the likelihood of a peaceful outcome, but only if the government is sufficiently powerful relative to the rebels, and if the risk of externalization is sufficiently high , and the foreign actor who can intervene in the domestic conflict is sufficiently uninterested in material costs and benefits .
Edit intent: meaning-changed
Original:  To quantify possible group differences in discrete (average and range of motion (ROM)) and continuous LLAs , we calculated effect sizes (Hedges' g) . Patients with NSCLBP showed reduced average LLAs during standing, walking and running (g=-0.44 to -0.68) and smaller LLA-ROM during walking (g=0.60) . Analyses of continuous data indicated the largest group differences occurring around 25\% (g=-0.80) and 70\% (g=-0.75) of the walking and 25\% (g=-0.57) and 75\% (g=-0.55) of the running cycle.
Revised:  Possible group differences in discrete (average and range of motion (ROM)) and continuous LLAs , we calculated effect sizes (Hedges' g) . Patients with NSCLBP showed reduced average LLAs during standing, walking and running (g=-0.44 to -0.68) and smaller LLA-ROM during walking (g=0.60) . Analyses of continuous data indicated the largest group differences occurring around 25\% (g=-0.80) and 70\% (g=-0.75) of the walking and 25\% (g=-0.57) and 75\% (g=-0.55) of the running cycle.
Edit intent: clarity
Original:  To quantify possible group differences in discrete (average and range of motion (ROM)) and continuous LLAs , we calculated effect sizes (Hedges' g) . Patients with NSCLBP showed reduced average LLAs during standing, walking and running (g=-0.44 to -0.68) and smaller LLA-ROM during walking (g=0.60) . Analyses of continuous data indicated the largest group differences occurring around 25\% (g=-0.80) and 70\% (g=-0.75) of the walking and 25\% (g=-0.57) and 75\% (g=-0.55) of the running cycle.
Revised:  To quantify possible group differences in discrete (average and range of motion (ROM)) and continuous LLAs were analyzed descriptively using mean differences with confidence intervals ranging from 95\% to 75\% . Patients with NSCLBP showed reduced average LLAs during standing, walking and running (g=-0.44 to -0.68) and smaller LLA-ROM during walking (g=0.60) . Analyses of continuous data indicated the largest group differences occurring around 25\% (g=-0.80) and 70\% (g=-0.75) of the walking and 25\% (g=-0.57) and 75\% (g=-0.55) of the running cycle.
Edit intent: clarity
Original:  To quantify possible group differences in discrete (average and range of motion (ROM)) and continuous LLAs , we calculated effect sizes (Hedges' g) . Patients with NSCLBP showed reduced average LLAs during standing, walking and running (g=-0.44 to -0.68) and smaller LLA-ROM during walking (g=0.60) . Analyses of continuous data indicated the largest group differences occurring around 25\% (g=-0.80) and 70\% (g=-0.75) of the walking and 25\% (g=-0.57) and 75\% (g=-0.55) of the running cycle.
Revised:  To quantify possible group differences in discrete (average and range of motion (ROM)) and continuous LLAs , we calculated effect sizes (Hedges' g) . Patients with NSCLBP indicated reduced average LLAs during standing, walking and running (g=-0.44 to -0.68) and smaller LLA-ROM during walking (g=0.60) . Analyses of continuous data indicated the largest group differences occurring around 25\% (g=-0.80) and 70\% (g=-0.75) of the walking and 25\% (g=-0.57) and 75\% (g=-0.55) of the running cycle.
Edit intent: clarity
Original:  To quantify possible group differences in discrete (average and range of motion (ROM)) and continuous LLAs , we calculated effect sizes (Hedges' g) . Patients with NSCLBP showed reduced average LLAs during standing, walking and running (g=-0.44 to -0.68) and smaller LLA-ROM during walking (g=0.60) . Analyses of continuous data indicated the largest group differences occurring around 25\% (g=-0.80) and 70\% (g=-0.75) of the walking and 25\% (g=-0.57) and 75\% (g=-0.55) of the running cycle.
Revised:  To quantify possible group differences in discrete (average and range of motion (ROM)) and continuous LLAs , we calculated effect sizes (Hedges' g) . Patients with NSCLBP showed reduced average LLAs during standing, walking and running and a tendency for lower LLA-ROM during walking (g=0.60) . Analyses of continuous data indicated the largest group differences occurring around 25\% (g=-0.80) and 70\% (g=-0.75) of the walking and 25\% (g=-0.57) and 75\% (g=-0.55) of the running cycle.
Edit intent: clarity
Original:  To quantify possible group differences in discrete (average and range of motion (ROM)) and continuous LLAs , we calculated effect sizes (Hedges' g) . Patients with NSCLBP showed reduced average LLAs during standing, walking and running (g=-0.44 to -0.68) and smaller LLA-ROM during walking (g=0.60) . Analyses of continuous data indicated the largest group differences occurring around 25\% (g=-0.80) and 70\% (g=-0.75) of the walking and 25\% (g=-0.57) and 75\% (g=-0.55) of the running cycle.
Revised:  To quantify possible group differences in discrete (average and range of motion (ROM)) and continuous LLAs , we calculated effect sizes (Hedges' g) . Patients with NSCLBP showed reduced average LLAs during standing, walking and running (g=-0.44 to -0.68) and smaller LLA-ROM during walking . Analyses of continuous data indicated the largest group differences occurring around 25\% (g=-0.80) and 70\% (g=-0.75) of the walking and 25\% (g=-0.57) and 75\% (g=-0.55) of the running cycle.
Edit intent: meaning-changed
Original:  To quantify possible group differences in discrete (average and range of motion (ROM)) and continuous LLAs , we calculated effect sizes (Hedges' g) . Patients with NSCLBP showed reduced average LLAs during standing, walking and running (g=-0.44 to -0.68) and smaller LLA-ROM during walking (g=0.60) . Analyses of continuous data indicated the largest group differences occurring around 25\% (g=-0.80) and 70\% (g=-0.75) of the walking and 25\% (g=-0.57) and 75\% (g=-0.55) of the running cycle.
Revised:  To quantify possible group differences in discrete (average and range of motion (ROM)) and continuous LLAs , we calculated effect sizes (Hedges' g) . Patients with NSCLBP showed reduced average LLAs during standing, walking and running (g=-0.44 to -0.68) and smaller LLA-ROM during walking (g=0.60) . Analyses of continuous data indicated the largest group differences occurring around 25\% and 70\% (g=-0.75) of the walking and 25\% (g=-0.57) and 75\% (g=-0.55) of the running cycle.
Edit intent: meaning-changed
Original:  To quantify possible group differences in discrete (average and range of motion (ROM)) and continuous LLAs , we calculated effect sizes (Hedges' g) . Patients with NSCLBP showed reduced average LLAs during standing, walking and running (g=-0.44 to -0.68) and smaller LLA-ROM during walking (g=0.60) . Analyses of continuous data indicated the largest group differences occurring around 25\% (g=-0.80) and 70\% (g=-0.75) of the walking and 25\% (g=-0.57) and 75\% (g=-0.55) of the running cycle.
Revised:  To quantify possible group differences in discrete (average and range of motion (ROM)) and continuous LLAs , we calculated effect sizes (Hedges' g) . Patients with NSCLBP showed reduced average LLAs during standing, walking and running (g=-0.44 to -0.68) and smaller LLA-ROM during walking (g=0.60) . Analyses of continuous data indicated the largest group differences occurring around 25\% (g=-0.80) and 70\% of the walking and 25\% (g=-0.57) and 75\% (g=-0.55) of the running cycle.
Edit intent: clarity
Original:  To quantify possible group differences in discrete (average and range of motion (ROM)) and continuous LLAs , we calculated effect sizes (Hedges' g) . Patients with NSCLBP showed reduced average LLAs during standing, walking and running (g=-0.44 to -0.68) and smaller LLA-ROM during walking (g=0.60) . Analyses of continuous data indicated the largest group differences occurring around 25\% (g=-0.80) and 70\% (g=-0.75) of the walking and 25\% (g=-0.57) and 75\% (g=-0.55) of the running cycle.
Revised:  To quantify possible group differences in discrete (average and range of motion (ROM)) and continuous LLAs , we calculated effect sizes (Hedges' g) . Patients with NSCLBP showed reduced average LLAs during standing, walking and running (g=-0.44 to -0.68) and smaller LLA-ROM during walking (g=0.60) . Analyses of continuous data indicated the largest group differences occurring around 25\% (g=-0.80) and 70\% (g=-0.75) of the walking and 25\% and 75\% (g=-0.55) of the running cycle.
Edit intent: meaning-changed
Original:  To quantify possible group differences in discrete (average and range of motion (ROM)) and continuous LLAs , we calculated effect sizes (Hedges' g) . Patients with NSCLBP showed reduced average LLAs during standing, walking and running (g=-0.44 to -0.68) and smaller LLA-ROM during walking (g=0.60) . Analyses of continuous data indicated the largest group differences occurring around 25\% (g=-0.80) and 70\% (g=-0.75) of the walking and 25\% (g=-0.57) and 75\% (g=-0.55) of the running cycle.
Revised:  To quantify possible group differences in discrete (average and range of motion (ROM)) and continuous LLAs , we calculated effect sizes (Hedges' g) . Patients with NSCLBP showed reduced average LLAs during standing, walking and running (g=-0.44 to -0.68) and smaller LLA-ROM during walking (g=0.60) . Analyses of continuous data indicated the largest group differences occurring around 25\% (g=-0.80) and 70\% (g=-0.75) of the walking and 25\% (g=-0.57) and 75\% of the running cycle.
Edit intent: clarity
Original:  We complement the latter result by showing that, for any fixed p\geq 3 the problem becomes NP-complete when restricted to split (S_3,\ldots,S_p)-free graphs, where S_q denotes the q-sun graph.
Revised:  We complement the polynomial-time solvability results for split strongly chordal graphs by showing that, for any fixed p\geq 3 the problem becomes NP-complete when restricted to split (S_3,\ldots,S_p)-free graphs, where S_q denotes the q-sun graph.
Edit intent: meaning-changed
Original:  We complement the latter result by showing that, for any fixed p\geq 3 the problem becomes NP-complete when restricted to split (S_3,\ldots,S_p)-free graphs, where S_q denotes the q-sun graph.
Revised:  We complement the latter result by showing that, for any fixed p\geq 3 both variants of the problem become NP-complete when restricted to split (S_3,\ldots,S_p)-free graphs, where S_q denotes the q-sun graph.
Edit intent: meaning-changed
Original:  To demonstrate the usability of DeFINE from both experimentalists' and participants' perspectives, a case study was conducted in which participants navigated to a hidden goal location with feedback that differentially weighted speed and accuracy of their responses.
Revised:  To show the usability of DeFINE from both experimentalists' and participants' perspectives, a case study was conducted in which participants navigated to a hidden goal location with feedback that differentially weighted speed and accuracy of their responses.
Edit intent: fluency
Original:  To demonstrate the usability of DeFINE from both experimentalists' and participants' perspectives, a case study was conducted in which participants navigated to a hidden goal location with feedback that differentially weighted speed and accuracy of their responses.
Revised:  To demonstrate the usability of DeFINE from both experimentalists' and participants' perspectives, a demonstration was made in which participants navigated to a hidden goal location with feedback that differentially weighted speed and accuracy of their responses.
Edit intent: clarity
Original:  We have compiled a shareable dataset containing detailed information about 461 bots we found (all of whom have more than 1000 commits) and 14,678,222 commits they created.
Revised:  We have compiled a shareable dataset containing detailed information about 461 bots we found (all of whom have more than 1000 commits) and 13,762,430 commits they created.
Edit intent: meaning-changed
Original:  DML based methods leverage flexible prediction methods to control for confounding in the estimation of (i) standard average effects, (ii) different forms of heterogeneous effects, and (iii) optimal treatment assignment rules.
Revised:  DML based methods leverage flexible prediction models to adjust for confounding variables in the estimation of (i) standard average effects, (ii) different forms of heterogeneous effects, and (iii) optimal treatment assignment rules.
Edit intent: clarity
Original:  An evaluation of multiple programs of the Swiss Active Labor Market Policy shows how DML based methods enable a comprehensive policy analysis. However, we find evidence that estimates of individualized heterogeneous effects can become unstable .
Revised:  An evaluation of multiple programs of the Swiss Active Labor Market Policy shows how DML based methods enable a comprehensive program evaluation. Motivated by extreme individualized treatment effect estimates of the DR-learner method, we propose the normalized DR-learner to address this issue .
Edit intent: meaning-changed
Original:  Motivated by extreme individualized treatment effect estimates of the DR-learner method, we propose the normalized DR-learner  to address this issue  .
Revised:  Motivated by extreme individualized treatment effect estimates of the DR-learner method, we propose the normalized DR-learner to address this issue . The NDR-learner acknowledges that individualized effect estimates can be stabilized by an individualized normalization of inverse probability weights .
Edit intent: meaning-changed
Original:  This Gray code can be generated in O(log \Oh ^2 n) amortized time per word, while the best generation algorithm hitherto has O \Oh (n) running time per word.
Revised:  This Gray code can be generated in O(log \Oh(\log ^2 n) amortized time per word, while the best generation algorithm hitherto has O \Oh (n) running time per word.
Edit intent: meaning-changed
Original: The interest in spatial networks where vertices are embedded in a one-dimensional space is growing. Remarkable examples of these networks are syntactic dependency trees and RNA structures. In this setup, the vertices of the network are arranged linearly and then edges may cross when drawn above the sequence of vertices. Recently, two aspects of the distribution of the number of crossings in uniformly randomlinear arrangements have been investigated: the expectation and the variance.
Revised: The crossing number, i.e. the minimum number of edge crossings arising when drawing a graph on a certain surface, is a very important problem of graph theory. The opposite problem, i.e. the maximum crossing number, is receiving growing attention. Here we consider a complementary problem of the distribution of the number of edge crossings, namely the variance of the number of crossings in uniformly randomlinear arrangements have been investigated: the expectation and the variance.
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Original:  Recently, two aspects of the distribution of the number of crossings in uniformly randomlinear arrangements have been investigated: the expectation and the variance. While the computation of the expectation is straightforward, that of the variance is not. Here we present fast algorithms to calculate that variance in arbitrary graphs and forests.
Revised:  Recently, two aspects of the distribution of the number of crossings , when embedding the vertices of an arbitrary graph in some space at random. In his pioneering research, Moon derived that variance on random linear arrangements of complete unipartite and bipartite graphs. Given the need of efficient algorithms to support this sort of research and given also the growing interest of the number of edge crossings in spatial networks, networks where vertices are embedded in some space, here we derive algorithms to calculate that variance in arbitrary graphs and forests.
Edit intent: meaning-changed