wanyu/IteraTeR_v2 huggingface dataset:
Original:  However, the discriminative learning pays more attention to classification boundaries which is prone to overfitting to training data and yielding poor generalization on testing data.
Revised:  However, the discriminative learning pays more attention to classification boundaries and is prone to overfitting to training data and yielding poor generalization on testing data.
Edit intent: coherence
Original:  In this paper , we propose a hybrid learning framework, i.e., integrating a joint Bayesian (JB) generative model into a neural discriminative learning framework for SV.
Revised:  In this paper , we propose a hybrid learning framework, i.e., coupling a joint Bayesian (JB) generative model into a neural discriminative learning framework for SV.
Edit intent: clarity
Original:  A Siamese NN is built with dense layers to approximate the mapping functions used in the SV pipeline with the JB model, and the L-LLR score estimated based on the JB model is connected to the distance metric in a pair-wised discriminative learning .
Revised:  A two-branch Siamese neural network is built with dense layers to approximate the mapping functions used in the SV pipeline with the JB model, and the L-LLR score estimated based on the JB model is connected to the distance metric in a pair-wised discriminative learning .
Edit intent: clarity
Original:  A Siamese NN is built with dense layers to approximate the mapping functions used in the SV pipeline with the JB model, and the L-LLR score estimated based on the JB model is connected to the distance metric in a pair-wised discriminative learning .
Revised:  A Siamese NN is built with dense layers to approximate the mapping functions used in the JB model. The LLR score estimation in the JB model is connected to the distance metric in a pair-wised discriminative learning .
Edit intent: coherence
Original:  A Siamese NN is built with dense layers to approximate the mapping functions used in the SV pipeline with the JB model, and the L-LLR score estimated based on the JB model is connected to the distance metric in a pair-wised discriminative learning .
Revised:  A Siamese NN is built with dense layers to approximate the mapping functions used in the SV pipeline with the JB model, and the L-LLR score estimated based on the JB model is formulated according to the distance metric in a pair-wised discriminative learning .
Edit intent: clarity
Original:  A Siamese NN is built with dense layers to approximate the mapping functions used in the SV pipeline with the JB model, and the L-LLR score estimated based on the JB model is connected to the distance metric in a pair-wised discriminative learning .
Revised:  A Siamese NN is built with dense layers to approximate the mapping functions used in the SV pipeline with the JB model, and the L-LLR score estimated based on the JB model is connected to the distance metric in the discriminative learning framework .
Edit intent: clarity
Original:  By initializing the Siamese NN with the parameters learned from the JB model, we further train the model parameters with the pair-wised samples as a binary discrimination task.
Revised:  By initializing the two-branch neural network with the generatively learned model parameters of the JB model, we further train the model parameters with the pair-wised samples as a binary discrimination task.
Edit intent: clarity
Original:  By initializing the Siamese NN with the parameters learned from the JB model, we further train the model parameters with the pair-wised samples as a binary discrimination task.
Revised:  By initializing the Siamese NN with the parameters learned from the JB model, we train the model parameters with the pair-wised samples as a binary discrimination task.
Edit intent: clarity
Original:  By initializing the Siamese NN with the parameters learned from the JB model, we further train the model parameters with the pair-wised samples as a binary discrimination task.
Revised:  By initializing the Siamese NN with the parameters learned from the JB model, we further train the model parameters with the pairwise samples as a binary discrimination task.
Edit intent: fluency
Original:  Moreover,  direct evaluation metric in SV , i.e., minimum empirical Bayes risk , is designed and integrated as an objective function in the discriminative learning.
Revised:  Moreover, a direct evaluation metric in SV , i.e., minimum empirical Bayes risk , is designed and integrated as an objective function in the discriminative learning.
Edit intent: fluency
Original:  Moreover,  direct evaluation metric in SV , i.e., minimum empirical Bayes risk , is designed and integrated as an objective function in the discriminative learning.
Revised:  Moreover, direct evaluation metric in SV based on minimum empirical Bayes risk , is designed and integrated as an objective function in the discriminative learning.
Edit intent: clarity
Original:  Moreover,  direct evaluation metric in SV , i.e., minimum empirical Bayes risk , is designed and integrated as an objective function in the discriminative learning.
Revised:  Moreover, direct evaluation metric in SV , i.e., minimum empirical Bayes risk is designed and integrated as an objective function in the discriminative learning.
Edit intent: fluency
Original:  Moreover,  direct evaluation metric in SV , i.e., minimum empirical Bayes risk , is designed and integrated as an objective function in the discriminative learning.
Revised:  Moreover, direct evaluation metric in SV , i.e., minimum empirical Bayes risk , is designed and integrated as an objective function in discriminative learning.
Edit intent: fluency
Original:  We carried out SV experiments on speakers in the wild (SITW) and Voxcelebcorpora . Experimental results showed that our proposed model improved the performance with a large margin compared with state-of-the-art models for SV.
Revised:  We carried out SV experiments on Speakers in the wild (SITW) and Voxcelebcorpora . Experimental results showed that our proposed model improved the performance with a large margin compared with state-of-the-art models for SV.
Edit intent: fluency
Original:  We carried out SV experiments on speakers in the wild (SITW) and Voxcelebcorpora . Experimental results showed that our proposed model improved the performance with a large margin compared with state-of-the-art models for SV.
Revised:  We carried out SV experiments on speakers in the wild and Voxceleb . Experimental results showed that our proposed model improved the performance with a large margin compared with state-of-the-art models for SV.
Edit intent: clarity
Original:  We carried out SV experiments on speakers in the wild (SITW) and Voxcelebcorpora . Experimental results showed that our proposed model improved the performance with a large margin compared with state-of-the-art models for SV.
Revised:  We carried out SV experiments on speakers in the wild (SITW) and Voxcelebcorpora . Experimental results showed that our proposed model improved the performance with a large margin compared with state-of-art models for SV.
Edit intent: fluency
Original:  Modular training has also revealed novel insights about learning and may have further implications on other important research domains.
Revised:  Modular training has also revealed novel insights about learning and has further implications on other important research domains.
Edit intent: clarity
Original: Current emotion detection classifiers predict discrete emotions.
Revised: Emotion classifiers traditionally predict discrete emotions.
Edit intent: clarity
Original:  However, literature in psychology has documented that compound and ambiguous facial expressions are often evoked by humans.
Revised:  However, emotion expressions are often evoked by humans.
Edit intent: clarity
Original:  We center our study on the Child Affective Facial Expression (CAFE) dataset, a gold standard dataset of pediatric facial expressions which includes 100 human labels per image.
Revised:  We center our study on the Child Affective Facial Expression (CAFE) dataset, a gold standard collection of images depicting pediatric facial expressions which includes 100 human labels per image.
Edit intent: clarity
Original:  We center our study on the Child Affective Facial Expression (CAFE) dataset, a gold standard dataset of pediatric facial expressions which includes 100 human labels per image.
Revised:  We center our study on the Child Affective Facial Expression (CAFE) dataset, a gold standard dataset of pediatric facial expressions along with 100 human labels per image.
Edit intent: clarity
Original:  We then train two versions of a ResNet-152 classifier on CAFE images with two types of labels (1)  traditional one-hot encoding and (2)  vector labels representing the crowd distribution of responses.
Revised:  We then train two versions of a ResNet-152 classifier on CAFE images with two types of labels (1) traditional one-hot encoded labels, and (2) vector labels representing the crowd distribution of responses.
Edit intent: fluency
Original:  We then train two versions of a ResNet-152 classifier on CAFE images with two types of labels (1)  traditional one-hot encoding and (2)  vector labels representing the crowd distribution of responses.
Revised:  We then train two versions of a ResNet-152 classifier on CAFE images with two types of labels (1) traditional one-hot encoding and (2) vector labels representing the distribution of CAFE annotator responses.
Edit intent: clarity
Original:  While the traditional F1-score for the one-hot encoding classifier is much higher (94.33\% vs. 78.68\%)  , the output probability vector of the crowd-trained classifier much more closely resembles the distribution of human labels (t=3.2827, p=0.0014).
Revised:  While the traditional F1-score for the one-hot encoded classifier is much higher (94.33\% vs. 78.68\%) , the output probability vector of the crowd-trained classifier much more closely resembles the distribution of human labels (t=3.2827, p=0.0014).
Edit intent: fluency
Original:  While the traditional F1-score for the one-hot encoding classifier is much higher (94.33\% vs. 78.68\%)  , the output probability vector of the crowd-trained classifier much more closely resembles the distribution of human labels (t=3.2827, p=0.0014).
Revised:  While the traditional F1-score for the one-hot encoding classifier is much higher (94.33\% vs. 78.68\%) , the output probability vector of the crowd-trained classifier more closely resembles the distribution of human labels (t=3.2827, p=0.0014).
Edit intent: fluency
Original:  For many applications of affective computing, reporting an emotion probability distribution that more closely resembles human interpretationcan be more important than traditional machine learning metrics.
Revised:  Reporting an emotion probability distribution that more closely resembles human interpretationcan be more important than traditional machine learning metrics.
Edit intent: clarity
Original: Evolutionary game theory assumes that players replicate a highly scored player's strategy through genetic inheritance  , but in terms of cultural learning , it is often difficult to recognize a strategy just by observing the behaviour.
Revised: Evolutionary game theory assumes that players replicate a highly scored player's strategy through genetic inheritance , it is often difficult to recognize a strategy just by observing the behaviour.
Edit intent: coherence
Original: Evolutionary game theory assumes that players replicate a highly scored player's strategy through genetic inheritance  , but in terms of cultural learning , it is often difficult to recognize a strategy just by observing the behaviour.
Revised: Evolutionary game theory assumes that players replicate a highly scored player's strategy through genetic inheritance , but in terms of cultural learning , it is often difficult to recognize someone's strategy just by observing the behaviour.
Edit intent: clarity
Original:  Based on the observation, the observer has to infer the resident strategy in a Bayesian way and adjust his or her own strategy accordingly.
Revised:  Based on the observation, the observer has to infer the resident strategy in a Bayesian way and chooses his or her own strategy accordingly.
Edit intent: clarity
Original:  Two different scalings are considered , one results in an equilibrium diffusion limit system, while the other results in a non-equilibrium system.
Revised:  Two different scalings are considered . One results in an equilibrium diffusion limit system, while the other results in a non-equilibrium system.
Edit intent: fluency
Original:  The performances of the semi-implicit method are presented, for both optically thin and thick regions, as well as for the radiative shock problem . Comparisons with the semi-analytic solution are given to verify the accuracy and asymptotic properties of the method .
Revised:  The performances of the semi-implicit method are presented, for both optically thin and thick regions, as well as for the radiative shock problem .
Edit intent: clarity
Original:  In this work we focus on interaction structures represented as simplicial complexes, in which a group interaction is identified with a face.
Revised:  In this work , focusing on interaction structures represented as simplicial complexes, in which a group interaction is identified with a face.
Edit intent: fluency
Original:  We present a microscopic discrete-time  model of complex contagion for which a susceptible-infected-susceptible dynamics is considered .
Revised:  We present a microscopic discrete-time model of complex contagion for a susceptible-infected-susceptible dynamics is considered .
Edit intent: fluency
Original:  We present a microscopic discrete-time  model of complex contagion for which a susceptible-infected-susceptible dynamics is considered .
Revised:  We present a microscopic discrete-time model of complex contagion for which a susceptible-infected-susceptible dynamics .
Edit intent: clarity
Original:  Introducing a particular edge clique cover and a heuristic to find it, the model accounts for the high-order state correlations among the members of the substructures (cliques/simplices).
Revised:  Introducing a particular edge clique cover and a heuristic to find it, the model accounts for the higher-order dynamical correlations among the members of the substructures (cliques/simplices).
Edit intent: clarity
Original:  The mathematical tractability of the model allows for the analytical computation of the epidemic threshold, thus extending to structured populations some primary features of the critical properties of mean-field models.
Revised:  The analytical computation of the epidemic threshold, thus extending to structured populations some primary features of the critical properties of mean-field models.
Edit intent: clarity
Original: We consider the problem of service placement at the network edge, in which a decision maker has to choose between N services to host at the edge to satisfy the demands of end users .
Revised: We consider the problem of service placement at the network edge, in which a decision maker has to choose between N services to host at the edge to satisfy the demands of customers .
Edit intent: clarity
Original:  Our goal is to design adaptive algorithms to minimize the average service delivery latency for users .
Revised:  Our goal is to design adaptive algorithms to minimize the average service delivery latency for customers .
Edit intent: clarity
Original:  We pose the problem as a Markov decision process (MDP) in which the system state is given by describing, for each service, the number of users that are currently waiting at the edge to obtain the service.
Revised:  We pose the problem as a Markov decision process (MDP) in which the system state is given by describing, for each service, the number of customers that are currently waiting at the edge to obtain the service.
Edit intent: clarity
Original:  To overcome this challenge, we show that the optimal policy for a single-service MDP has an appealing threshold structure, and derive explicitly the Whittle indices for each service as a function of the number of requests from end users based on the theory of Whittle index policy.
Revised:  To overcome this challenge, we show that the optimal policy for a single-service MDP has an appealing threshold structure, and derive explicitly the Whittle indices for each service as a function of the number of requests from customers based on the theory of Whittle index policy.
Edit intent: clarity
Original: Click-through rate (CTR) prediction, which aims to predict the probability that whether of a user will click on an item, is an essential task for many online applications.
Revised: Click-through rate (CTR) prediction, whose aim is to predict the probability that whether of a user will click on an item, is an essential task for many online applications.
Edit intent: clarity
Original: Click-through rate (CTR) prediction, which aims to predict the probability that whether of a user will click on an item, is an essential task for many online applications.
Revised: Click-through rate (CTR) prediction, which aims to predict the probability of whether a user will click on an item, is an essential task for many online applications.
Edit intent: fluency
Original:  Due to the nature of data sparsity and high dimensionality in CTR prediction, a key to making effective prediction is to model high-order feature interactions among feature fields.
Revised:  Due to the nature of data sparsity and high dimensionality in CTR prediction, a key to making effective prediction is to model high-order feature interaction among feature fields.
Edit intent: fluency
Original:  To explicitly model high-order feature interactions , an efficient way is to stack multihead self-attentive neural networks , which has achieved promising performance.
Revised:  To explicitly model high-order feature interaction , an efficient way is to stack multihead self-attentive neural networks , which has achieved promising performance.
Edit intent: fluency
Original:  However, one problem of the vanilla self-attentive network is that two terms, a whitened pairwise interactionterm and a unary term, are coupled in the computation of  the self-attention score, where the pairwise term contributes to learning the importance score for each feature interaction, while the unary term models the  impact of one feature on all other features .
Revised:  However, one problem of the vanilla self-attentive network is that two terms, a whitened pairwise interactionterm and a unary term, are coupled in the computation of the unary term models the impact of one feature on all other features .
Edit intent: coherence
Original:  However, one problem of the vanilla self-attentive network is that two terms, a whitened pairwise interactionterm and a unary term, are coupled in the computation of  the self-attention score, where the pairwise term contributes to learning the importance score for each feature interaction, while the unary term models the  impact of one feature on all other features .
Revised:  However, one problem of the vanilla self-attentive network is that two terms, a whitened pairwise interactionterm and a unary term, are coupled in the computation of the self-attention score, where the pairwise term contributes to learning the importance score for each feature interaction, while the unary term models the general impact of one feature on all other features .
Edit intent: clarity
Original: Click-through rate (CTR) prediction, whose aim is to predict the probability of whether a user will click on an item, is an essential task for many online applications.
Revised: Click-Through Rate (CTR) prediction, whose aim is to predict the probability of whether a user will click on an item, is an essential task for many online applications.
Edit intent: fluency
Original:  Due to the nature of data sparsity and high dimensionality in CTR prediction, a key to making effective prediction is to model high-order feature interaction among feature fields.
Revised:  Due to the nature of data sparsity and high dimensionality of CTR prediction, a key to making effective prediction is to model high-order feature interaction among feature fields.
Edit intent: clarity
Original:  To better model complex feature interaction, in this paper we propose a novel DisentanglEd Self-atTentIve NEtwork (DESTINE) framework for CTR prediction that explicitly decouples the computation of unary  importance from pairwise interaction.
Revised:  To better model complex feature interaction, in this paper we propose a novel DisentanglEd Self-atTentIve NEtwork (DESTINE) framework for CTR prediction that explicitly decouples the computation of unary feature importance from pairwise interaction.
Edit intent: fluency
Original:  Specifically, the unary term models the general impact of one feature on all other features, whereas the whitened pairwise interaction term contributes to learning the pure importance score for each feature interaction .
Revised:  Specifically, the unary term models the general importance of one feature on all other features, whereas the whitened pairwise interaction term contributes to learning the pure importance score for each feature interaction .
Edit intent: fluency
Original:  Specifically, the unary term models the general impact of one feature on all other features, whereas the whitened pairwise interaction term contributes to learning the pure importance score for each feature interaction .
Revised:  Specifically, the unary term models the general impact of one feature on all other features, whereas the pairwise interaction term contributes to learning the pure importance score for each feature interaction .
Edit intent: clarity
Original:  Specifically, the unary term models the general impact of one feature on all other features, whereas the whitened pairwise interaction term contributes to learning the pure importance score for each feature interaction .
Revised:  Specifically, the unary term models the general impact of one feature on all other features, whereas the whitened pairwise interaction term contributes to learning the pure impact for each feature interaction .
Edit intent: clarity
Original:  Specifically, the unary term models the general impact of one feature on all other features, whereas the whitened pairwise interaction term contributes to learning the pure importance score for each feature interaction .
Revised:  Specifically, the unary term models the general impact of one feature on all other features, whereas the whitened pairwise interaction term contributes to learning the pure importance score for each feature pair .
Edit intent: clarity
Original:  We conduct extensive experiments framework using two real-world benchmark datasets.
Revised:  We conduct extensive experiments using two real-world benchmark datasets.
Edit intent: clarity
Original:  The results show that DESTINE not only maintains computational efficiency but obtains performance improvements over state-of-the-art baselines.
Revised:  The results show that DESTINE not only maintains computational efficiency but achieves consistent improvements over state-of-the-art baselines.
Edit intent: clarity
Original: Continuous-time Markov chains (CTMCs) are widely used in many applications, including in modeling nucleic acid kinetics (non-equilibrium dynamics) .
Revised: For predicting the kinetics of nucleic acid reactions, continuous-time Markov chains (CTMCs) are widely used in many applications, including in modeling nucleic acid kinetics (non-equilibrium dynamics) .
Edit intent: clarity
Original:  A typical issue in CTMCs is that the number of states could be large, making mean first passage time (MFPT ) estimation challenging, particularly for events that happen on a long time scale (rare events).
Revised:  A typical issue in CTMCs is that the number of states could be large, making MFPT estimation challenging, particularly for events that happen on a long time scale (rare events).
Edit intent: clarity