wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The resulting model, which we name FNet, scales very efficiently to long inputs , matching the accuracy of the most accurate "efficient" Transformers on the Long Range Arena benchmark, but training and running faster across all sequence lengths on GPUs and relatively shorter sequence lengths on TPUs  . Finally, FNet has a light memory footprint and is particularly efficient at smaller model sizes: for a fixed speed and accuracy budget, small FNet models outperform Transformer counterparts.
Revised:  The resulting model, which we name FNet, scales very efficiently to long inputs , matching the accuracy of the most accurate "efficient" Transformers on the Long Range Arena benchmark, but training and running faster across all sequence lengths on GPUs (and across relatively shorter lengths on TPUs . Finally, FNet has a light memory footprint and is particularly efficient at smaller model sizes: for a fixed speed and accuracy budget, small FNet models outperform Transformer counterparts.
Edit intent: fluency
Original:  The resulting model, which we name FNet, scales very efficiently to long inputs , matching the accuracy of the most accurate "efficient" Transformers on the Long Range Arena benchmark, but training and running faster across all sequence lengths on GPUs and relatively shorter sequence lengths on TPUs  . Finally, FNet has a light memory footprint and is particularly efficient at smaller model sizes: for a fixed speed and accuracy budget, small FNet models outperform Transformer counterparts.
Revised:  The resulting model, which we name FNet, scales very efficiently to long inputs , matching the accuracy of the most accurate "efficient" Transformers on the Long Range Arena benchmark, but training and running faster across all sequence lengths on GPUs and relatively shorter sequence lengths on TPUs ) . Finally, FNet has a light memory footprint and is particularly efficient at smaller model sizes: for a fixed speed and accuracy budget, small FNet models outperform Transformer counterparts.
Edit intent: fluency
Original: Building on the breakthrough of reinforcement learning, this paper introduces a unified framework of model-free reinforcement learning, CASA-B , Critic AS an Actor with Bandits Vote Algorithm. CASA-B is an actor-critic framework that estimates state-value, state-action-value and policy .
Revised: Building on the breakthrough of reinforcement learning, this paper introduces a unified framework of model-free reinforcement learning, CASA-B , Critic AS an Actor . CASA follows the actor-critic framework that estimates state-value, state-action-value and policy .
Edit intent: clarity
Original:  The policy evaluation is equivalent to a compensational policy improvement, which alleviates the function approximation error, and is also equivalent to an entropy-regularized policy improvement, which prevents the policy from collapsing to a suboptimal solution.
Revised:  The policy evaluation is equivalent to a compensational policy improvement, which alleviates the function approximation error, and is also equivalent to an entropy-regularized policy improvement, which prevents the policy from being trapped into a suboptimal solution.
Edit intent: clarity
Original:  Building on this design, we find the entropy of the behavior policies' and the target policy's are disentangled. Based on this observation, we propose a progressive closed-form entropy control mechanism, which explicitly controls the behavior policies' entropy to arbitrary range .
Revised:  Building on this design, an expectation-correct Doubly Robust Trace is introduced to learn state-value and state-action-value, and the convergence is guaranteed .
Edit intent: meaning-changed
Original:  Our experiments show that CASAB is super sample efficient and achieves State-Of-The-Art on Arcade Learning Environment . Our mean Human Normalized Score is 6456.63\% and our median Human Normalized Score is 477.17\%, under 200M training scale .
Revised:  Our experiments show that CASAB is super sample efficient and achieves State-Of-The-Art on Arcade Learning Environment .
Edit intent: coherence
Original:  Due to the constant negative curvature, the hyperbolic space resembles tree metrics and captures the tree-like properties of hierarchical graphs naturally, which enables the hyperbolic embeddings to improve over traditional Euclidean models.
Revised:  Due to the constant negative curvature, the hyperbolic space resembles tree metrics and captures the tree-like properties naturally, which enables the hyperbolic embeddings to improve over traditional Euclidean models.
Edit intent: clarity
Original:  However, most graph data , even the data with hierarchical structures are not trees and they usually do not ubiquitously match the constant curvature property of the hyperbolic space.
Revised:  However, many real-world hierarchically structured data such as taxonomies and multitree networks have varying local structures and they are not trees and they usually do not ubiquitously match the constant curvature property of the hyperbolic space.
Edit intent: meaning-changed
Original:  Furthermore, by attaining a stable factorization of the system matrix as a forward-backward pass, corresponding to the Thomas algorithm for self-diffusion processes, we show that the use of implicit cross-diffusion can be competitive in terms of execution time, widening the range of viable cross-diffusion coefficients for on-the-fly applications.
Revised:  Furthermore, by attaining a stable factorization of the system matrix as a forward-backward pass, corresponding to the Thomas algorithm for self-diffusion processes, we show that the use of implicit cross-diffusion can be competitive in terms of execution time, widening the range of viable cross-diffusion coefficients for on-the-fly applications.
Edit intent: fluency
Original:  The privacy protection of the broad Data-InformationKnowledge-Wisdom (DIKW) landscape, the next generation of URLanization, has not been in the limelight. Next , we will explore DIKW architecture through the applications of  popular swarm intelligence and differential privacy.
Revised:  The privacy protection of the broad Data-InformationKnowledge-Wisdom (DIKW) landscape, the next generation of URLanization, has taken a secondary role. In this paper , we will explore DIKW architecture through the applications of popular swarm intelligence and differential privacy.
Edit intent: clarity
Original:  Swarm Intelligence could effectively optimize and reduce the number of items in DIKW used in differential privacy, this way accelerating both the effectiveness and the efficiency of differential privacy for crossing multiple modals of conceptual DIKW.
Revised:  Swarm Intelligence can effectively optimize and reduce the number of items in DIKW used in differential privacy, this way accelerating both the effectiveness and the efficiency of differential privacy for crossing multiple modals of conceptual DIKW.
Edit intent: fluency
Original:  Swarm Intelligence could effectively optimize and reduce the number of items in DIKW used in differential privacy, this way accelerating both the effectiveness and the efficiency of differential privacy for crossing multiple modals of conceptual DIKW.
Revised:  Swarm Intelligence could effectively optimize and reduce the number of items in DIKW used in differential privacy, thus accelerating both the effectiveness and the efficiency of differential privacy for crossing multiple modals of conceptual DIKW.
Edit intent: coherence
Original:  Minimum Excess Risk (MER) in Bayesian learning is defined as the difference between the minimum expected loss achievable when learning from data and the minimum expected loss that could be achieved if the underlying parameter W was observed.
Revised: In parametric Bayesian learning, a prior is assumed on the parameter W which determines the distribution of samples. In this setting, Minimum Excess Risk (MER) in Bayesian learning is defined as the difference between the minimum expected loss achievable when learning from data and the minimum expected loss that could be achieved if the underlying parameter W was observed.
Edit intent: meaning-changed
Original:  Minimum Excess Risk (MER) in Bayesian learning is defined as the difference between the minimum expected loss achievable when learning from data and the minimum expected loss that could be achieved if the underlying parameter W was observed.
Revised:  Minimum Excess Risk (MER) is defined as the difference between the minimum expected loss achievable when learning from data and the minimum expected loss that could be achieved if the underlying parameter W was observed.
Edit intent: clarity
Original:  Minimum Excess Risk (MER) in Bayesian learning is defined as the difference between the minimum expected loss achievable when learning from data and the minimum expected loss that could be achieved if the underlying parameter W was observed.
Revised:  Minimum Excess Risk (MER) in Bayesian learning is defined as the difference between the minimum expected loss achievable when learning from data and the minimum expected loss that could be achieved if W was observed.
Edit intent: clarity
Original:  The lower bound represents the minimum possible excess risk achievable by any process using R bits of information from the parameter W. For the upper bound, the optimization is further constrained to use R bits from the training set, a setting which relates MER to information-theoretic bounds on the generalization gap in frequentist learning.
Revised:  The lower bound represents the minimum possible excess risk achievable by any process using R bits of information from the parameter W. For the upper bound, the optimization is further constrained to use R bits from the training set, a setting which relates MER to information-theoretic bounds on the generalization gap in frequentist learning.
Edit intent: fluency
Original:  We derive information-theoretic bounds on the difference between these upper and lower bounds and show that they can provide order-wise tight rates for MER  . This analysis gives more insight into the information-theoretic nature of Bayesian learning as well as providing novel bounds.
Revised:  We derive information-theoretic bounds on the difference between these upper and lower bounds and show that they can provide order-wise tight rates for MER under certain conditions . This analysis gives more insight into the information-theoretic nature of Bayesian learning as well as providing novel bounds.
Edit intent: meaning-changed
Original:  In this work, we address this limitation of SIW by using a simple but powerful language for expressing  problem decompositions introduced recently by Bonet and Geffner, called policy sketches.
Revised:  In this work, we address this limitation of SIW by using a simple but powerful language for expressing finer problem decompositions introduced recently by Bonet and Geffner, called policy sketches.
Edit intent: meaning-changed
Original:  However, many existing classifiers, especially deep neural network classifiers, tend not to be calibrated . Post-hoc calibration is a technique to recalibrate a model , and its goal is to learn a calibration map.
Revised:  However, many existing classifiers, especially deep neural network classifiers, tend to be uncalibrated . Post-hoc calibration is a technique to recalibrate a model , and its goal is to learn a calibration map.
Edit intent: clarity
Original:  However, many existing classifiers, especially deep neural network classifiers, tend not to be calibrated . Post-hoc calibration is a technique to recalibrate a model , and its goal is to learn a calibration map.
Revised:  However, many existing classifiers, especially deep neural network classifiers, tend not to be calibrated . Post-hoc calibration is a technique to recalibrate a model by learning a calibration map.
Edit intent: clarity
Original:  Existing approaches mostly focus on constructing calibration maps with low calibration errors . Contrary to these methods, we study post-hoc calibration for multi-class classification under constraints, as a calibrator with a low calibration error does not necessarily mean it is usefulin practice .
Revised:  Existing approaches mostly focus on constructing calibration maps with low calibration errors , however, this quality is inadequate for a calibrator being useful .
Edit intent: coherence
Original:  Considering the serious imbalance of intrusion detection datasets will result in low classification performance on attack behaviors of small sample size and difficulty to detect network attacks accurately and efficiently, using ADASYN oversampling method to balance datasets was proposed in this paper.
Revised:  Considering the serious imbalance of intrusion detection datasets will result in low classification performance on attack behaviors of small sample size and difficulty to detect network attacks accurately and efficiently, using Adaptive Synthetic Sampling (ADASYN) method to balance datasets was proposed in this paper.
Edit intent: clarity
Original:  In this paper, we propose DefSent, a sentence embedding method that uses definition sentences from a word dictionary  . Since dictionaries are available for many languages, DefSent is more broadly applicable than methods using NLI datasets without constructing additional datasets.
Revised:  In this paper, we propose DefSent, a sentence embedding method that uses definition sentences from a word dictionary , which performs comparably on unsupervised semantics textual similarity (STS) tasks and slightly better on SentEval tasks than conventional methods . Since dictionaries are available for many languages, DefSent is more broadly applicable than methods using NLI datasets without constructing additional datasets.
Edit intent: meaning-changed
Original:  We demonstrate that DefSent performs comparably on unsupervised semantics textual similarity (STS) tasks and slightly better on SentEval tasks to the methods using large NLI datasets  .
Revised:  We demonstrate that DefSent performs comparably on unsupervised semantics textual similarity (STS) tasks and slightly better on SentEval tasks to the methods using large NLI datasets . Our code is publicly available at URL .
Edit intent: meaning-changed
Original:  There are two major types of modelling algorithm , template-free modelling and template-based modelling , which is suitable for easy prediction tasks , and is widely adopted in computer aided drug discoveries for drug design and screening.
Revised:  There are two major types of modelling algorithms , template-free modelling and template-based modelling , which is suitable for easy prediction tasks , and is widely adopted in computer aided drug discoveries for drug design and screening.
Edit intent: fluency
Original:  There are two major types of modelling algorithm , template-free modelling and template-based modelling , which is suitable for easy prediction tasks , and is widely adopted in computer aided drug discoveries for drug design and screening.
Revised:  There are two major types of modelling algorithm , template-free modelling and template-based modelling . The latter one is suitable for easy prediction tasks , and is widely adopted in computer aided drug discoveries for drug design and screening.
Edit intent: meaning-changed
Original:  There are two major types of modelling algorithm , template-free modelling and template-based modelling , which is suitable for easy prediction tasks , and is widely adopted in computer aided drug discoveries for drug design and screening.
Revised:  There are two major types of modelling algorithm , template-free modelling and template-based modelling , which is suitable for easy prediction tasks and is widely adopted in computer aided drug discoveries for drug design and screening.
Edit intent: fluency
Original:  There are two major types of modelling algorithm , template-free modelling and template-based modelling , which is suitable for easy prediction tasks , and is widely adopted in computer aided drug discoveries for drug design and screening.
Revised:  There are two major types of modelling algorithm , template-free modelling and template-based modelling , which is suitable for easy prediction tasks , and is widely adopted in computer-aided drug discoveries for drug design and screening.
Edit intent: fluency
Original:  To solve the two problems, we propose a structural optimization process based on template modelling, introducing two neural network models  predict the distance information of the missing regions and the accuracy of the distance pairs of different regions in the template modeling structure.
Revised:  To solve the two problems, we propose a structural optimization process based on template modelling, introducing two neural network models to predict the distance information of the missing regions and the accuracy of the distance pairs of different regions in the template modeling structure.
Edit intent: fluency
Original:  Although it has been several decades since its first edition, the current template-based modeling approach suffers from two important problems: 1) there are many missing regions in the template-query sequence alignment, and 2) the accuracy of the distance pairs from different regions of the template varies, and this information is not well introduced into the modeling.
Revised:  Although it has been several decades since its first edition, the current template-based modeling approach suffers from two critical problems: 1) there are many missing regions in the template-query sequence alignment, and 2) the accuracy of the distance pairs from different regions of the template varies, and this information is not well introduced into the modeling.
Edit intent: clarity
Original:  To solve the two problems, we propose a structural optimization process based on template modelling , introducing two neural network models to predict the distance information of the missing regions and the accuracy of the distance pairs of different regions in the template modeling structure.
Revised:  To solve these two problems, we propose a structural optimization process based on template modelling , introducing two neural network models to predict the distance information of the missing regions and the accuracy of the distance pairs of different regions in the template modeling structure.
Edit intent: coherence
Original:  To solve the two problems, we propose a structural optimization process based on template modelling , introducing two neural network models to predict the distance information of the missing regions and the accuracy of the distance pairs of different regions in the template modeling structure.
Revised:  To solve the two problems, we propose a structural optimization process based on template modeling , introducing two neural network models to predict the distance information of the missing regions and the accuracy of the distance pairs of different regions in the template modeling structure.
Edit intent: fluency
Original: We propose two novel loss functions based on Jensen-Shannon divergence for learning under label noise.Following the work of Ghosh et al.(2017) , we argue about their theoretical robustness. Furthermore, we reveal several other desirable properties by drawing informative connections to various loss functions, e.g. , cross entropy , mean absolute error, generalized cross entropy, symmetric cross entropy, label smoothing, and most importantly consistency regularization.
Revised: Prior works have found it beneficial to combine provably noise-robust loss functions e.g., mean absolute error (MAE) with standard categorical loss function e.g. , cross entropy , mean absolute error, generalized cross entropy, symmetric cross entropy, label smoothing, and most importantly consistency regularization.
Edit intent: meaning-changed
Original:  Furthermore, we reveal several other desirable properties by drawing informative connections to various loss functions, e.g. , cross entropy , mean absolute error, generalized cross entropy, symmetric cross entropy, label smoothing, and most importantly consistency regularization. We conduct extensive and systematic experiments using both synthetic (CIFAR) and real (WebVision) noise and demonstrate significant and consistent improvements over other loss functions.
Revised:  Furthermore, we reveal several other desirable properties by drawing informative connections to various loss functions, e.g. cross entropy (CE) to improve their learnability. Here, we propose to use Jensen-Shannon divergence as a noise-robust loss function and show that it interestingly interpolate between CE and MAE with a controllable mixing parameter. Furthermore, we make a crucial observation that CE exhibit lower consistency around noisy data points. Based on this observation, we adopt a generalized version of the Jensen-Shannon divergence for multiple distributions to encourage consistency around data points. Using this loss function, we show state-of-the-art results on both synthetic (CIFAR) and real (WebVision) noise and demonstrate significant and consistent improvements over other loss functions.
Edit intent: meaning-changed
Original:  The single model we proposed won the top 15th in the 2nd Alibaba Cloud AIOps Competition belonging to the 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining.
Revised:  The single model we proposed won the top 15th in the 2nd Alibaba Cloud AIOps Competition belonging to the 25th PAKDD conference. It takes only 30 minutes to pass the online test, while most of the other contestants' solution need more than 3 hours. Codes has been open source to URL
Edit intent: meaning-changed
Original:  We introduce TANGO, a novel neural model  for predicting task-specific tool interactions . TANGO is trained using demonstrations obtained from human teachers instructing a virtual robot in a physics simulator . TANGO encodes the world state comprising of objects and symbolic relationships between them  using a graph neural network.
Revised:  We introduce a novel neural model for predicting task-specific tool interactions . TANGO is trained using demonstrations obtained from human teachers instructing a virtual robot in a physics simulator . TANGO encodes the world state comprising of objects and symbolic relationships between them using a graph neural network.
Edit intent: fluency
Original:  We introduce TANGO, a novel neural model  for predicting task-specific tool interactions . TANGO is trained using demonstrations obtained from human teachers instructing a virtual robot in a physics simulator . TANGO encodes the world state comprising of objects and symbolic relationships between them  using a graph neural network.
Revised:  We introduce TANGO, a novel neural model , termed TANGO, for predicting task-specific tool interactions . TANGO is trained using demonstrations obtained from human teachers instructing a virtual robot in a physics simulator . TANGO encodes the world state comprising of objects and symbolic relationships between them using a graph neural network.
Edit intent: meaning-changed
Original:  We introduce TANGO, a novel neural model  for predicting task-specific tool interactions . TANGO is trained using demonstrations obtained from human teachers instructing a virtual robot in a physics simulator . TANGO encodes the world state comprising of objects and symbolic relationships between them  using a graph neural network.
Revised:  We introduce TANGO, a novel neural model for predicting task-specific tool interactions , trained using demonstrations obtained from human teachers instructing a virtual robot in a physics simulator . TANGO encodes the world state comprising of objects and symbolic relationships between them using a graph neural network.
Edit intent: clarity