wanyu/IteraTeR_v2 huggingface dataset:
Original:  We  use our directed closure coefficients as predictors in two machine learning tasks.
Revised:  We also use our directed closure coefficients as predictors in two machine learning tasks.
Edit intent: coherence
Original:  In this paper, we present a low pass  recurrent neural network (lpRNN) cell that can be trained using backpropagation and implemented on neuromorphic SNN devices.
Revised:  In this paper, we present a low pass recurrent neural network enables mapping to neuromorphic SNN devices.
Edit intent: clarity
Original:  We further argue that the low pass filter is a temporal regularizer and highlight its advantage in a Long Short-Term Memory (LSTM) cell.
Revised:  We further show that the low pass filter is a temporal regularizer and highlight its advantage in a Long Short-Term Memory (LSTM) cell.
Edit intent: clarity
Original:  We show that low pass RNNs are able to learn and generalize better than their unfiltered variants on two long memory synthetic tasks, a character-level text modeling task, and a neuromorphic spoken command detection system .
Revised:  We show that low pass RNNs are able to learn and generalise better than their unfiltered variants on two long memory synthetic tasks, a character-level text modeling task, and a neuromorphic spoken command detection system .
Edit intent: fluency
Original:  We show that low pass RNNs are able to learn and generalize better than their unfiltered variants on two long memory synthetic tasks, a character-level text modeling task, and a neuromorphic spoken command detection system .
Revised:  We show that low pass RNNs are able to learn and generalize better than their unfiltered variants .
Edit intent: clarity
Original: The increasing difficulty with Moore's law scaling and the remarkable success of machine learning have triggered a renaissance in the study of low-latency, energy-efficient accelerators for machine learning applications .
Revised: The increasing need for compact and low-power computing solutions for machine learning applications has triggered a renaissance in the study of low-latency, energy-efficient accelerators for machine learning applications .
Edit intent: clarity
Original: The increasing difficulty with Moore's law scaling and the remarkable success of machine learning have triggered a renaissance in the study of low-latency, energy-efficient accelerators for machine learning applications .
Revised: The increasing difficulty with Moore's law scaling and the remarkable success of machine learning have triggered a renaissance in the study of energy-efficient accelerators for machine learning applications .
Edit intent: clarity
Original: The increasing difficulty with Moore's law scaling and the remarkable success of machine learning have triggered a renaissance in the study of low-latency, energy-efficient accelerators for machine learning applications .
Revised: The increasing difficulty with Moore's law scaling and the remarkable success of machine learning have triggered a renaissance in the study of low-latency, energy-efficient neural network accelerators .
Edit intent: clarity
Original:  In particular, spiking neural networks (SNNs) and their neuromorphic hardware implementations have started to receive substantial attention from  academia and industry.
Revised:  In particular, in-memory computing neuromorphic architectures have started to receive substantial attention from academia and industry.
Edit intent: clarity
Original:  In particular, spiking neural networks (SNNs) and their neuromorphic hardware implementations have started to receive substantial attention from  academia and industry.
Revised:  In particular, spiking neural networks (SNNs) and their neuromorphic hardware implementations have started to receive substantial attention from both academia and industry.
Edit intent: clarity
Original:  However, SNNs perform relatively poorly compared to their rate-based counterparts , in terms of accuracy in pattern recognition tasks .
Revised:  However, most of these architectures rely on spiking neural networks, which typically perform poorly compared to their rate-based counterparts , in terms of accuracy in pattern recognition tasks .
Edit intent: clarity
Original:  However, SNNs perform relatively poorly compared to their rate-based counterparts , in terms of accuracy in pattern recognition tasks .
Revised:  However, SNNs perform relatively poorly compared to their rate-based counterparts , in terms of accuracy .
Edit intent: clarity
Original: The increasing need for compact and low-power computing solutions for machine learning applications has triggered a renaissance in the study of energy-efficient neural network accelerators.
Revised: The increasing need for compact and low-power computing solutions for machine learning applications has triggered significant interest in energy-efficient neural network accelerators.
Edit intent: clarity
Original:  In this paper, we propose a new adaptive spiking neuron model that can also be abstracted as a low-pass filter.
Revised:  In this paper, we propose a new adaptive spiking neuron model that can be abstracted as a low-pass filter.
Edit intent: clarity
Original:  In this paper, we present a new connectionist model , the Sequential Neural Coding Network, and its learning procedure, grounded in the neurocognitive theory of predictive coding.
Revised:  In this article, we propose a new kind of connectionist architecture , the Sequential Neural Coding Network, and its learning procedure, grounded in the neurocognitive theory of predictive coding.
Edit intent: clarity
Original:  The architecture experiences significantly less forgetting as compared to standard neural models and outperforms a variety of previously proposed remedies and methods when trained across multiple task datasets in a stream-like fashion.
Revised:  The architecture experiences significantly less forgetting as compared to standard neural models and outperforms a wide swath of previously proposed remedies and methods when trained across multiple task datasets in a stream-like fashion.
Edit intent: clarity
Original:  The architecture experiences significantly less forgetting as compared to standard neural models and outperforms a variety of previously proposed remedies and methods when trained across multiple task datasets in a stream-like fashion.
Revised:  The architecture experiences significantly less forgetting as compared to standard neural models and outperforms a variety of previously proposed methods even though it is trained across task datasets in a stream-like fashion.
Edit intent: clarity
Original:  The promising performance demonstrated in our experiments offers motivation that directly incorporating mechanisms prominent in real neuronal systems, such as competition, sparse activation patterns, and iterative input processing, can create viable pathways for tackling the  challenge of lifelong machine learning  .
Revised:  The promising performance demonstrated in our experiments offers motivation that directly incorporating mechanisms prominent in real neuronal systems, such as competition, sparse activation patterns, and iterative input processing, a new possibility for tackling the challenge of lifelong machine learning .
Edit intent: clarity
Original:  The promising performance demonstrated in our experiments offers motivation that directly incorporating mechanisms prominent in real neuronal systems, such as competition, sparse activation patterns, and iterative input processing, can create viable pathways for tackling the  challenge of lifelong machine learning  .
Revised:  The promising performance demonstrated in our experiments offers motivation that directly incorporating mechanisms prominent in real neuronal systems, such as competition, sparse activation patterns, and iterative input processing, can create viable pathways for tackling the grand challenge of lifelong machine learning .
Edit intent: clarity
Original: Activation functions play an important role in the training of artificial neural networks and the Rectified Linear Unit (ReLU) has been the mainstream in recent years.
Revised: Activation functions play an important role in training artificial neural networks and the Rectified Linear Unit (ReLU) has been the mainstream in recent years.
Edit intent: clarity
Original:  Most of the activation functions currently used are deterministic in nature, whose input-output relationship is fixed .
Revised:  Most of the activation functions currently used are deterministic in nature, with their fixed input-output relationship is fixed .
Edit intent: clarity
Original:  Most of the activation functions currently used are deterministic in nature, whose input-output relationship is fixed .
Revised:  Most of the activation functions currently used are deterministic in nature, whose input-output relationship .
Edit intent: clarity
Original:  In the trainable ProbAct, the  variance of the activation distribution is trained through back-propagation .
Revised:  In the trainable ProbAct, the variance of the activation distribution is trained within the back-propagation .
Edit intent: clarity
Original:  We also show that the stochastic perturbation through ProbAct is a viable generalization technique that can prevent overfitting .
Revised:  We also show that the stochastic perturbation induced through ProbAct acts as a viable generalization technique that can prevent overfitting .
Edit intent: clarity
Original:  We also show that the stochastic perturbation through ProbAct is a viable generalization technique that can prevent overfitting .
Revised:  We also show that the stochastic perturbation through ProbAct is a viable generalization technique for feature augmentation .
Edit intent: clarity
Original: Automatically computing shape correspondence is a difficult problem, especially when the shapes are significantly different.
Edit intent: coherence
Original:  In this paper we suggest a fully automatic method for shape correspondence , that is suitable for non isometric shapes and shapes of different topology.
Revised:  In this paper we propose a fully automatic method for shape correspondence , that is suitable for non isometric shapes and shapes of different topology.
Edit intent: fluency
Original:  Our method is general, widely applicable, and outperforms state of the art methods for automatic shape correspondence both quantitatively and qualitatively  .
Revised:  Our method is general, widely applicable, and outperforms state of the art methods for automatic shape correspondence both quantitatively and qualitatively on challenging datasets .
Edit intent: clarity
Original:  We prove convergence of the numerical scheme and estimate the rate of convergence.
Revised:  We prove convergence of the numerical method and estimate the rate of convergence.
Edit intent: clarity
Original:  To the best of our knowledge, this is the very first study that uses the MOGP in the hybrid two-stage structure for individuals  activity status prediction.
Revised:  To the best of our knowledge, this is the very first study that uses the MOGP in the hybrid two-stage structure for individuals ' activity status prediction.
Edit intent: fluency
Original: In this article , we introduce partial dependency modality D into epistemic logic so as to reason about partial dependency relationship in Kripke models.
Revised: In this paper , we introduce partial dependency modality D into epistemic logic so as to reason about partial dependency relationship in Kripke models.
Edit intent: fluency
Original:  The resulting dependence epistemic logic possesses decent expressivity and beautiful properties.
Revised:  The resulted dependence epistemic logic possesses decent expressivity and beautiful properties.
Edit intent: fluency
Original:  Several interesting examples are given, which illustrate this logic's practical usage.
Revised:  Several interesting examples are provided, which highlight this logic's practical usage.
Edit intent: clarity
Original:  The logic's bisimulation is then discussed, and we provide a sound and strongly complete axiomatization for part of the language .
Revised:  The logic's bisimulation is then discussed, and we give a sound and strongly complete axiomatization for part of the language .
Edit intent: clarity
Original:  The logic's bisimulation is then discussed, and we provide a sound and strongly complete axiomatization for part of the language .
Revised:  The logic's bisimulation is then discussed, and we provide a sound and strongly complete axiomatization for a sub-language of the logic .
Edit intent: clarity
Original:  Different from traditional supervised attack strategies, EDA does not need labeling information, and, to the best of our knowledge, is the first unsupervised network embedding attack method.
Revised:  Different from traditional supervised attack strategies, EDA does not need labeling information, and, in other words, is an unsupervised network embedding attack method.
Edit intent: coherence
Original:  In addition, we establish the global convergence and convergence rate of our block decomposition algorithm.
Revised:  In addition, we establish the convergence rate of our block decomposition algorithm.
Edit intent: clarity
Original:  In this letter , we propose a dimerization model of ligand-induced receptors in multivalent form representing physical observables under basis vectors of various aggregated receptor-states.
Revised:  Here , we propose a dimerization model of ligand-induced receptors in multivalent form representing physical observables under basis vectors of various aggregated receptor-states.
Edit intent: coherence
Original:  Our simulations of multivalent models not only reject Wofsy-Goldstein prediction for cooperativity, but show higher-order oligomer formations can shift cooperativity from positive to negative.
Revised:  Our simulations of multivalent models not only reject Wofsy-Goldstein parameter conditions for cooperativity, but show higher-order oligomer formations can shift cooperativity from positive to negative.
Edit intent: clarity
Original:  The experiment results show that our method can efficiently synthesize high quality emotional speech from any source speech for given emotion categories, without the limitation of language differences and aligned speech pairs.
Revised:  The experiment results show that our method can efficiently generate high-quality emotional speech for any given emotion category, without aligned speech pairs.
Edit intent: clarity
Original: Despite the remarkable progress made for synthesizing emotional speech from text, it is still challenging to provide emotion information to existing speech segments.
Revised: Despite the remarkable progress made in synthesizing emotional speech from text, it is still challenging to provide emotion information to existing speech segments.
Edit intent: fluency
Original:  Based on cycle-consistent generative adversarial network, our method ensures the transfer of only emotion with novel simple loss designs.
Revised:  Based on cycle-consistent generative adversarial network, our method ensures the transfer of only emotion information across speeches with simple loss designs.
Edit intent: clarity
Original:  In this work , we propose Continuous Integrate-and-Fire (CIF), a 'soft ' and 'monotonic ' acoustic-to-linguistic alignment mechanism that addresses the boundary positioning by simulating the integrate-and-fire neuron model using continuous functions under the encoder-decoder framework .
Revised:  In this work , we propose a novel soft and monotonic alignment mechanism used for sequence transduction. It is inspired by the integrate-and-fire neuron model using continuous functions under the encoder-decoder framework .
Edit intent: clarity
Original:  Evaluated on multiple ASR datasets that cover different languages and speech types , the CIF-based model shows stable convergence and competitive performance.
Revised:  Evaluated on multiple ASR datasets that cover different languages and speech types , the CIF-based model shows competitive performance.
Edit intent: clarity
Original:  Especially , it achieves a word error rate (WER) of 2.92 \% on the test-clean of Librispeech  .
Revised:  Notably , it achieves a word error rate (WER) of 2.92 \% on the test-clean of Librispeech .
Edit intent: fluency
Original:  Search engine surrogates help a user answer the question `` Will this link meet my information need?
Revised:  Search engine surrogates help a user answer the question ` Will this link meet my information need?
Edit intent: fluency
Original:  '' Social media surrogates help a user decide `` Should I click on this?
Revised:  ' Social media surrogates help a user decide `` Should I click on this?
Edit intent: fluency
Original:  '' Social media surrogates help a user decide `` Should I click on this?
Revised:  '' Social media surrogates help a user decide ` Should I click on this?
Edit intent: fluency
Original:  '' Our use case is subtly different.
Revised:  ' Our use case is subtly different.
Edit intent: fluency
Original:  We want to help users answer the question of `` What does the underlying collection contain?
Revised:  We want to help users answer the question of ` What does the underlying collection contain?
Edit intent: fluency
Original:  '' But which surrogate should we use?
Revised:  ' But which surrogate should we use?
Edit intent: fluency
Original:  Search engine surrogates help a user answer the question ` Will this link meet my information need?
Revised:  Search engine surrogates help a user answer the question " Will this link meet my information need?
Edit intent: fluency
Original:  ' Social media surrogates help a user decide ` Should I click on this?
Revised:  " Social media surrogates help a user decide ` Should I click on this?
Edit intent: fluency
Original:  ' Social media surrogates help a user decide ` Should I click on this?
Revised:  ' Social media surrogates help a user decide " Should I click on this?
Edit intent: fluency
Original:  ' Our use case is subtly different.
Revised:  " Our use case is subtly different.
Edit intent: fluency
Original:  We want to help users answer the question of ` What does the underlying collection contain?
Revised:  We want to help users answer the question of " What does the underlying collection contain?
Edit intent: fluency
Original:  ' But which surrogate should we use?
Revised:  " But which surrogate should we use?
Edit intent: fluency
Original: How to optimally dispatch orders to vehicles and how to trade off between immediate and future returns are fundamental questions for a typical ride-hailing platform.
Revised: How to optimally dispatch orders to vehicles and how to tradeoff between immediate and future returns are fundamental questions for a typical ride-hailing platform.
Edit intent: fluency
Original:  We model ride-hailing as a large-scale parallel ranking problem and study the joint decision-making the task of order dispatching and fleet management in online ride-hailing platforms.
Revised:  We model ride-hailing as a large-scale parallel ranking problem and study the joint decision-making task of order dispatching and fleet management in online ride-hailing platforms.
Edit intent: fluency
Original:  This task brings unique challenges in the  four aspects.
Revised:  This task brings unique challenges in the following four aspects.
Edit intent: clarity
Original:  Fourth, to achieve the multi-scale ride-hailing platform, we conduct the decision-making process in a hierarchical way where  multi-head attention mechanism is utilized to incorporate the impacts of neighbor agents and capture the key agent in each scale.
Revised:  Fourth, to achieve the multi-scale ride-hailing platform, we conduct the decision-making process in a hierarchical way where a multi-head attention mechanism is utilized to incorporate the impacts of neighbor agents and capture the key agent in each scale.
Edit intent: fluency
Original:  When the training dataset contains a fraction of noisy labels, can neural networks be resistant to over-fitting and still generalize on the true distribution?
Revised:  Such over-fitting and still generalize on the true distribution?
Edit intent: clarity
Original:  Theoretically, both methods are related to kernel ridge regression with respect to the NTK, and we prove their generalization guarantee on the true data distribution despite being trained using noisy labels.
Revised:  Theoretically, we prove that gradient descent training with either of these two methods leads to a generalization guarantee on the true data distribution despite being trained using noisy labels.
Edit intent: clarity
Original:  Theoretically, we prove that gradient descent training with either of these two methods leads to a generalization guarantee on the true data distribution despite being trained using noisy labels.
Revised:  Theoretically, we prove that gradient descent training with either of these two methods leads to a generalization guarantee on the clean data distribution despite being trained using noisy labels.
Edit intent: clarity
Original:  Empirical results verify the effectiveness of these methods on noisily labeled datasets.
Revised:  Experimental results verify the effectiveness of these methods on noisily labeled datasets.
Edit intent: clarity
Original: With a computer-aided approach based on the connection with equitable partitions, we establish the uniqueness of the orthogonal array OA(1536,13,2,7) (or, in a different notation, OA_{12 constructed in [D.G.Fon-Der-Flaass.
Revised: With a computer-aided approach based on the connection with equitable partitions, we establish the uniqueness of the orthogonal array OA(1536,13,2,7) , constructed in [D.G.Fon-Der-Flaass.
Edit intent: coherence
Original:  By shortening the OA(1536,13,2,7), we obtain 3 nonequivalent orthogonal arrays OA(768,12,2,6) (or OA_{12 , which is a complete classification for these parameters too.
Revised:  By shortening the OA(1536,13,2,7), we obtain 3 inequivalent orthogonal arrays OA(768,12,2,6) (or OA_{12 , which is a complete classification for these parameters too.
Edit intent: fluency
Original:  By shortening the OA(1536,13,2,7), we obtain 3 nonequivalent orthogonal arrays OA(768,12,2,6) (or OA_{12 , which is a complete classification for these parameters too.
Revised:  By shortening the OA(1536,13,2,7), we obtain 3 nonequivalent orthogonal arrays OA(768,12,2,6) , which is a complete classification for these parameters too.
Edit intent: coherence
Original:  We construct an orthogonal array with these and similar parameters OA(N=2^{n-m+1},n=2^m-2,2,t=2^{m-1}-1), m\ge 4, that is not a punctured 1-perfect code .
Revised:  We construct orthogonal arrays with these and similar parameters OA(N=2^{n-m+1},n=2^m-2,2,t=2^{m-1}-1), m\ge 4, that is not a punctured 1-perfect code .
Edit intent: fluency
Original:  We construct an orthogonal array with these and similar parameters OA(N=2^{n-m+1},n=2^m-2,2,t=2^{m-1}-1), m\ge 4, that is not a punctured 1-perfect code .
Revised:  We construct an orthogonal array with these and similar parameters OA(N=2^{n-m+1},n=2^m-2,2,t=2^{m-1}-1), m\ge 4, that are not punctured 1-perfect code .
Edit intent: fluency
Original:  We construct an orthogonal array with these and similar parameters OA(N=2^{n-m+1},n=2^m-2,2,t=2^{m-1}-1), m\ge 4, that is not a punctured 1-perfect code .
Revised:  We construct an orthogonal array with these and similar parameters OA(N=2^{n-m+1},n=2^m-2,2,t=2^{m-1}-1), m\ge 4, that is not a punctured 1-perfect codes .
Edit intent: fluency
Original:  We express these brain patterns in a closed-form mathematical expression, termed the ` Cognitive Neural Activation metric ' (CNA) and apply it to DNNs.
Revised:  We express these brain patterns in a closed-form mathematical expression, termed the ` Cognitive Neural Activation metric (CNA) and apply it to DNNs.
Edit intent: fluency
Original:  Indeed, numerous cryptographic tasks (e.g. encryption of long messages) are only be secure against computationally bounded adversaries, and are indeed mpossible for computationally unbounded attackers.
Revised:  Indeed, numerous cryptographic tasks can only be secure against computationally bounded adversaries, and are indeed mpossible for computationally unbounded attackers.
Edit intent: clarity