wanyu/IteraTeR_v2 huggingface dataset:
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 (e.g. encryption of long messages) are only be secure against computationally bounded adversaries, and are indeed impossible for computationally unbounded attackers.
Edit intent: fluency
Original:  We show that computational limitation of attackers can indeed be useful in robust learning by demonstrating  a classifier for a learning task in which computational and information theoretic adversaries of bounded perturbations have very different power.
Revised:  We show that computational limitation of attackers can indeed be useful in robust learning by demonstrating the possibility of a classifier for a learning task in which computational and information theoretic adversaries of bounded perturbations have very different power.
Edit intent: clarity
Original:  We show that computational limitation of attackers can indeed be useful in robust learning by demonstrating  a classifier for a learning task in which computational and information theoretic adversaries of bounded perturbations have very different power.
Revised:  We show that computational limitation of attackers can indeed be useful in robust learning by demonstrating a classifier for some learning task for which computational and information theoretic adversaries of bounded perturbations have very different power.
Edit intent: clarity
Original:  We also show that the existence of such learning task in which computational robustness beats information theoretic robustness implies (average case) hard problems in NP .
Revised:  On the reverse directions, we also show that the existence of such learning task in which computational robustness beats information theoretic robustness implies (average case) hard problems in NP .
Edit intent: clarity
Original:  We also show that the existence of such learning task in which computational robustness beats information theoretic robustness implies (average case) hard problems in NP .
Revised:  We also show that the existence of such learning task in which computational robustness beats information theoretic robustness requires computational hardness by implying (average-case) hardness of NP .
Edit intent: clarity
Original: We study the low rank regression problem \mathbf{y \my = M \mathbf{x \mx + \epsilon, where \mathbf{x \mx \my are d_1 and d_2 dimensional vectors respectively.
Revised: We study the low rank regression problem \my = M \mathbf{x \mx + \epsilon, where \mathbf{x \mx \my are d_1 and d_2 dimensional vectors respectively.
Edit intent: clarity
Original: We study the low rank regression problem \mathbf{y \my = M \mathbf{x \mx + \epsilon, where \mathbf{x \mx \my are d_1 and d_2 dimensional vectors respectively.
Revised: We study the low rank regression problem \mathbf{y \my = M \mx + \epsilon, where \mathbf{x \mx \my are d_1 and d_2 dimensional vectors respectively.
Edit intent: fluency
Original: We study the low rank regression problem \mathbf{y \my = M \mathbf{x \mx + \epsilon, where \mathbf{x \mx \my are d_1 and d_2 dimensional vectors respectively.
Revised: We study the low rank regression problem \mathbf{y \my = M \mathbf{x \mx + \epsilon, where \mx \my are d_1 and d_2 dimensional vectors respectively.
Edit intent: fluency
Original:  Existing algorithms are designed for settings where n is typically as large as \mathrm{rank \Rank (M)(d_1+d_2).
Revised:  Existing algorithms are designed for settings where n is typically as large as \Rank (M)(d_1+d_2).
Edit intent: clarity
Original:  The proposed framework is modelled after downloading sample data from two leading app stores: Google Play and Apple iTunes.
Revised:  The proposed application in its final form is modelled after downloading sample data from two leading app stores: Google Play and Apple iTunes.
Edit intent: clarity
Original:  The proposed framework is modelled after downloading sample data from two leading app stores: Google Play and Apple iTunes.
Revised:  The proposed framework is modelled after mining sample data from two leading app stores: Google Play and Apple iTunes.
Edit intent: clarity
Original: This paper presents an application of developers' and users' dependent factors in the app store optimization .
Revised: In this paper a framework for app store optimization .
Edit intent: clarity
Original:  The proposed application in its final form is modelled after mining sample data from two leading app stores: Google Play and Apple App Store.
Revised:  The proposed framework is modelled after mining sample data from two leading app stores: Google Play and Apple App Store.
Edit intent: clarity
Original:  The proposed application in its final form is modelled after mining sample data from two leading app stores: Google Play and Apple App Store.
Revised:  The proposed application in its final form is modelled after downloading sample data from two leading app stores: Google Play and Apple App Store.
Edit intent: clarity
Original:  The proposed algorithm, named Learn2Adapt (L2A), is shown to provide a robust  adaptation strategy which, unlike most of the state-of-the-art techniques, does not require parameter tuning, channel model assumptions , or application-specific adjustments.
Revised:  The proposed algorithm, named Learn2Adapt (L2A), is shown to provide a robust adaptation strategy which, unlike most of the state-of-the-art techniques, does not require parameter tuning, channel model assumptions or application-specific adjustments.
Edit intent: fluency
Original:  Simulations show that L2A improves on average streaming bit-rate without impairing the overall Quality of Experience (QoE)  , a result that is independent of the channel and application scenarios.
Revised:  Simulations show that L2A improves on the overall Quality of Experience (QoE) , a result that is independent of the channel and application scenarios.
Edit intent: clarity
Original:  Simulations show that L2A improves on average streaming bit-rate without impairing the overall Quality of Experience (QoE)  , a result that is independent of the channel and application scenarios.
Revised:  Simulations show that L2A improves on average streaming bit-rate without impairing the overall Quality of Experience (QoE) , a result obtained independently of the channel and application scenarios.
Edit intent: clarity
Original:  While being successful, these models depend on huge numbers of ad hoc {\em  introduced parameters, which have to be estimated from finite amount of data and whose biophysical interpretation remains unclear.
Revised:  While being successful, these models depend on huge numbers of {\em introduced parameters, which have to be estimated from finite amount of data and whose biophysical interpretation remains unclear.
Edit intent: clarity
Original:  Here we propose an approach to parameter reduction, which is based on collective-variable selection .
Revised:  Here we propose an approach to parameter reduction, which is based on selecting collective sequence motifs .
Edit intent: clarity
Original:  It naturally leads to the formulation of statistical sequence models in terms of Hopfield-Potts models , with the Hopfield patterns being the collective variables .
Revised:  It naturally leads to the formulation of statistical sequence models in terms of Hopfield-Potts models .
Edit intent: clarity
Original:  We show that, when applied to protein data, even 20-40 patterns are sufficient to obtain statistically generative models.
Revised:  We show that, when applied to protein data, even 20-40 patterns are sufficient to obtain statistically close-to-generative models.
Edit intent: clarity
Original:  The Hopfield patterns are interpretable in terms of sequence motifs and may be used to clusterize amino-acid sequences into functional sub-families.
Revised:  The Hopfield patterns form interpretable sequence motifs and may be used to clusterize amino-acid sequences into functional sub-families.
Edit intent: clarity
Original:  The objective is to minimize the total distance between successive points in this sequence.
Revised:  The objective is to minimize the sum of distances between successive points in this sequence.
Edit intent: clarity
Original:  Recently, Bubeck et al. (STOC 2019) gave a 2^{O(d)}-competitive algorithm for this problem.
Revised:  Bubeck et al. (STOC 2019) gave a 2^{O(d)}-competitive algorithm for this problem.
Edit intent: coherence
Original: Convolution is a central operation in Convolutional Neural Networks (CNNs), which applies a kernel or mask to overlapping regions shifted across the image.
Revised: Convolution is a central operation in Convolutional Neural Networks (CNNs), which applies a kernel to overlapping regions shifted across the image.
Edit intent: clarity
Original:  To address this issue we propose what we call network deconvolution, a procedure that aims to remove pixel-wise and channel-wise correlations before the data is fed into each layer.
Revised:  To address this issue we propose what we call network deconvolution, a procedure which optimally removes pixel-wise and channel-wise correlations before the data is fed into each layer.
Edit intent: clarity
Original:  We show that by removing this correlation we are able to achieve better convergence rates during model training with superior results without the use of batch normalization on the CIFAR-10, CIFAR-100, MNIST, Fashion-MNIST datasets, as well as against reference models from "model zoo" on the ImageNet standard benchmark .
Revised:  We show that by removing this correlation we are able to achieve better convergence rates during model training with superior results without the use of batch normalization on the CIFAR-10, CIFAR-100, MNIST, Fashion-MNIST and ImageNet datasets .
Edit intent: clarity
Original:  However, because of the immense amount of correlations in real-world image data, convolutional kernels are in effect re-learning redundant data.
Revised:  However, because of the strong correlations in real-world image data, convolutional kernels are in effect re-learning redundant data.
Edit intent: clarity
Original:  Network deconvolution can be efficiently calculated at a fraction of the computation cost of a convolution layer.
Revised:  Network deconvolution can be efficiently calculated at a fraction of the computational cost of a convolution layer.
Edit intent: fluency
Original:  We apply our network deconvolution operation to 10 modern neural network models by replacing batch normailization within each.
Revised:  We apply our network deconvolution operation to 10 modern neural network models by replacing batch normalization within each.
Edit intent: fluency
Original:  Our extensive experiments show the network deconvolution operation is able to deliver performance improvement in all cases on  CIFAR-10, CIFAR-100, MNIST, Fashion-MNIST  and ImageNet datasets.
Revised:  Extensive experiments show that the network deconvolution operation is able to deliver performance improvement in all cases on CIFAR-10, CIFAR-100, MNIST, Fashion-MNIST and ImageNet datasets.
Edit intent: clarity
Original:  Our extensive experiments show the network deconvolution operation is able to deliver performance improvement in all cases on  CIFAR-10, CIFAR-100, MNIST, Fashion-MNIST  and ImageNet datasets.
Revised:  Our extensive experiments show the network deconvolution operation is able to deliver performance improvement in all cases on the CIFAR-10, CIFAR-100, MNIST, Fashion-MNIST and ImageNet datasets.
Edit intent: fluency
Original:  Our second algorithm reduces time complexity of pre-processing to O \left( nnz(M) \log(n/\epsilon) + m^3 \log^2 m + m^2 \log(1 \log^7(m} /\epsilon) \right), where nnz(M) is the number of nonzero elements of M.
Revised:  Our second algorithm reduces time complexity of pre-processing to O \left( nnz(M) + m^3 \log^2 m + m^2 \log(1 \log^7(m} /\epsilon) \right), where nnz(M) is the number of nonzero elements of M.
Edit intent: clarity
Original:  Then after an edge insertion or an edge deletion or a node insertion or a node deletion, it updates the approximate solution in O(qm) time, with q=O (m^2 \left({\epsilon^2} \log^6(m} /\epsilon ^2)\right) our first algorithm outperforms our second algorithm and if \ln n \geq \epsilon^{-1} our second algorithm outperforms our first algorithm} .
Revised:  Then after an edge insertion or an edge deletion or a node insertion or a node deletion, it updates the approximate solution in O(qm) time, with q=O \left({\epsilon^2} \log^6(m} /\epsilon ^2)\right) our first algorithm outperforms our second algorithm and if \ln n \geq \epsilon^{-1} our second algorithm outperforms our first algorithm} .
Edit intent: clarity
Original:  Then after an edge insertion or an edge deletion or a node insertion or a node deletion, it updates the approximate solution in O(qm) time, with q=O (m^2 \left({\epsilon^2} \log^6(m} /\epsilon ^2)\right) our first algorithm outperforms our second algorithm and if \ln n \geq \epsilon^{-1} our second algorithm outperforms our first algorithm} .
Revised:  Then after an edge insertion or an edge deletion or a node insertion or a node deletion, it updates the approximate solution in O(qm) time, with q=O (m^2 \left({\epsilon^2} \log^6(m} /\epsilon )\right) our first algorithm outperforms our second algorithm and if \ln n \geq \epsilon^{-1} our second algorithm outperforms our first algorithm} .
Edit intent: fluency
Original:  Our model combines a  probabilistic encoder with a generative model and a generative linear classifier that get shared across tasks.
Revised:  Our model combines a probabilistic encoder with a generative model and a linear classifier that get shared across tasks.
Edit intent: fluency
Original:  The open set recognition bounds the approximate posterior by fitting regions of high density on the basis of correctly classified data points and balances open-space risk with recognition errors.
Revised:  The open set recognition bounds the approximate posterior by fitting regions of high density on the basis of correctly classified data points and balances open set detection with recognition errors.
Edit intent: clarity
Original:  Catastrophic inference for both generative models is significantly alleviated through generative replay, where the open set recognition is used to sample from high density areas of the class specific posterior and reject statistical outliers.
Revised:  Catastrophic forgetting is significantly alleviated through generative replay, where the open set recognition is used to sample from high density areas of the class specific posterior and reject statistical outliers.
Edit intent: clarity
Original:  We demonstrate compelling results in the challenging scenario of incrementally expanding the single-head classifier for both class incremental visual and audio classification tasks, as well as incremental learning of datasets across modalities.
Revised:  We show compelling results in the challenging scenario of incrementally expanding the single-head classifier for both class incremental visual and audio classification tasks, as well as incremental learning of datasets across modalities.
Edit intent: fluency
Original: We introduce a unified probabilistic approach for deep continual learning  based on variational Bayesian inference with open set recognition.
Revised: We introduce a probabilistic approach to unify deep continual learning based on variational Bayesian inference with open set recognition.
Edit intent: fluency
Original: We introduce a unified probabilistic approach for deep continual learning  based on variational Bayesian inference with open set recognition.
Revised: We introduce a unified probabilistic approach for deep continual learning with open set recognition, based on variational Bayesian inference with open set recognition.
Edit intent: fluency
Original:  We show compelling results in the challenging scenario of incrementally expanding the single-head classifier for both class incremental visual and audio classification tasks , as well as incremental learning of datasets across modalities.
Revised:  We show compelling results in the challenging scenario of incrementally expanding the single-head classifier for both class incremental visual and audio classification tasks , as well as cross-dataset scenarios across modalities.
Edit intent: clarity
Original: We introduce a probabilistic approach to unify deep continual learning with open set recognition  , based on variational Bayesian inference.
Revised: We introduce a probabilistic approach to unify open set recognition , based on variational Bayesian inference.
Edit intent: clarity
Original:  Our approach requires no storing of old- or upfront knowledge of future data  and is empirically validated on visual and audio tasks in class incremental, as well as cross-dataset scenarios across modalities.
Revised:  Our approach requires neither storing of old, nor upfront knowledge of future data and is empirically validated on visual and audio tasks in class incremental, as well as cross-dataset scenarios across modalities.
Edit intent: fluency
Original:  Our approach requires no storing of old- or upfront knowledge of future data  and is empirically validated on visual and audio tasks in class incremental, as well as cross-dataset scenarios across modalities.
Revised:  Our approach requires no storing of old- or upfront knowledge of future data , and is empirically validated on visual and audio tasks in class incremental, as well as cross-dataset scenarios across modalities.
Edit intent: fluency
Original: Sparse rewards are one of the most important challenges in reinforcement learning.
Revised: Solving tasks with sparse rewards is one of the most important challenges in reinforcement learning.
Edit intent: clarity
Original:  In the single-agent setting, these challenges have been addressed by introducing intrinsic rewards that motivate agents to explore unseen regions of their state spaces . Applying these techniques naively to the multi-agent setting results in individual agents exploring independently, without any coordination among themselves.
Revised:  In the single-agent setting, this challenge is addressed by introducing intrinsic rewards that motivate agents to explore unseen regions of their state spaces . Applying these techniques naively to the multi-agent setting results in individual agents exploring independently, without any coordination among themselves.
Edit intent: clarity
Original:  In the single-agent setting, these challenges have been addressed by introducing intrinsic rewards that motivate agents to explore unseen regions of their state spaces . Applying these techniques naively to the multi-agent setting results in individual agents exploring independently, without any coordination among themselves.
Revised:  In the single-agent setting, these challenges have been addressed by introducing intrinsic rewards that motivate agents to explore unseen regions of their state spaces ; however, applying these techniques naively to the multi-agent setting results in individual agents exploring independently, without any coordination among themselves.
Edit intent: coherence
Original:  In the single-agent setting, these challenges have been addressed by introducing intrinsic rewards that motivate agents to explore unseen regions of their state spaces . Applying these techniques naively to the multi-agent setting results in individual agents exploring independently, without any coordination among themselves.
Revised:  In the single-agent setting, these challenges have been addressed by introducing intrinsic rewards that motivate agents to explore unseen regions of their state spaces . Applying these techniques naively to the multi-agent setting results in agents exploring independently, without any coordination among themselves.
Edit intent: clarity
Original:  We argue that learning in cooperative multi-agent settings can be accelerated and improved if agents coordinate with respect to what they have explored .
Revised:  We argue that exploration in cooperative multi-agent settings can be accelerated and improved if agents coordinate with respect to what they have explored .
Edit intent: clarity
Original:  We argue that learning in cooperative multi-agent settings can be accelerated and improved if agents coordinate with respect to what they have explored .
Revised:  We argue that learning in cooperative multi-agent settings can be accelerated and improved if agents coordinate with respect to the regions of the state space they explore .
Edit intent: clarity
Original:  In this paper we propose an approach for learning how to dynamically select between different types of intrinsic rewards which consider not just what an individual agent has explored, but all agents, such that the agents can coordinate their exploration and maximize extrinsic returns.
Revised:  In this paper we propose an approach for learning how to dynamically select between proposed intrinsic reward types which consider not just what an individual agent has explored, but all agents, such that the agents can coordinate their exploration and maximize extrinsic returns.
Edit intent: clarity
Original:  Concretely, we formulate the approach as a hierarchical policy where a high-level controller selects among sets of policies trained on different types of intrinsic rewards and the low-level controllers learn the action policies of all agents under these specific rewards.
Revised:  Concretely, we formulate the approach as a hierarchical policy where a high-level controller selects among sets of policies trained on diverse intrinsic rewards and the low-level controllers learn the action policies of all agents under these specific rewards.
Edit intent: clarity
Original:  We argue that exploration in cooperative multi-agent settings can be accelerated and improved if agents coordinate with respect to the regions of the state space they explore .
Revised:  Exploration in cooperative multi-agent settings can be accelerated and improved if agents coordinate with respect to the regions of the state space they explore .
Edit intent: clarity
Original:  We argue that exploration in cooperative multi-agent settings can be accelerated and improved if agents coordinate with respect to the regions of the state space they explore .
Revised:  We argue that exploration in cooperative multi-agent settings can be accelerated and improved if agents coordinate their exploration .
Edit intent: clarity
Original:  We demonstrate the effectiveness of the proposed approach in a multi-agent GridWorld domain with sparse rewards and then show that our method scales up to more complex settings by evaluating on the VizDoom platform .
Revised:  We demonstrate the effectiveness of the proposed approach in cooperative domains with sparse rewards and then show that our method scales up to more complex settings by evaluating on the VizDoom platform .
Edit intent: clarity
Original:  However, in many applications, the objective functions are not exactly submodular.
Revised:  However, in many applications, the objective function is not exactly submodular.
Edit intent: fluency
Original:   While submodular minimization algorithms rely on intricate connections between submodularity and convexity, we show that these relations can be extended sufficiently to obtain guaranteed approximations for approximately submodular minimization.
Revised:  While submodular minimization algorithms rely on intricate connections between submodularity and convexity, we show that these relations can be extended sufficiently to obtain approximation guarantees for non-submodular minimization.
Edit intent: clarity
Original:  In particular, we prove how a projected subgradient method can perform well even for a class of non-submodular functions.
Revised:  In particular, we prove how a projected subgradient method can perform well even for certain non-submodular functions.
Edit intent: clarity