wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To ad-dress these issues, we propose a method to generate syntheticimages from existing categorical emotion datasets using facemorphing, with full control over the resulting sample distri-bution as well as dimensional labels in the circumplex space  , while achieving augmentation factors of at least 20x or more.
Revised:  To ad-dress these issues, we propose a method to generate syntheticimages from existing categorical emotion datasets using facemorphing, with full control over the resulting sample distri-bution as well as dimensional labels in the circumplex space with full control over the resulting sample distribution , while achieving augmentation factors of at least 20x or more.
Edit intent: meaning-changed
Original: We present a new deep learning-based adaptive control framework for nonlinear systems with multiplicatively-separable parametric uncertainty, called an adaptive Neural Contraction Metric (aNCM). The aNCM uses a neural network model of an optimal adaptive contraction metric, the existence of which guarantees asymptotic stability and exponential boundedness of system trajectories under the parametric uncertainty. In particular, we exploit the concept of a Neural Contraction Metric (NCM) to obtain a nominal provably stable robust control policy for nonlinear systems with bounded disturbances, and combine this policy with a novel adaptation law to achieve stability guarantees. We also show that the framework is applicable to adaptive control of dynamical systemsmodeled via basis function approximation  .
Revised: We present a new deep learning-based adaptive control framework for nonlinear systems with multiplicatively-separable parametric uncertainty, called an adaptive Neural Contraction Metric . The framework utilizes a deep neural network to approximate a stabilizing adaptive control law parameterized by an optimal contraction metric. The use of deep networks permits real-time implementation of the control law and broad applicability to a variety of systems, including systems modeled with basis function approximation .
Edit intent: clarity
Original:  We also show that the framework is applicable to adaptive control of dynamical systemsmodeled via basis function approximation  .
Revised:  We also show that the framework is applicable to adaptive control of dynamical systemsmodeled via basis function approximation methods. We show using contraction theory that aNCM ensures exponential boundedness of the distance between the target and controlled trajectories even under the presence of the parametric uncertainty, robustly to the learning errors caused by aNCM approximation as well as external additive disturbances .
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Original:  Domain generalization (DG), i.e., out-of-distribution generalization, has attracted increased interests in recent years.
Revised: Machine learning systems generally assume that the training and testing distributions are the same. To this end, a key requirement is to develop models that can generalize to unseen distributions. Domain generalization (DG), i.e., out-of-distribution generalization, has attracted increased interests in recent years.
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Original:  This paper introduces the sparse random feature method that learns parsimonious random feature models utilizing techniques from compressive sensing . We provide uniform bounds on the approximation error  for functions in a  reproducing kernel Hilbert space  depending on the number of samples and the distribution of features.
Revised:  This paper introduces the sparse random feature method that learns parsimonious random feature models utilizing techniques from compressive sensing to generate random feature expansions with theoretical guarantees even in the data-scarce setting. In particular, we provide uniform bounds on the approximation error for functions in a reproducing kernel Hilbert space depending on the number of samples and the distribution of features.
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Original:  We empirically support our theoretical claims by showing that MPSNs can distinguish challenging strongly regular graphs for which GNNs fail and, when equipped with orientation equivariant layers, they can improve classification accuracy in oriented SCs compared to a GNN baseline . Additionally, we implement a library for message passing on simplicial complexes that we envision to release in due course .
Revised:  We empirically support our theoretical claims by showing that MPSNs can distinguish challenging strongly regular graphs for which GNNs fail and, when equipped with orientation equivariant layers, they can improve classification accuracy in oriented SCs compared to a GNN baseline .
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Original:  We propose an objective function that naturally avoids such collapse by measuring the cross-correlation matrix between the outputs of two identical networks fed with distorted versions of a sample, and making it as close to the identity matrix as possible.
Revised:  We propose an objective function that naturally avoids collapse by measuring the cross-correlation matrix between the outputs of two identical networks fed with distorted versions of a sample, and making it as close to the identity matrix as possible.
Edit intent: clarity
Original:  We also introduce the notion of  recoverability, implicit in many previous analyses of imitation learning, which allows us to cleanly delineate how well each algorithmic family is able to mitigate compounding errors.
Revised:  We also introduce the notion of moment recoverability, implicit in many previous analyses of imitation learning, which allows us to cleanly delineate how well each algorithmic family is able to mitigate compounding errors.
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Original:  However, at some point in the future, any training data point z \in S can request to be unlearned, thus prompting the learner to modify its output predictor while still ensuring the same accuracy guarantees.
Revised:  However, at some point in the future, any training datapoint z \in S can request to be unlearned, thus prompting the learner to modify its output predictor while still ensuring the same accuracy guarantees.
Edit intent: fluency
Original:  However, at some point in the future, any training data point z \in S can request to be unlearned, thus prompting the learner to modify its output predictor while still ensuring the same accuracy guarantees.
Revised:  However, at some point in the future, any training data point z \in S can request to be unlearned, thus prompting the learner to modify its output model while still ensuring the same accuracy guarantees.
Edit intent: clarity
Original:  In our work, we initiate a rigorous study of machine unlearning in the population setting , where the goal is to maintain performance on the unseen test loss. We then provide unlearning algorithms for convex loss functions .
Revised:  In our work, we initiate a rigorous study of machine unlearning in the population setting , where the goal is to perform well on previously unseen datapoints. Our focus is on both computational and storage complexity .
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Original:  \mbox{%DIFAUXCMD lee2020predicting that pretext-based self-supervised learning can effectively reduce the sample complexity of downstream tasks under Conditional Independence (CI) between the components of the pretext task conditional on the downstream label.
Revised:  It is proved that pretext-based self-supervised learning can effectively reduce the sample complexity of downstream tasks under Conditional Independence (CI) between the components of the pretext task conditional on the downstream label.
Edit intent: coherence
Original:  In this paper, we explore the idea of applying a learnable function to the input to make the CI condition hold . In particular, we first rigorously formulate the criteria that the function needs to satisfy. We then design an ingenious loss function for learningsuch a function and prove that the function minimizing the proposed loss satisfies the above criteria. We theoretically study the number of labeled data required, and give a model-free lower bound showing that taking limited downstream data will hurt the performance of self-supervised learning.
Revised:  In this paper, we explore the idea of applying a learnable function to the input to make the CI condition hold by using downstream data to refine the unlabeled data to boost self-supervised learning. At first glance, one might think that seeing downstream data in advance would always boost the downstream performance. However, we show that it is not intuitively true and point out that in some cases, it will hurt the performance of self-supervised learning.
Edit intent: coherence
Original:  We theoretically study the number of labeled data required, and give a model-free lower bound showing that taking limited downstream data will hurt the performance of self-supervised learning. Furthermore, we take the model structure into account and give a model-dependent lower bound, which gets higher when the model capacity gets larger . Moreover, we conduct several numerical experiments to verify our theoretical results.
Revised:  We theoretically study the number of labeled data required, and give a model-free lower bound showing that taking limited downstream data will hurt the final performance instead. In particular, we prove both model-free and model-dependent lower bound, which gets higher when the model capacity gets larger . Moreover, we conduct several numerical experiments to verify our theoretical results.
Edit intent: clarity
Original:  Furthermore, we take the model structure into account and give a model-dependent lower bound, which gets higher when the model capacity gets larger . Moreover, we conduct several numerical experiments to verify our theoretical results.
Revised:  Furthermore, we take the model structure into account and give a model-dependent lower bound, which gets higher when the model capacity gets larger . Moreover, we conduct several experiments on both synthetic and real-world datasets to verify our theoretical results.
Edit intent: clarity
Original:  This study aims to investigate the difference in the Electroencephalograph (EEG) signals of three groups of subjects: Normal Control (NC), Mild Cognitive Impairment (MCI), and Dementia (DEM).
Revised:  Recent progress has shown that Electroencephalography (EEG) signals of three groups of subjects: Normal Control (NC), Mild Cognitive Impairment (MCI), and Dementia (DEM).
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Original:  This study aims to investigate the difference in the Electroencephalograph (EEG) signals of three groups of subjects: Normal Control (NC), Mild Cognitive Impairment (MCI), and Dementia (DEM). Unlike previous works that focus on the diagnosis of Alzheimer's disease (AD) from EEG signals , we study the detection of dementia to generalize the classificationmodels to other types of dementia. We have developed a pilot studyon machine learning-based dementia diagnosis using EEG signals from four visual stimulation tasks (Fixation, Mental Imagery, Symbol Recognition, and Visually Evoked Related Potential) to identify the most suitable task and method to detect dementia using EEG signals. We extracted both frequency and time domain features from the EEG signals and applied a Support Vector Machine (SVM)for each domain to classify the patients using those extracted features.
Revised:  This study aims to investigate the difference in the Electroencephalograph (EEG) is the promising and cost-effective test to facilitate the detection of neurocognitive disorders. However, most of the existing works have been using only resting-state EEG. The efficiencies of EEG signals from various cognitive tasks, for dementia classification, have yet to be thoroughly investigated. In this study, we designed four cognitive tasks that engage different cognitive performances: attention, working memory, and applied a Support Vector Machine (SVM)for each domain to classify the patients using those extracted features.
Edit intent: coherence
Original:  We extracted both frequency and time domain features from the EEG signals and applied a Support Vector Machine (SVM)for each domain to classify the patients using those extracted features. Additionally, we study the feasibility of the Filter Bank Common Spatial Pattern (FBCSP) algorithm to extract features from the frequency domain to detect dementia.
Revised:  We extracted both frequency and time domain features from the EEG signals and executive function. We investigated these tasks by using statistical analysis on both time and frequency domains of EEG signals from three classes of human subjects: Dementia (DEM), Mild Cognitive Impairment (MCI), and Normal Control (NC). We also further evaluated the classification performances of two features extraction methods: Principal Component Analysis (PCA) and Filter Bank Common Spatial Pattern (FBCSP) algorithm to extract features from the frequency domain to detect dementia.
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Original:  Additionally, we study the feasibility of the Filter Bank Common Spatial Pattern (FBCSP) algorithm to extract features from the frequency domain to detect dementia. The evaluation of the model shows that the tasks that test the working memory are the most appropriate to detect dementia using EEG signals in both time and frequency domain analysis. However, the best results in both domains are obtained by combining features of all four cognitive tasks .
Revised:  Additionally, we study the feasibility of the Filter Bank Common Spatial Pattern (FBCSP) . We found that the working memory related tasks yielded good performances for dementia recognition in both cases using PCA and FBCSP. Moreover, FBCSP with features combination from four tasks revealed the best sensitivity of 0.87 and the specificity of 0.80. To our best knowledge, this is the first work that concurrently investigated several cognitive tasks for dementia recognition using both statistical analysis and classification scores. Our results give essential information to design and aid in conducting further experimental tasks to early diagnose dementia patients .
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Original: We explore the utility of harnessing auxiliary labels (e.g., facial expression) to impose ] ] geometric structure when training embedding models for one-shot learning (e. g., for face verification).
Revised: For the task of face verification, we explore the utility of harnessing auxiliary labels (e.g., facial expression) to impose ] ] geometric structure when training embedding models for one-shot learning (e. g., for face verification).
Edit intent: clarity
Original: We explore the utility of harnessing auxiliary labels (e.g., facial expression) to impose ] ] geometric structure when training embedding models for one-shot learning (e. g., for face verification).
Revised: We explore the utility of harnessing auxiliary facial emotion labels to impose explicit geometric constraints on the embedding space when training deep embedding models. We introduce several novel loss functions that, in conjunction with a standard Triplet Loss ] ] geometric structure when training embedding models for one-shot learning (e. g., for face verification).
Edit intent: meaning-changed
Original: We explore the utility of harnessing auxiliary labels (e.g., facial expression) to impose ] ] geometric structure when training embedding models for one-shot learning (e. g., for face verification).
Revised: We explore the utility of harnessing auxiliary labels (e.g., facial expression) to impose ], or ArcFace loss ] geometric structure when training embedding models for one-shot learning (e. g., for face verification).
Edit intent: meaning-changed
Original: We explore the utility of harnessing auxiliary labels (e.g., facial expression) to impose ] ] geometric structure when training embedding models for one-shot learning (e. g., for face verification). We introduce novel geometric constraints on the embedding space learned by a deep model using either manually annotated or automatically detected auxiliary  labels.
Revised: We explore the utility of harnessing auxiliary labels (e.g., facial expression) to impose ] ] geometric constraints on the embedding space learned by a deep model using either manually annotated or automatically detected auxiliary labels.
Edit intent: clarity
Original:  We contrast their performances (AUC) on four different face datasets (CK+, VGGFace-2, TuftsFace, and PubFig). Due to the additional structure encoded in the embedding space, our methods provide a higher verification accuracy (99.7, 86.2, 99.4, and 79.3\% with our proposed TL+PDP+FBV loss, versus 97.5, 72.6, 93.1, and 70.5\% using a standard Triplet Loss on the four datasets, respectively) .
Revised:  We contrast their performances (AUC) on four different face datasets (CK+, PubFig, Tufts, VggFace2, LFW, YTF, MegaFace, IJB-B, IJB-C). Our results consistently show that the additional structure encoded in the embedding space, our methods provide a higher verification accuracy (99.7, 86.2, 99.4, and 79.3\% with our proposed TL+PDP+FBV loss, versus 97.5, 72.6, 93.1, and 70.5\% using a standard Triplet Loss on the four datasets, respectively) .
Edit intent: clarity
Original:  Due to the additional structure encoded in the embedding space, our methods provide a higher verification accuracy (99.7, 86.2, 99.4, and 79.3\% with our proposed TL+PDP+FBV loss, versus 97.5, 72.6, 93.1, and 70.5\% using a standard Triplet Loss on the four datasets, respectively) .
Revised:  Due to the additional structure encoded about each face's emotions results in an embedding model that gives higher verification accuracy (99.7, 86.2, 99.4, and 79.3\% with our proposed TL+PDP+FBV loss, versus 97.5, 72.6, 93.1, and 70.5\% using a standard Triplet Loss on the four datasets, respectively) .
Edit intent: clarity
Original:  Due to the additional structure encoded in the embedding space, our methods provide a higher verification accuracy (99.7, 86.2, 99.4, and 79.3\% with our proposed TL+PDP+FBV loss, versus 97.5, 72.6, 93.1, and 70.5\% using a standard Triplet Loss on the four datasets, respectively) .
Revised:  Due to the additional structure encoded in the embedding space, our methods provide a higher verification accuracy than training with just ArcFace or Triplet Loss alone. Moreover, the method is significantly more accurate than a simple multi-task learning approach .
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Original:  We analyze the performance of our methods on nine different (CK+, PubFig, Tufts, VggFace2, LFW, YTF, MegaFace, IJB-B, IJB-C). Our results consistently show that the additional structure encoded about each face's emotions results in an embedding model that gives higher verification accuracy than training with just ArcFace or Triplet Loss alone. Moreover, the method is significantly more accurate than a simple multi-task learning approach.
Edit intent: coherence
Original:  In this work, we propose a new graph-based model that learns to extend a given partial molecule by flexibly choosing between adding individual atoms and entire fragments. Extending a scaffold is implemented by using it as the initial partial graph , which is possible because our model does not depend on generation history.
Revised:  In this work, we propose a new graph-based model that naturally supports scaffolds as initial seed of the generative procedure , which is possible because our model does not depend on generation history.
Edit intent: clarity
Original:   We show that training using a randomized generation order is necessary for good performance when extending scaffolds, and that the results are further improved by increasing  fragment vocabulary size.
Revised:  At the same time, our generation procedure can flexibly choose between adding individual atoms and entire fragments. We show that training using a randomized generation order is necessary for good performance when extending scaffolds, and that the results are further improved by increasing fragment vocabulary size.
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Original:  We demonstrate that this allocation scheme improves performance in  conditional image generation, resulting in new state-of-the-art  likelihood values on binarized MNIST (<=41.58 nats/image) , binarized Omniglot (<=66.24 nats/image), as well as presenting competitive performance on CIFAR10, DMLab Mazes, Celeb-A and ImageNet32x32.
Revised:  We demonstrate that this allocation scheme improves performance in memory conditional image generation, resulting in new state-of-the-art likelihood values on binarized MNIST (<=41.58 nats/image) , binarized Omniglot (<=66.24 nats/image), as well as presenting competitive performance on CIFAR10, DMLab Mazes, Celeb-A and ImageNet32x32.
Edit intent: meaning-changed
Original:  We demonstrate that this allocation scheme improves performance in  conditional image generation, resulting in new state-of-the-art  likelihood values on binarized MNIST (<=41.58 nats/image) , binarized Omniglot (<=66.24 nats/image), as well as presenting competitive performance on CIFAR10, DMLab Mazes, Celeb-A and ImageNet32x32.
Revised:  We demonstrate that this allocation scheme improves performance in conditional image generation, resulting in new state-of-the-art conditional likelihood values on binarized MNIST (<=41.58 nats/image) , binarized Omniglot (<=66.24 nats/image), as well as presenting competitive performance on CIFAR10, DMLab Mazes, Celeb-A and ImageNet32x32.
Edit intent: fluency
Original:  When the input is the WMG, to our knowledge, the fastest algorithm for computing all possible winners in the Schulze method uses a folklore reduction to the All-Pairs Bottleneck Paths (APBP) problem and runs in O(m^{2.69}) time, where m is the number of candidates.
Revised:  When the input is the WMG, to our knowledge, the fastest algorithm for computing all winners in the Schulze method uses a folklore reduction to the All-Pairs Bottleneck Paths (APBP) problem and runs in O(m^{2.69}) time, where m is the number of candidates.
Edit intent: clarity
Original:  When the input is the WMG, to our knowledge, the fastest algorithm for computing all possible winners in the Schulze method uses a folklore reduction to the All-Pairs Bottleneck Paths (APBP) problem and runs in O(m^{2.69}) time, where m is the number of candidates.
Revised:  When the input is the WMG, to our knowledge, the fastest algorithm for computing all possible winners in the Schulze method uses a folklore reduction to the All-Pairs Bottleneck Paths problem and runs in O(m^{2.69}) time, where m is the number of candidates.
Edit intent: clarity
Original:  in the special case when there are  O(m) voters and candidates , the running time becomes O(m^{2.69}), or O(m^{2.5}) if there is a nearly-linear time algorithm for multiplying dense square matrices.
Revised:  in the special case when there are m candidates and n= O(m) voters and candidates , the running time becomes O(m^{2.69}), or O(m^{2.5}) if there is a nearly-linear time algorithm for multiplying dense square matrices.
Edit intent: meaning-changed
Original:  in the special case when there are  O(m) voters and candidates , the running time becomes O(m^{2.69}), or O(m^{2.5}) if there is a nearly-linear time algorithm for multiplying dense square matrices.
Revised:  in the special case when there are O(m) voters , the running time becomes O(m^{2.69}), or O(m^{2.5}) if there is a nearly-linear time algorithm for multiplying dense square matrices.
Edit intent: clarity
Original:  in the special case when there are  O(m) voters and candidates , the running time becomes O(m^{2.69}), or O(m^{2.5}) if there is a nearly-linear time algorithm for multiplying dense square matrices.
Revised:  in the special case when there are O(m) voters and candidates , the running time is O(m^{2.69}), or O(m^{2.5}) if there is a nearly-linear time algorithm for multiplying dense square matrices.
Edit intent: clarity