wanyu/IteraTeR_v2 huggingface dataset:
Original:  We develop the first efficient single pass streaming algorithm, Streaming Local Search, with constant factor (4p-1) / 4p(8p \big( + 2d-1)+1/\alpha +2d(1+\alpha)}\big) approximation guarantee for maximizing a non-monotone submodular function under a p-system and d knapsack constraints , with a memory independent of data size.
Revised:  We develop the first efficient single pass streaming algorithm, Streaming Local Search, with constant factor (4p-1) / 4p(8p \big(1 + 2d-1)+1/\alpha +2d(1+\alpha)}\big) approximation guarantee for maximizing a non-monotone submodular function under a p-system and d knapsack constraints , with a memory independent of data size.
Edit intent: fluency
Original:  In our experiments , we show that for the video summarizationproblem, our streaming method , while achieving practically the same performance, runs more than 1700 times faster than previous work  .
Revised:  In our experiments , we show that for video summarization, our method runs more than 1700 times faster than previous work .
Edit intent: clarity
Original:  We develop the first efficient single pass streaming algorithm, Streaming Local Search, that for any streaming monotone submodular maximization algorithm with approximation guarantee \alpha under a collection of independence systems \mathcal{I {\cal  , provides a constant 1/\big(1+2/\alpha+1/\alpha +2d(1+\alpha)\big) approximation guarantee for maximizing a non-monotone submodular function under the intersection of \mathcal{I {\cal  and d knapsack constraints.
Revised:  We develop the first efficient single pass streaming algorithm, Streaming Local Search, that for any streaming monotone submodular maximization algorithm with approximation guarantee \alpha under a collection of independence systems {\cal , provides a constant 1/\big(1+2/\alpha+1/\alpha +2d(1+\alpha)\big) approximation guarantee for maximizing a non-monotone submodular function under the intersection of \mathcal{I {\cal and d knapsack constraints.
Edit intent: fluency
Original:  We develop the first efficient single pass streaming algorithm, Streaming Local Search, that for any streaming monotone submodular maximization algorithm with approximation guarantee \alpha under a collection of independence systems \mathcal{I {\cal  , provides a constant 1/\big(1+2/\alpha+1/\alpha +2d(1+\alpha)\big) approximation guarantee for maximizing a non-monotone submodular function under the intersection of \mathcal{I {\cal  and d knapsack constraints.
Revised:  We develop the first efficient single pass streaming algorithm, Streaming Local Search, that for any streaming monotone submodular maximization algorithm with approximation guarantee \alpha under a collection of independence systems \mathcal{I {\cal I , provides a constant 1/\big(1+2/\alpha+1/\alpha +2d(1+\alpha)\big) approximation guarantee for maximizing a non-monotone submodular function under the intersection of \mathcal{I {\cal and d knapsack constraints.
Edit intent: fluency
Original:  We develop the first efficient single pass streaming algorithm, Streaming Local Search, that for any streaming monotone submodular maximization algorithm with approximation guarantee \alpha under a collection of independence systems \mathcal{I {\cal  , provides a constant 1/\big(1+2/\alpha+1/\alpha +2d(1+\alpha)\big) approximation guarantee for maximizing a non-monotone submodular function under the intersection of \mathcal{I {\cal  and d knapsack constraints.
Revised:  We develop the first efficient single pass streaming algorithm, Streaming Local Search, that for any streaming monotone submodular maximization algorithm with approximation guarantee \alpha under a collection of independence systems \mathcal{I {\cal , provides a constant 1/\big(1+2/\alpha+1/\alpha +2d(1+\alpha)\big) approximation guarantee for maximizing a non-monotone submodular function under the intersection of {\cal and d knapsack constraints.
Edit intent: fluency
Original:  We develop the first efficient single pass streaming algorithm, Streaming Local Search, that for any streaming monotone submodular maximization algorithm with approximation guarantee \alpha under a collection of independence systems \mathcal{I {\cal  , provides a constant 1/\big(1+2/\alpha+1/\alpha +2d(1+\alpha)\big) approximation guarantee for maximizing a non-monotone submodular function under the intersection of \mathcal{I {\cal  and d knapsack constraints.
Revised:  We develop the first efficient single pass streaming algorithm, Streaming Local Search, that for any streaming monotone submodular maximization algorithm with approximation guarantee \alpha under a collection of independence systems \mathcal{I {\cal , provides a constant 1/\big(1+2/\alpha+1/\alpha +2d(1+\alpha)\big) approximation guarantee for maximizing a non-monotone submodular function under the intersection of \mathcal{I {\cal I and d knapsack constraints.
Edit intent: fluency
Original:  In this paper, we characterize two novel camera systems that employ acceleration techniques to push the extremes of energy and performance scaling, and explore the computation-communication tradeoffs in their designs .
Revised:  In this paper, we characterize two novel camera systems that use acceleration techniques to push the extremes of energy and performance scaling, and explore the computation-communication tradeoffs in their designs .
Edit intent: fluency
Original:  In this paper, we characterize two novel camera systems that employ acceleration techniques to push the extremes of energy and performance scaling, and explore the computation-communication tradeoffs in their designs .
Revised:  In this paper, we characterize two novel camera systems that employ acceleration techniques to push the extremes of energy and performance scaling, and explore the computation-communication tradeoffs in their design .
Edit intent: fluency
Original:  The first case study is a camera system designed to detect and authenticate individual faces, running solely on energy harvested from RFID readers.
Revised:  The first case study targets a camera system designed to detect and authenticate individual faces, running solely on energy harvested from RFID readers.
Edit intent: clarity
Original:  The second camera system is a 16-camera rig processing over 32 Gb/s of data to produce real-time 3D-360 degree virtual reality video.
Revised:  The second camera system supports a 16-camera rig processing over 32 Gb/s of data to produce real-time 3D-360 degree virtual reality video.
Edit intent: clarity
Original:  We design a multi-FPGA processing pipeline that outperforms CPU and GPU configurations by up to 10\times in the computation time, producing panoramic stereo video directly from the camera rig at 30 frames per second.
Revised:  We design a multi-FPGA processing pipeline that outperforms CPU and GPU configurations by up to 10x in computation time, producing panoramic stereo video directly from the camera rig at 30 frames per second.
Edit intent: fluency
Original: We give a complete characterization of receptive field codes realizable by connected receptive fields and their minimal embedding dimensions  .
Revised: We give a complete characterization of receptive field codes realizable by connected receptive fields and we give the minimal embedding dimensions .
Edit intent: coherence
Original: We give a complete characterization of receptive field codes realizable by connected receptive fields and their minimal embedding dimensions  .
Revised: We give a complete characterization of receptive field codes realizable by connected receptive fields and their minimal embedding dimensions of these codes .
Edit intent: clarity
Original:  In particular, we show that all connected codes are realizable in dimension at most three.
Revised:  In particular, we show that all connected codes are realizable in dimension at most 3.
Edit intent: fluency
Original:  When the system is not observable, an important and practical problem is how to add a minimal number of sensors so that the system becomes observable.
Revised:  When the system is not observable, an important and practical problem is how to add a minimal number of sensors so that the system becomes observable.
Edit intent: fluency
Original:  We propose a seamless way to personalize RNN models with cross-session information transfer and devise a hierarchical RNN model that relies end evolves latent hidden states of the RNN's across user sessions.
Revised:  We propose a seamless way to personalize RNN models with cross-session information transfer and devise a hierarchical RNN model that relays end evolves latent hidden states of the RNN's across user sessions.
Edit intent: clarity
Original:   Recurrent Neural Networks have recently been shown to perform very well in session-based settings.
Revised:  Recently, Recurrent Neural Networks have recently been shown to perform very well in session-based settings.
Edit intent: coherence
Original:   Recurrent Neural Networks have recently been shown to perform very well in session-based settings.
Revised:  Recurrent Neural Networks have been shown to perform very well in session-based settings.
Edit intent: clarity
Original:  We propose a seamless way to personalize RNN models with cross-session information transfer and devise a hierarchical RNN model that relays end evolves latent hidden states of the RNN's across user sessions.
Revised:  We propose a seamless way to personalize RNN models with cross-session information transfer and devise a Hierarchical RNN model that relays end evolves latent hidden states of the RNN's across user sessions.
Edit intent: fluency
Original:  We propose a seamless way to personalize RNN models with cross-session information transfer and devise a hierarchical RNN model that relays end evolves latent hidden states of the RNN's across user sessions.
Revised:  We propose a seamless way to personalize RNN models with cross-session information transfer and devise a hierarchical RNN model that relays end evolves latent hidden states of the RNNs across user sessions.
Edit intent: fluency
Original:  Results on two industry datasets show large improvements over the session-only RNN's .
Revised:  Results on two industry datasets show large improvements over the session-only RNNs .
Edit intent: fluency
Original: Generative adversarial networks are an effective approach for learning rich latent representations of continuous data, but have proven difficult to apply directly to discrete structured data , such as text sequences or discretized images .
Revised: Generative adversarial networks are an effective approach for learning rich latent representations of continuous data, but have proven difficult to apply directly to discrete structured data , such as text sequence or discretized images .
Edit intent: fluency
Original: While autoencoders are a key technique in representation learning for continuous structures, such as images or wave forms, developing general-purpose autoencoders for discrete structures, such as text sequence or discretized images, has proven to be more challenging.
Revised: While autoencoders are a key technique for representation learning of continuous structures. However, applying similar methods to discrete structures, such as text sequence or discretized images, has proven to be more challenging.
Edit intent: clarity
Original: While autoencoders are a key technique in representation learning for continuous structures, such as images or wave forms, developing general-purpose autoencoders for discrete structures, such as text sequence or discretized images, has proven to be more challenging.
Revised: While autoencoders are a key technique in representation learning for continuous structures, such as images or wave forms, developing general-purpose autoencoders for discrete structures, such as text sequences or discretized images, has proven to be more challenging.
Edit intent: fluency
Original: We consider the problem of selecting a portfolio of items of fixed cardinality where the goal is to have at least one item achieve a high return , which we refer to as winning.
Revised: We consider the problem of selecting a portfolio of items of fixed cardinality where the goal is to maximize the probability of having at least one item achieve a high return , which we refer to as winning.
Edit intent: clarity
Original:  We show that a greedy solution to this problem has strong performance guarantees , even under arbitrary correlations between the items .
Revised:  We show that a greedy solution has strong performance guarantees , even under arbitrary correlations between the items .
Edit intent: clarity
Original:  We show that a greedy solution to this problem has strong performance guarantees , even under arbitrary correlations between the items .
Revised:  We show that a greedy solution to this problem has strong performance guarantees .
Edit intent: clarity
Original:  To overscale DNN weight SRAMs , MATIC combines specific characteristics of destructive SRAM reads with the error resilience of neural networks in a memory-adaptive training process.
Revised:  To overscale DNN weight SRAMs , MATIC combines the characteristics of destructive SRAM reads with the error resilience of neural networks in a memory-adaptive training process.
Edit intent: clarity
Original:   PVT-related voltage margins are eliminated using bit-cells from synaptic weights as in-situ canaries to track runtime environmental variation.
Revised:  Furthermore, PVT-related voltage margins are eliminated using bit-cells from synaptic weights as in-situ canaries to track runtime environmental variation.
Edit intent: coherence
Original:  Demonstrated on a low-power DNN accelerator fabricated in 65nm CMOS, MATIC enables up to  3.3x total energy reduction  , or 18.6x application error reduction.
Revised:  Demonstrated on a low-power DNN accelerator that we fabricate in 65 nm CMOS, MATIC enables up to 3.3x total energy reduction , or 18.6x application error reduction.
Edit intent: clarity
Original: In this paper, we first present a novel structure of AutoEncoder, namely Semi-AutoEncoder  .
Revised: In this paper, we present a novel structure of AutoEncoder, namely Semi-AutoEncoder .
Edit intent: coherence
Original: In this paper, we first present a novel structure of AutoEncoder, namely Semi-AutoEncoder  .
Revised: In this paper, we first present a novel structure , Semi-AutoEncoder .
Edit intent: coherence
Original:  We generalize it into a designated hybrid collaborative filtering model , which is able to predict ratings as well as to generate personalized top-N recommendations.
Revised:  We generalize it into a hybrid collaborative filtering model , which is able to predict ratings as well as to generate personalized top-N recommendations.
Edit intent: clarity
Original:  We generalize it into a designated hybrid collaborative filtering model , which is able to predict ratings as well as to generate personalized top-N recommendations.
Revised:  We generalize it into a designated hybrid collaborative filtering model for rating prediction as well as to generate personalized top-N recommendations.
Edit intent: clarity
Original:  We generalize it into a designated hybrid collaborative filtering model , which is able to predict ratings as well as to generate personalized top-N recommendations.
Revised:  We generalize it into a designated hybrid collaborative filtering model , which is able to predict ratings as well as personalized top-n recommendations.
Edit intent: coherence
Original:  Our key insight is that the neighborhood of a point on a shape is effectively captured at multiple scales by a succession of progressively zoomed out views, taken from care fully selected camera positions.
Revised:  Our key insight is that the neighborhood of a point on a shape that are captured at multiple scales by a succession of progressively zoomed out views, taken from care fully selected camera positions.
Edit intent: clarity
Original:  Our key insight is that the neighborhood of a point on a shape is effectively captured at multiple scales by a succession of progressively zoomed out views, taken from care fully selected camera positions.
Revised:  Our key insight is that the neighborhood of a point on a shape is effectively captured at multiple scales by a succession of progressively zoomed out views, taken from carefully selected camera positions.
Edit intent: clarity
Original:  To train our network, we leverage two extremely large sources of data  .
Revised:  To train our network, we leverage two extremely large sources of data to train our network .
Edit intent: coherence
Original:  Second, we automatically generate a synthetic dense  correspondence dataset by part-aware, non-rigid alignment of a massive collection of 3D models.
Revised:  Second, we automatically generate a synthetic dense correspondence dataset by non-rigid alignment of a massive collection of 3D models.
Edit intent: clarity
Original:  As a result of these design choices, our view-based architecture effectively encodes multi-scale local context and fine-grained surface detail.
Revised:  As a result of these design choices, our network effectively encodes multi-scale local context and fine-grained surface detail.
Edit intent: clarity
Original:   We demonstrate through several experiments that our learned local descriptors are more general and robust compared to state of the art alternatives, and have a variety of applications without any additional fine-tuning .
Revised:  We demonstrate through several experiments that our learned local descriptors are more general and robust compared to state of the art alternatives, and are effective in a variety of applications without any additional fine-tuning .
Edit intent: clarity
Original: We propose several neural models arranged in a two-stage framework to tackle question generation from documents.
Revised: We propose a two-stage framework to tackle question generation from documents.
Edit intent: clarity
Original: We propose several neural models arranged in a two-stage framework to tackle question generation from documents.
Revised: We propose several neural models arranged in a two-stage neural model to tackle question generation from documents.
Edit intent: clarity
Original:  First, we estimate the probability of "interesting" answers in a document using a neural model trained on a question-answering corpus.
Revised:  Our model first estimates the probability that word sequences in a document compose "interesting" answers in a document using a neural model trained on a question-answering corpus.
Edit intent: clarity
Original:  First, we estimate the probability of "interesting" answers in a document using a neural model trained on a question-answering corpus.
Revised:  First, we estimate the probability of "interesting" answers using a neural model trained on a question-answering corpus.
Edit intent: clarity
Original:  The predicted key phrases are then used as answers to condition a sequence-to-sequence question generation model  . Empirically, our neural key phrase detection models significantly outperform an entity-tagging baseline system  .
Revised:  We thus take a data-driven approach to interestingness. Predicted key phrases then act as target answers that condition a sequence-to-sequence question generation model . Empirically, our neural key phrase detection models significantly outperform an entity-tagging baseline system .
Edit intent: clarity
Original:  The predicted key phrases are then used as answers to condition a sequence-to-sequence question generation model  . Empirically, our neural key phrase detection models significantly outperform an entity-tagging baseline system  .
Revised:  The predicted key phrases are then used as answers to condition a sequence-to-sequence question generation model . Empirically, our neural key phrase detection model significantly outperforms an entity-tagging baseline system .
Edit intent: fluency
Original:  The resulting questions and answers can be used to assess reading comprehension in educational settings .
Revised:  The resulting questions and answers can be used to assess reading comprehension and also as a data augmentation technique for semi-supervised learning .
Edit intent: clarity
Original:  Our model first estimates the probability that word sequences in a document compose "interesting" answers using a neural model trained on a question-answering corpus.
Revised:  First, our model estimates the probability that word sequences in a document compose "interesting" answers using a neural model trained on a question-answering corpus.
Edit intent: coherence
Original:  Our model first estimates the probability that word sequences in a document compose "interesting" answers using a neural model trained on a question-answering corpus.
Revised:  Our model first estimates the probability that word sequences in a document are ones that a human would pick when selecting candidate answers by training a neural key-phrase extractor on the answers in a question-answering corpus.
Edit intent: clarity
Original:  We thus take a data-driven approach to interestingness.
Edit intent: coherence
Original:  Predicted key phrases then act as target answers that condition a sequence-to-sequence question generation model with a copy mechanism.
Revised:  Predicted key phrases then act as target answers and condition a sequence-to-sequence question generation model with a copy mechanism.
Edit intent: clarity
Original:  Predicted key phrases then act as target answers that condition a sequence-to-sequence question generation model with a copy mechanism.
Revised:  Predicted key phrases then act as target answers that condition a sequence-to-sequence question-generation model with a copy mechanism.
Edit intent: fluency
Original:  Empirically, our neural key phrase detection model significantly outperforms an entity-tagging baseline system and existing rule-based approaches.
Revised:  Empirically, our neural key phrase detection model significantly outperforms an entity-tagging baseline and existing rule-based approaches.
Edit intent: clarity
Original: Visualizing the information available to a human observer in a single glance at an image provides a powerful tool for evaluating models of full-field human vision .
Revised: Foveal vision makes up less than 1\% of the visual field. The other 99\% is peripheral vision. Precisely what human beings see in the periphery is both obvious and mysterious in that we see it with our own eyes but can't visualize what we see, except in controlled lab experiments .
Edit intent: coherence
Original:  The hard part is human-realistic visualization of the periphery . Degradation of information with distance from fixation is far more complex than a mere reduction of acuity that might be mimicked using blur with a standard deviation that linearly increases with eccentricity .
Revised:  Degradation of information with distance from fixation is far more complex than a mere reduction of acuity that might be mimicked using blur with a standard deviation that linearly increases with eccentricity .
Edit intent: coherence
Original:  The hard part is human-realistic visualization of the periphery . Degradation of information with distance from fixation is far more complex than a mere reduction of acuity that might be mimicked using blur with a standard deviation that linearly increases with eccentricity .
Revised:  The hard part is human-realistic visualization of the periphery . Degradation of information in the periphery is far more complex than a mere reduction of acuity that might be mimicked using blur with a standard deviation that linearly increases with eccentricity .
Edit intent: clarity
Original:  The hard part is human-realistic visualization of the periphery . Degradation of information with distance from fixation is far more complex than a mere reduction of acuity that might be mimicked using blur with a standard deviation that linearly increases with eccentricity .
Revised:  The hard part is human-realistic visualization of the periphery . Degradation of information with distance from fixation is far more complex than what might be mimicked using blur with a standard deviation that linearly increases with eccentricity .
Edit intent: clarity