wanyu/IteraTeR_full_sent huggingface dataset:
Original: Machine learning is experiencing an explosion of software and hardware solutions, and needs industry-standard performance benchmarks to drive design and enable competitive evaluation . However, machine learning training presents a number of unique challenges to benchmarking that do not exist in other domains: (1) some optimizations that improve training throughput actually increase time to solution, (2) training is stochastic and time to solution has high variance, and (3) the software and hardware systems are so diverse that they cannot be fairly benchmarked with the same binary, code, or even hyperparameters .
Revised: Machine learning is experiencing an explosion of software and hardware solutions, and needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But ML training presents three unique benchmarking challenges absent from other domains: (1) some optimizations that improve training throughput actually increase time to solution, (2) training is stochastic and time to solution has high variance, and (3) the software and hardware systems are so diverse that they cannot be fairly benchmarked with the same binary, code, or even hyperparameters .
Edit intent: clarity
Original:  However, machine learning training presents a number of unique challenges to benchmarking that do not exist in other domains: (1) some optimizations that improve training throughput actually increase time to solution, (2) training is stochastic and time to solution has high variance, and (3) the software and hardware systems are so diverse that they cannot be fairly benchmarked with the same binary, code, or even hyperparameters .
Revised:  However, machine learning training presents a number of unique challenges to benchmarking that do not exist in other domains: optimizations that improve training throughput actually increase time to solution, (2) training is stochastic and time to solution has high variance, and (3) the software and hardware systems are so diverse that they cannot be fairly benchmarked with the same binary, code, or even hyperparameters .
Edit intent: clarity
Original:  However, machine learning training presents a number of unique challenges to benchmarking that do not exist in other domains: (1) some optimizations that improve training throughput actually increase time to solution, (2) training is stochastic and time to solution has high variance, and (3) the software and hardware systems are so diverse that they cannot be fairly benchmarked with the same binary, code, or even hyperparameters .
Revised:  However, machine learning training presents a number of unique challenges to benchmarking that do not exist in other domains: (1) some optimizations that improve training throughput can increase the time to solution, (2) training is stochastic and time to solution has high variance, and (3) the software and hardware systems are so diverse that they cannot be fairly benchmarked with the same binary, code, or even hyperparameters .
Edit intent: clarity
Original:  However, machine learning training presents a number of unique challenges to benchmarking that do not exist in other domains: (1) some optimizations that improve training throughput actually increase time to solution, (2) training is stochastic and time to solution has high variance, and (3) the software and hardware systems are so diverse that they cannot be fairly benchmarked with the same binary, code, or even hyperparameters .
Revised:  However, machine learning training presents a number of unique challenges to benchmarking that do not exist in other domains: (1) some optimizations that improve training throughput actually increase time to solution, training is stochastic and time to solution has high variance, and (3) the software and hardware systems are so diverse that they cannot be fairly benchmarked with the same binary, code, or even hyperparameters .
Edit intent: fluency
Original:  However, machine learning training presents a number of unique challenges to benchmarking that do not exist in other domains: (1) some optimizations that improve training throughput actually increase time to solution, (2) training is stochastic and time to solution has high variance, and (3) the software and hardware systems are so diverse that they cannot be fairly benchmarked with the same binary, code, or even hyperparameters .
Revised:  However, machine learning training presents a number of unique challenges to benchmarking that do not exist in other domains: (1) some optimizations that improve training throughput actually increase time to solution, (2) training is stochastic and time to solution exhibits high variance, and (3) the software and hardware systems are so diverse that they cannot be fairly benchmarked with the same binary, code, or even hyperparameters .
Edit intent: clarity
Original:  However, machine learning training presents a number of unique challenges to benchmarking that do not exist in other domains: (1) some optimizations that improve training throughput actually increase time to solution, (2) training is stochastic and time to solution has high variance, and (3) the software and hardware systems are so diverse that they cannot be fairly benchmarked with the same binary, code, or even hyperparameters .
Revised:  However, machine learning training presents a number of unique challenges to benchmarking that do not exist in other domains: (1) some optimizations that improve training throughput actually increase time to solution, (2) training is stochastic and time to solution has high variance, and software and hardware systems are so diverse that they cannot be fairly benchmarked with the same binary, code, or even hyperparameters .
Edit intent: coherence
Original:  However, machine learning training presents a number of unique challenges to benchmarking that do not exist in other domains: (1) some optimizations that improve training throughput actually increase time to solution, (2) training is stochastic and time to solution has high variance, and (3) the software and hardware systems are so diverse that they cannot be fairly benchmarked with the same binary, code, or even hyperparameters .
Revised:  However, machine learning training presents a number of unique challenges to benchmarking that do not exist in other domains: (1) some optimizations that improve training throughput actually increase time to solution, (2) training is stochastic and time to solution has high variance, and (3) the software and hardware systems are so diverse that fair benchmarking with the same binary, code, or even hyperparameters .
Edit intent: coherence
Original:  However, machine learning training presents a number of unique challenges to benchmarking that do not exist in other domains: (1) some optimizations that improve training throughput actually increase time to solution, (2) training is stochastic and time to solution has high variance, and (3) the software and hardware systems are so diverse that they cannot be fairly benchmarked with the same binary, code, or even hyperparameters . We present MLPerf, a machine learning benchmark that overcomes these challenges.
Revised:  However, machine learning training presents a number of unique challenges to benchmarking that do not exist in other domains: (1) some optimizations that improve training throughput actually increase time to solution, (2) training is stochastic and time to solution has high variance, and (3) the software and hardware systems are so diverse that they cannot be fairly benchmarked with the same binary, code, and even hyperparameters is difficult. We therefore present MLPerf, a machine learning benchmark that overcomes these challenges.
Edit intent: coherence
Original:  This question depends on the complexity of the visual input, the set of transforms applied to this multivariate input, and the noise of the sensors in the considered layer . Separate subsystems (e.g. opponent channels, spatial filters, nonlinearities of the texture sensors) have been suggested to URLanized for optimal information transmission.
Revised:  . Separate subsystems (e.g. opponent channels, spatial filters, nonlinearities of the texture sensors) have been suggested to URLanized for optimal information transmission.
Edit intent: clarity
Original:  Specifically, we propose an empirical estimate of the capacity of the system based on a recent Gaussianization technique that reduces the (challenging ) multivariate PDF estimation problem to a set of (easy) univariate estimations.
Revised:  Specifically, we propose an empirical estimate of the information transmitted by the system based on a recent Gaussianization technique that reduces the (challenging ) multivariate PDF estimation problem to a set of (easy) univariate estimations.
Edit intent: clarity
Original:  Specifically, we propose an empirical estimate of the capacity of the system based on a recent Gaussianization technique that reduces the (challenging ) multivariate PDF estimation problem to a set of (easy) univariate estimations.
Revised:  Specifically, we propose an empirical estimate of the capacity of the system based on a recent Gaussianization technique that reduces the challenging multivariate PDF estimation problem to a set of (easy) univariate estimations.
Edit intent: fluency
Original:  Specifically, we propose an empirical estimate of the capacity of the system based on a recent Gaussianization technique that reduces the (challenging ) multivariate PDF estimation problem to a set of (easy) univariate estimations.
Revised:  Specifically, we propose an empirical estimate of the capacity of the system based on a recent Gaussianization technique that reduces the (challenging ) multivariate PDF estimation problem to a set of simpler univariate estimations.
Edit intent: clarity
Original:  Results show that (1) the proposed estimator is consistent with theoretical predictions for total correlation, ( 2 ) the capacity increases substantially along the layers of the model, and (3) the capacity of the cortical representation follows the PDF of natural scenes over the chromatic and achromatic dimensions of the stimulus space .
Revised:  Total correlation measured using the proposed estimator is consistent with theoretical predictions for total correlation, ( 2 ) the capacity increases substantially along the layers of the model, and (3) the capacity of the cortical representation follows the PDF of natural scenes over the chromatic and achromatic dimensions of the stimulus space .
Edit intent: clarity
Original:  Results show that (1) the proposed estimator is consistent with theoretical predictions for total correlation, ( 2 ) the capacity increases substantially along the layers of the model, and (3) the capacity of the cortical representation follows the PDF of natural scenes over the chromatic and achromatic dimensions of the stimulus space .
Revised:  Results show that (1) the proposed estimator is consistent with predictions based on the analytical Jacobian of a standard spatio-chromatic model of the retina-cortex pathway. If the noise at certain representation is proportional to the dynamic range of the response, and one assumes sensors of equivalent noise level, transmitted information shows the following trends: (1) progressively deeper representations are better in terms of the amount of information about the input, ( 2 ) the capacity increases substantially along the layers of the model, and (3) the capacity of the cortical representation follows the PDF of natural scenes over the chromatic and achromatic dimensions of the stimulus space .
Edit intent: meaning-changed
Original:  Results show that (1) the proposed estimator is consistent with theoretical predictions for total correlation, ( 2 ) the capacity increases substantially along the layers of the model, and (3) the capacity of the cortical representation follows the PDF of natural scenes over the chromatic and achromatic dimensions of the stimulus space .
Revised:  Results show that (1) the proposed estimator is consistent with theoretical predictions for total correlation, ( 2 ) the transmitted information up to the cortical representation follows the PDF of natural scenes over the chromatic and achromatic dimensions of the stimulus space .
Edit intent: clarity
Original:  Results show that (1) the proposed estimator is consistent with theoretical predictions for total correlation, ( 2 ) the capacity increases substantially along the layers of the model, and (3) the capacity of the cortical representation follows the PDF of natural scenes over the chromatic and achromatic dimensions of the stimulus space . In theoretical neuroscience, the proposed capacity measure based on Gaussianization is useful to check the Efficient Coding Hypothesis and to assess the relative relevance of the chromatic and spatial elements of the models. From an applied perspective, in image compression, the proposed measure suggest which visual features should be addressed first; and in image quality, the proposed measure can generalize the visual information fidelity concept .
Revised:  Results show that (1) the proposed estimator is consistent with theoretical predictions for total correlation, ( 2 ) the capacity increases substantially along the layers of the model, and (3) the capacity of the cortical representation follows the PDF of natural scenes over the chromatic and achromatic dimensions of the stimulus space , (3) the contribution of spatial transforms to capture visual information is substantially bigger than the contribution of chromatic transforms, and (4) nonlinearities of the responses contribute substantially to the transmitted information but less than the linear transforms .
Edit intent: meaning-changed
Original:  As the backward weights become correct, this improves learning performance over feedback alignment on tasks such as Fashion-MNIST and CIFAR-10  . Our results demonstrate that a simple learning rule in a spiking network can allow neurons to produce the right backward connections and thus solve the weight transport problem.
Revised:  As the backward weights become correct, this improves learning performance over feedback alignment on tasks such as Fashion-MNIST , SVHN, CIFAR-10 . Our results demonstrate that a simple learning rule in a spiking network can allow neurons to produce the right backward connections and thus solve the weight transport problem.
Edit intent: meaning-changed
Original:  As the backward weights become correct, this improves learning performance over feedback alignment on tasks such as Fashion-MNIST and CIFAR-10  . Our results demonstrate that a simple learning rule in a spiking network can allow neurons to produce the right backward connections and thus solve the weight transport problem.
Revised:  As the backward weights become correct, this improves learning performance over feedback alignment on tasks such as Fashion-MNIST and CIFAR-10 and VOC . Our results demonstrate that a simple learning rule in a spiking network can allow neurons to produce the right backward connections and thus solve the weight transport problem.
Edit intent: meaning-changed
Original:  Predictive maintenance (PdM) is an industry 4.0 solution, which facilitates predicting faults in a component or a system powered by state-of-the-art machine learning (ML) algorithms (especially deep learning) and the Internet-of-Things (IoT) sensors.
Revised:  Predictive maintenance (PdM) is an industry 4.0 solution, which facilitates predicting faults in a component or a system powered by state-of-the-art machine learning (ML) algorithms and the Internet-of-Things (IoT) sensors.
Edit intent: coherence
Original:  Afterward, we model and apply two different types of false data injection attacks (FDIA) on turbofan engine sensor data and evaluate their impact on CNN, LSTM, and GRU-based PdM systems.
Revised:  Afterward, we model two different types of false data injection attacks (FDIA) on turbofan engine sensor data and evaluate their impact on CNN, LSTM, and GRU-based PdM systems.
Edit intent: clarity
Original: {\cal  {\cal  _1,...,{\cal P}_m, for the data reduction purpose, one way to summarize their topological features is to compute the {\em center} {\cal C} of them .
Revised: Throughout this paper, a persistence diagram{\cal {\cal _1,...,{\cal P}_m, for the data reduction purpose, one way to summarize their topological features is to compute the {\em center} {\cal C} of them .
Edit intent: meaning-changed
Original: {\cal  {\cal  _1,...,{\cal P}_m, for the data reduction purpose, one way to summarize their topological features is to compute the {\em center} {\cal C} of them .
Revised: {\cal P {\cal _1,...,{\cal P}_m, for the data reduction purpose, one way to summarize their topological features is to compute the {\em center} {\cal C} of them .
Edit intent: fluency
Original: {\cal  {\cal  _1,...,{\cal P}_m, for the data reduction purpose, one way to summarize their topological features is to compute the {\em center} {\cal C} of them .
Revised: {\cal is composed of a set P of planar points (each corresponding to a topological feature) above the line Y=X, as well as the line Y=X itself, i.e.,{\cal _1,...,{\cal P}_m, for the data reduction purpose, one way to summarize their topological features is to compute the {\em center} {\cal C} of them .
Edit intent: meaning-changed
Original: {\cal  {\cal  _1,...,{\cal P}_m, for the data reduction purpose, one way to summarize their topological features is to compute the {\em center} {\cal C} of them .
Revised: {\cal {\cal P _1,...,{\cal P}_m, for the data reduction purpose, one way to summarize their topological features is to compute the {\em center} {\cal C} of them .
Edit intent: fluency
Original: {\cal  {\cal  _1,...,{\cal P}_m, for the data reduction purpose, one way to summarize their topological features is to compute the {\em center} {\cal C} of them .
Revised: {\cal {\cal =P\cup\{(x,y)|y=x\} Given a set of persistence diagrams {\cal P_1,...,{\cal P}_m, for the data reduction purpose, one way to summarize their topological features is to compute the {\em center} {\cal C} of them .
Edit intent: meaning-changed
Original: {\cal  {\cal  _1,...,{\cal P}_m, for the data reduction purpose, one way to summarize their topological features is to compute the {\em center} {\cal C} of them . Let P_i be the set of feature points in%DIFDELCMD < {\cal %%% P _i. Here we mainly focus on the two discrete versions when points in%DIFDELCMD < {\cal %%% C could be selected with or without replacement from P_i's. (We will briefly discuss the continuous case, i. e., points in%DIFDELCMD < {\cal %%% C are arbitrary, which turns out to be closely related to the 3-dimensional geometric assignment problem).
Revised: {\cal {\cal _1,...,{\cal P}_m, for the data reduction purpose, one way to summarize their topological features is to compute the {\em center} {\cal C} of them %DIFDELCMD < {\cal %%% P _i. Here we mainly focus on the two discrete versions when points in%DIFDELCMD < {\cal %%% C could be selected with or without replacement from P_i's. (We will briefly discuss the continuous case, i. e., points in%DIFDELCMD < {\cal %%% C are arbitrary, which turns out to be closely related to the 3-dimensional geometric assignment problem).
Edit intent: clarity
Original:  Let P_i be the set of feature points in%DIFDELCMD < {\cal %%% P _i. Here we mainly focus on the two discrete versions when points in%DIFDELCMD < {\cal %%% C could be selected with or without replacement from P_i's. (We will briefly discuss the continuous case, i. e., points in%DIFDELCMD < {\cal %%% C are arbitrary, which turns out to be closely related to the 3-dimensional geometric assignment problem).
Revised:  Let P_i be the set of feature points in%DIFDELCMD < {\cal %%% _i. Here we mainly focus on the two discrete versions when points in%DIFDELCMD < {\cal %%% C could be selected with or without replacement from P_i's. (We will briefly discuss the continuous case, i. e., points in%DIFDELCMD < {\cal %%% C are arbitrary, which turns out to be closely related to the 3-dimensional geometric assignment problem).
Edit intent: clarity
Original:  Let P_i be the set of feature points in%DIFDELCMD < {\cal %%% P _i. Here we mainly focus on the two discrete versions when points in%DIFDELCMD < {\cal %%% C could be selected with or without replacement from P_i's. (We will briefly discuss the continuous case, i. e., points in%DIFDELCMD < {\cal %%% C are arbitrary, which turns out to be closely related to the 3-dimensional geometric assignment problem).
Revised:  Let P_i be the set of feature points in%DIFDELCMD < {\cal %%% P first under the bottleneck distance. We consider two discrete versions when points in%DIFDELCMD < {\cal %%% C could be selected with or without replacement from P_i's. (We will briefly discuss the continuous case, i. e., points in%DIFDELCMD < {\cal %%% C are arbitrary, which turns out to be closely related to the 3-dimensional geometric assignment problem).
Edit intent: meaning-changed
Original:  Let P_i be the set of feature points in%DIFDELCMD < {\cal %%% P _i. Here we mainly focus on the two discrete versions when points in%DIFDELCMD < {\cal %%% C could be selected with or without replacement from P_i's. (We will briefly discuss the continuous case, i. e., points in%DIFDELCMD < {\cal %%% C are arbitrary, which turns out to be closely related to the 3-dimensional geometric assignment problem).
Revised:  Let P_i be the set of feature points in%DIFDELCMD < {\cal %%% P _i. Here we mainly focus on the two discrete versions %DIFDELCMD < {\cal %%% C could be selected with or without replacement from P_i's. (We will briefly discuss the continuous case, i. e., points in%DIFDELCMD < {\cal %%% C are arbitrary, which turns out to be closely related to the 3-dimensional geometric assignment problem).
Edit intent: clarity
Original:  Let P_i be the set of feature points in%DIFDELCMD < {\cal %%% P _i. Here we mainly focus on the two discrete versions when points in%DIFDELCMD < {\cal %%% C could be selected with or without replacement from P_i's. (We will briefly discuss the continuous case, i. e., points in%DIFDELCMD < {\cal %%% C are arbitrary, which turns out to be closely related to the 3-dimensional geometric assignment problem).
Revised:  Let P_i be the set of feature points in%DIFDELCMD < {\cal %%% P _i. Here we mainly focus on the two discrete versions when points in%DIFDELCMD < {\cal %%% could be selected with or without replacement from P_i's. (We will briefly discuss the continuous case, i. e., points in%DIFDELCMD < {\cal %%% C are arbitrary, which turns out to be closely related to the 3-dimensional geometric assignment problem).
Edit intent: fluency
Original:  Let P_i be the set of feature points in%DIFDELCMD < {\cal %%% P _i. Here we mainly focus on the two discrete versions when points in%DIFDELCMD < {\cal %%% C could be selected with or without replacement from P_i's. (We will briefly discuss the continuous case, i. e., points in%DIFDELCMD < {\cal %%% C are arbitrary, which turns out to be closely related to the 3-dimensional geometric assignment problem).
Revised:  Let P_i be the set of feature points in%DIFDELCMD < {\cal %%% P _i. Here we mainly focus on the two discrete versions when points in%DIFDELCMD < {\cal %%% C %DIFDELCMD < {\cal %%% C are arbitrary, which turns out to be closely related to the 3-dimensional geometric assignment problem).
Edit intent: coherence
Original:  Let P_i be the set of feature points in%DIFDELCMD < {\cal %%% P _i. Here we mainly focus on the two discrete versions when points in%DIFDELCMD < {\cal %%% C could be selected with or without replacement from P_i's. (We will briefly discuss the continuous case, i. e., points in%DIFDELCMD < {\cal %%% C are arbitrary, which turns out to be closely related to the 3-dimensional geometric assignment problem).
Revised:  Let P_i be the set of feature points in%DIFDELCMD < {\cal %%% P _i. Here we mainly focus on the two discrete versions when points in%DIFDELCMD < {\cal %%% C could be selected with or without replacement from P_i's. (We will briefly discuss the continuous case, i. e., points in%DIFDELCMD < {\cal %%% are arbitrary, which turns out to be closely related to the 3-dimensional geometric assignment problem).
Edit intent: fluency