wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Further, we show, for the first time, that it is possible to successfully train with tighter relaxations (i.e., triangle), a result supported by our two properties. Overall, we believe the insights of this work can help drive the systematic discovery of new effective certified defenses .
Revised:  Further, we investigate the possibility of designing and training with relaxations that are tight, continuous and not sensitive. We believe the insights of this work can help drive the systematic discovery of new effective certified defenses .
Edit intent: clarity
Original:  In this work we  completely characterize the approximability of all Boolean CSPs in the streaming model . Specifically, given f, \gamma and \beta we show that either (1) the (\gamma,\beta)-approximation version of Max-CSP(f) has a probabilistic  streaming algorithm using O(\log n) space, or (2) for every \varepsilon > 0 the (\gamma-\varepsilon,\beta+\varepsilon)-approximation version of Max-CSP(f) requires \Omega(n) space for probabilistic  streaming algorithms.
Revised:  In this work we consider the approximability of Max-CSP(f) in the (dynamic) streaming setting, where constraints are inserted (and may also be deleted in the dynamic setting) one at a time. We completely characterize the approximability of all Boolean CSPs in the streaming model . Specifically, given f, \gamma and \beta we show that either (1) the (\gamma,\beta)-approximation version of Max-CSP(f) has a probabilistic streaming algorithm using O(\log n) space, or (2) for every \varepsilon > 0 the (\gamma-\varepsilon,\beta+\varepsilon)-approximation version of Max-CSP(f) requires \Omega(n) space for probabilistic streaming algorithms.
Edit intent: meaning-changed
Original:  In this work we  completely characterize the approximability of all Boolean CSPs in the streaming model . Specifically, given f, \gamma and \beta we show that either (1) the (\gamma,\beta)-approximation version of Max-CSP(f) has a probabilistic  streaming algorithm using O(\log n) space, or (2) for every \varepsilon > 0 the (\gamma-\varepsilon,\beta+\varepsilon)-approximation version of Max-CSP(f) requires \Omega(n) space for probabilistic  streaming algorithms.
Revised:  In this work we completely characterize the approximability of all Boolean CSPs in the dynamic streaming setting . Specifically, given f, \gamma and \beta we show that either (1) the (\gamma,\beta)-approximation version of Max-CSP(f) has a probabilistic streaming algorithm using O(\log n) space, or (2) for every \varepsilon > 0 the (\gamma-\varepsilon,\beta+\varepsilon)-approximation version of Max-CSP(f) requires \Omega(n) space for probabilistic streaming algorithms.
Edit intent: clarity
Original:  In this work we  completely characterize the approximability of all Boolean CSPs in the streaming model . Specifically, given f, \gamma and \beta we show that either (1) the (\gamma,\beta)-approximation version of Max-CSP(f) has a probabilistic  streaming algorithm using O(\log n) space, or (2) for every \varepsilon > 0 the (\gamma-\varepsilon,\beta+\varepsilon)-approximation version of Max-CSP(f) requires \Omega(n) space for probabilistic  streaming algorithms.
Revised:  In this work we completely characterize the approximability of all Boolean CSPs in the streaming model . Specifically, given f, \gamma and \beta we show that either (1) the (\gamma,\beta)-approximation version of Max-CSP(f) has a probabilistic dynamic streaming algorithm using O(\log n) space, or (2) for every \varepsilon > 0 the (\gamma-\varepsilon,\beta+\varepsilon)-approximation version of Max-CSP(f) requires \Omega(n) space for probabilistic streaming algorithms.
Edit intent: meaning-changed
Original:  In this work we  completely characterize the approximability of all Boolean CSPs in the streaming model . Specifically, given f, \gamma and \beta we show that either (1) the (\gamma,\beta)-approximation version of Max-CSP(f) has a probabilistic  streaming algorithm using O(\log n) space, or (2) for every \varepsilon > 0 the (\gamma-\varepsilon,\beta+\varepsilon)-approximation version of Max-CSP(f) requires \Omega(n) space for probabilistic  streaming algorithms.
Revised:  In this work we completely characterize the approximability of all Boolean CSPs in the streaming model . Specifically, given f, \gamma and \beta we show that either (1) the (\gamma,\beta)-approximation version of Max-CSP(f) has a probabilistic streaming algorithm using O(\log n) space, or (2) for every \varepsilon > 0 the (\gamma-\varepsilon,\beta+\varepsilon)-approximation version of Max-CSP(f) requires \Omega(n) space for probabilistic dynamic streaming algorithms.
Edit intent: meaning-changed
Original:  Previously such a separation was known only for k = 2. We stress that for k= 2 , there are only finitely many distinct problems to consider. Our positive results show wider applicability of bias-based algorithms used previously by Guruswami-Velingker-Velusamy APPROX'17 , Chou-Golovnev-Velusamy FOCS'20 by giving a systematic way to explore biases.
Revised:  Previously such a separation was known only for k = 2. We stress that for k= 2 Guruswami-Velingker-Velusamy APPROX'17 , Chou-Golovnev-Velusamy FOCS'20 by giving a systematic way to explore biases.
Edit intent: coherence
Original:  Our positive results show wider applicability of bias-based algorithms used previously by Guruswami-Velingker-Velusamy APPROX'17 , Chou-Golovnev-Velusamy FOCS'20 by giving a systematic way to explore biases.
Revised:  Our positive results show wider applicability of bias-based algorithms used previously by where we get a dichotomy and the case when the satisfying assignments of f support a distribution on \{-1 , Chou-Golovnev-Velusamy FOCS'20 by giving a systematic way to explore biases.
Edit intent: meaning-changed
Original:  Our positive results show wider applicability of bias-based algorithms used previously by Guruswami-Velingker-Velusamy APPROX'17 , Chou-Golovnev-Velusamy FOCS'20 by giving a systematic way to explore biases.
Revised:  Our positive results show wider applicability of bias-based algorithms used previously by Guruswami-Velingker-Velusamy APPROX'17 , by giving a systematic way to explore biases.
Edit intent: clarity
Original:  Our positive results show wider applicability of bias-based algorithms used previously by Guruswami-Velingker-Velusamy APPROX'17 , Chou-Golovnev-Velusamy FOCS'20 by giving a systematic way to explore biases. Our negative results combine the Fourier analytic methods of%DIFDELCMD < [%%% Kapralov-Khanna-Sudan SODA'15 , which we extend to a wider class of CSPs, with a rich collection of reductions among communication complexity problems that lie at the heart of the negative results .
Revised:  Our positive results show wider applicability of bias-based algorithms used previously by Guruswami-Velingker-Velusamy APPROX'17 , Chou-Golovnev-Velusamy FOCS'20 %DIFDELCMD < [%%% Kapralov-Khanna-Sudan SODA'15 , which we extend to a wider class of CSPs, with a rich collection of reductions among communication complexity problems that lie at the heart of the negative results .
Edit intent: coherence
Original:  Our negative results combine the Fourier analytic methods of%DIFDELCMD < [%%% Kapralov-Khanna-Sudan SODA'15 , which we extend to a wider class of CSPs, with a rich collection of reductions among communication complexity problems that lie at the heart of the negative results .
Revised:  Our negative results combine the Fourier analytic methods of%DIFDELCMD < [%%% , which we extend to a wider class of CSPs, with a rich collection of reductions among communication complexity problems that lie at the heart of the negative results .
Edit intent: coherence
Original:  Our negative results combine the Fourier analytic methods of%DIFDELCMD < [%%% Kapralov-Khanna-Sudan SODA'15 , which we extend to a wider class of CSPs, with a rich collection of reductions among communication complexity problems that lie at the heart of the negative results .
Revised:  Our negative results combine the Fourier analytic methods of%DIFDELCMD < [%%% Kapralov-Khanna-Sudan SODA'15 1\ .
Edit intent: meaning-changed
Original:  Thus , it is crucial for machine learning models to account for self-tracking artifacts like skipped self-tracking. In this abstract , we propose and evaluate a hierarchical, generative model for predicting next cycle length based on previously tracked cycle lengths that accounts explicitly for the possibility of users URLetting to track their period.
Revised:  Thus , it is crucial for machine learning models to account for self-tracking artifacts . By taking a machine learning approach to modeling self-tracked cycle lengths, we can both make more informed predictions and learn the underlying structure of the observed data. In this work , we propose and evaluate a hierarchical, generative model for predicting next cycle length based on previously tracked cycle lengths that accounts explicitly for the possibility of users URLetting to track their period.
Edit intent: meaning-changed
Original:  Our experiments using real mHealth cycle length data from 5 ,000 menstruators  show that our method yields state-of-the-art performance against neural network-based and summary statistic-based baselines  .
Revised:  Our experiments using mHealth cycle length data from 5 ,000 menstruators show that our method yields state-of-the-art performance against neural network-based and summary statistic-based baselines .
Edit intent: clarity
Original:  Our experiments using real mHealth cycle length data from 5 ,000 menstruators  show that our method yields state-of-the-art performance against neural network-based and summary statistic-based baselines  .
Revised:  Our experiments using real mHealth cycle length data encompassing over 186 ,000 menstruators show that our method yields state-of-the-art performance against neural network-based and summary statistic-based baselines .
Edit intent: meaning-changed
Original:  Our experiments using real mHealth cycle length data from 5 ,000 menstruators  show that our method yields state-of-the-art performance against neural network-based and summary statistic-based baselines  .
Revised:  Our experiments using real mHealth cycle length data from 5 ,000 menstruators with over 2 million natural menstrual cycles show that our method yields state-of-the-art performance against neural network-based and summary statistic-based baselines .
Edit intent: meaning-changed
Original:  Our experiments using real mHealth cycle length data from 5 ,000 menstruators  show that our method yields state-of-the-art performance against neural network-based and summary statistic-based baselines  .
Revised:  Our experiments using real mHealth cycle length data from 5 ,000 menstruators show that our method yields state-of-the-art performance against neural network-based and summary statistic-based baselines , while providing insights on disentangling menstrual patterns from self-tracking artifacts. This work can benefit users, mHealth app developers, and researchers in better understanding cycle patterns and user adherence .
Edit intent: meaning-changed
Original: Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. 2014, Smilkov et al.  2017]  are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over non-discriminative features that are irrelevant for prediction .
Revised: Post-hoc gradient-based interpretability methods [Simonyan et al. 2014, Smilkov et al. 2017] are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over non-discriminative features that are irrelevant for prediction .
Edit intent: clarity
Original: Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. 2014, Smilkov et al.  2017]  are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over non-discriminative features that are irrelevant for prediction .
Revised: Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. , 2013, Smilkov et al. 2017] are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over non-discriminative features that are irrelevant for prediction .
Edit intent: meaning-changed
Original: Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. 2014, Smilkov et al.  2017]  are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over non-discriminative features that are irrelevant for prediction .
Revised: Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. 2014, Smilkov et al. , 2017] are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over non-discriminative features that are irrelevant for prediction .
Edit intent: fluency
Original: Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. 2014, Smilkov et al.  2017]  are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over non-discriminative features that are irrelevant for prediction .
Revised: Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. 2014, Smilkov et al. 2017] that provide instance-specific explanations of model predictions are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over non-discriminative features that are irrelevant for prediction .
Edit intent: meaning-changed
Original: Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. 2014, Smilkov et al.  2017]  are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over non-discriminative features that are irrelevant for prediction .
Revised: Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. 2014, Smilkov et al. 2017] are often based on assumption (A): magnitude of input gradients -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over non-discriminative features that are irrelevant for prediction .
Edit intent: meaning-changed
Original: Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. 2014, Smilkov et al.  2017]  are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over non-discriminative features that are irrelevant for prediction .
Revised: Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. 2014, Smilkov et al. 2017] are often based on the premise that the magnitude of input-gradient -- gradients of logits with respect to input -- highlights discriminative features that are relevant for prediction over non-discriminative features that are irrelevant for prediction .
Edit intent: meaning-changed
Original: Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. 2014, Smilkov et al.  2017]  are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- highlights discriminative features that are relevant for prediction over non-discriminative features that are irrelevant for prediction .
Revised: Interpretability methods that seek to explain instance-specific model predictions [Simonyan et al. 2014, Smilkov et al. 2017] are often based on the premise that the magnitude of input-gradient -- gradient of the loss with respect to input -- noisily highlight discriminative task-relevant features .
Edit intent: clarity
Original:  In this work, we introduce an evaluation framework to study this hypothesis for benchmark image classification tasks, and make two surprising observations on CIFAR-10 and Imagenet-10 datasets: (a) contrary to conventional wisdom, input gradients of standard models (i.e., trained on the original data) actually highlight irrelevant features over relevant features;
Revised:  In this work, we test the validity of assumption (A) using a three-pronged approach. First, we develop an evaluation framework to study this hypothesis for benchmark image classification tasks, and make two surprising observations on CIFAR-10 and Imagenet-10 datasets: (a) contrary to conventional wisdom, input gradients of standard models (i.e., trained on the original data) actually highlight irrelevant features over relevant features;
Edit intent: meaning-changed
Original:  In this work, we introduce an evaluation framework to study this hypothesis for benchmark image classification tasks, and make two surprising observations on CIFAR-10 and Imagenet-10 datasets: (a) contrary to conventional wisdom, input gradients of standard models (i.e., trained on the original data) actually highlight irrelevant features over relevant features;
Revised:  In this work, we introduce an evaluation framework , DiffROAR, to test assumption (A) on four image classification benchmarks. Our results suggest that (i) input gradients of standard models (i.e., trained on the original data) actually highlight irrelevant features over relevant features;
Edit intent: meaning-changed
Original:  In this work, we introduce an evaluation framework to study this hypothesis for benchmark image classification tasks, and make two surprising observations on CIFAR-10 and Imagenet-10 datasets: (a) contrary to conventional wisdom, input gradients of standard models (i.e., trained on the original data) actually highlight irrelevant features over relevant features;
Revised:  In this work, we introduce an evaluation framework to study this hypothesis for benchmark image classification tasks, and make two surprising observations on CIFAR-10 and Imagenet-10 datasets: (a) contrary to conventional wisdom, input gradients of standard models (i.e., trained on original data) actually highlight irrelevant features over relevant features;
Edit intent: fluency
Original:  In this work, we introduce an evaluation framework to study this hypothesis for benchmark image classification tasks, and make two surprising observations on CIFAR-10 and Imagenet-10 datasets: (a) contrary to conventional wisdom, input gradients of standard models (i.e., trained on the original data) actually highlight irrelevant features over relevant features; (b)however, input gradients of adversarially robust models (i.e., trained on adversarially perturbed data)starkly highlight relevant features over irrelevant features.
Revised:  In this work, we introduce an evaluation framework to study this hypothesis for benchmark image classification tasks, and make two surprising observations on CIFAR-10 and Imagenet-10 datasets: (a) contrary to conventional wisdom, input gradients of standard models (i.e., trained on the original data) may grossly violate (A), whereas (ii) input gradients of adversarially robust models (i.e., trained on adversarially perturbed data)starkly highlight relevant features over irrelevant features.
Edit intent: meaning-changed
Original:  (b)however, input gradients of adversarially robust models (i.e., trained on adversarially perturbed data)starkly highlight relevant features over irrelevant features. To better understand input gradients, we introduce a synthetic testbed and theoretically justify our counter-intuitive empirical findings .
Revised:  (b)however, input gradients of adversarially robust models satisfy (A) .
Edit intent: meaning-changed
Original:  Our observations motivate the need to formalize and verify common assumptions in interpretability , while our ] evaluation framework and synthetic dataset serve as a testbed to rigorously analyze instance-specific interpretability methods.
Revised:  Second, we then introduce BlockMNIST, an MNIST-based semi-real dataset, that by design encodes a priori knowledge of discriminative features. Our analysis on BlockMNIST leverages this information to validate as well as characterize differences between input gradient attributions of standard and robust models. Finally, we theoretically prove that our empirical findings hold on a simplified version of the BlockMNIST dataset. Specifically, we prove that input gradients of standard one-hidden-layer MLPs trained on this dataset do not highlight instance-specific signal coordinates, thus grossly violating assumption (A). Our findings motivate the need to formalize and verify common assumptions in interpretability , while our ] evaluation framework and synthetic dataset serve as a testbed to rigorously analyze instance-specific interpretability methods.
Edit intent: meaning-changed
Original:  Our observations motivate the need to formalize and verify common assumptions in interpretability , while our ] evaluation framework and synthetic dataset serve as a testbed to rigorously analyze instance-specific interpretability methods.
Revised:  Our observations motivate the need to formalize and verify common assumptions in interpretability in a falsifiable manner ] evaluation framework and synthetic dataset serve as a testbed to rigorously analyze instance-specific interpretability methods.
Edit intent: meaning-changed
Original:  Our observations motivate the need to formalize and verify common assumptions in interpretability , while our ] evaluation framework and synthetic dataset serve as a testbed to rigorously analyze instance-specific interpretability methods.
Revised:  Our observations motivate the need to formalize and verify common assumptions in interpretability , while our Leavitt and Morcos, 2020] evaluation framework and synthetic dataset serve as a testbed to rigorously analyze instance-specific interpretability methods.
Edit intent: meaning-changed
Original:  Our observations motivate the need to formalize and verify common assumptions in interpretability , while our ] evaluation framework and synthetic dataset serve as a testbed to rigorously analyze instance-specific interpretability methods.
Revised:  Our observations motivate the need to formalize and verify common assumptions in interpretability , while our ]. Additionally, we believe that the DiffROAR evaluation framework and synthetic dataset serve as a testbed to rigorously analyze instance-specific interpretability methods.
Edit intent: meaning-changed
Original:  Our observations motivate the need to formalize and verify common assumptions in interpretability , while our ] evaluation framework and synthetic dataset serve as a testbed to rigorously analyze instance-specific interpretability methods.
Revised:  Our observations motivate the need to formalize and verify common assumptions in interpretability , while our ] evaluation framework and BlockMNIST-based datasets can serve as sanity checks to audit instance-specific interpretability methods.
Edit intent: meaning-changed