wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In an effort to improve generalization of GNNs we suggest the Low-Rank Global Attention (LRGA ) module , taking advantage of the efficiency of low rank matrix-vector multiplication, that improves the algorithmic alignment (Xu et al., 2019b) of GNNs with the 2-folklore Weisfeiler-Lehman ( FWL) algorithm;
Revised:  In an effort to improve generalization of GNNs we suggest the Low-Rank Global Attention (LRGA ) module , to Graph Neural Networks (GNNs) for improving their generalization power. To theoretically quantify the generalization properties granted by adding the LRGA module to GNNs, we focus on a specific family of expressive GNNs and show that augmenting it with LRGA provides algorithmic alignment to a powerful graph isomorphism test, namely the 2-Folklore Weisfeiler-Lehman ( FWL) algorithm;
Edit intent: meaning-changed
Original:  In an effort to improve generalization of GNNs we suggest the Low-Rank Global Attention (LRGA ) module , taking advantage of the efficiency of low rank matrix-vector multiplication, that improves the algorithmic alignment (Xu et al., 2019b) of GNNs with the 2-folklore Weisfeiler-Lehman ( FWL) algorithm; 2-FWL is a graph isomorphism algorithm that is strictly more powerful than vertex coloring. Concretely, we: (i) formulate 2-FWL using polynomial kernels ;
Revised:  In an effort to improve generalization of GNNs we suggest the Low-Rank Global Attention (LRGA ) module , taking advantage of the efficiency of low rank matrix-vector multiplication, that improves the algorithmic alignment (Xu et al., 2019b) of GNNs with the 2-folklore Weisfeiler-Lehman ( 2-FWL) algorithm. In more detail we: (i) formulate 2-FWL using polynomial kernels ;
Edit intent: clarity
Original:  From a practical point of view, augmenting existing GNN layers with LRGA produces state of the art results on most datasets in a GNN standard benchmark .
Revised:  From a practical point of view, augmenting existing GNN layers with LRGA produces state of the art results in current GNN benchmarks. Lastly, we observe that augmenting various GNN architectures with LRGA often closes the performance gap between different models .
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Original:  When compared to Word2vec, PARSER and MRIL, SyncMap surpasses or ties with the best algorithm on 77 \\%DIF < of the scenarios while being the second best in the remaing 23\\%.abstract %DIF > of the scenarios while being the second best in the remaining 34\\%.
Revised:  When compared to Word2vec, PARSER and MRIL, SyncMap surpasses or ties with the best algorithm on 66 \\%DIF < of the scenarios while being the second best in the remaing 23\\%.abstract %DIF > of the scenarios while being the second best in the remaining 34\\%.
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Original:  By testing our model using simulation, we show that our redistribution algorithm can decrease wait times up to 50\% and increase vehicle utilization up to 8\% . Our findings outline that the value of redistribution is contingent on localised market structure and customer behaviour.
Revised:  By testing our model using simulation, we show that our redistribution algorithm can decrease wait times by more than 50\% and increase vehicle utilization up to 8\% . Our findings outline that the value of redistribution is contingent on localised market structure and customer behaviour.
Edit intent: meaning-changed
Original:  By testing our model using simulation, we show that our redistribution algorithm can decrease wait times up to 50\% and increase vehicle utilization up to 8\% . Our findings outline that the value of redistribution is contingent on localised market structure and customer behaviour.
Revised:  By testing our model using simulation, we show that our redistribution algorithm can decrease wait times up to 50\% , increase profit up to 10\% with less than 20\% increase in vehicle mileage . Our findings outline that the value of redistribution is contingent on localised market structure and customer behaviour.
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Original: We introduce foveated perceptual systems, inspired by human biological systems, and examine the impact that this foveation stage has on the nature and robustness of subsequently learned visual representation.
Revised: The goal of this work is to characterize the representational impact that foveation operations have for machine vision systems, inspired by human biological systems, and examine the impact that this foveation stage has on the nature and robustness of subsequently learned visual representation.
Edit intent: clarity
Original: We introduce foveated perceptual systems, inspired by human biological systems, and examine the impact that this foveation stage has on the nature and robustness of subsequently learned visual representation. Specifically, these%DIFDELCMD < \textit{%%% two-stage perceptual systems first foveate an image , inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorization   .
Revised: We introduce foveated perceptual systems, inspired by %DIFDELCMD < \textit{%%% two-stage perceptual systems first foveate an image , inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorization .
Edit intent: clarity
Original:  Specifically, these%DIFDELCMD < \textit{%%% two-stage perceptual systems first foveate an image , inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorization   .
Revised:  Specifically, these%DIFDELCMD < \textit{%%% perceptual systems first foveate an image , inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorization .
Edit intent: clarity
Original:  Specifically, these%DIFDELCMD < \textit{%%% two-stage perceptual systems first foveate an image , inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorization   .
Revised:  Specifically, these%DIFDELCMD < \textit{%%% two-stage the foveated human visual system, which has higher acuity at the center of gaze and a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorization .
Edit intent: meaning-changed
Original:  Specifically, these%DIFDELCMD < \textit{%%% two-stage perceptual systems first foveate an image , inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorization   .
Revised:  Specifically, these%DIFDELCMD < \textit{%%% two-stage perceptual systems first foveate an image , inducing a texture-like encoding mechanism in the periphery. To do so, we introduce models consisting of a first-stage .
Edit intent: clarity
Original:  Specifically, these%DIFDELCMD < \textit{%%% two-stage perceptual systems first foveate an image , inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorization   .
Revised:  Specifically, these%DIFDELCMD < \textit{%%% two-stage perceptual systems first foveate an image , inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorizationfixed .
Edit intent: fluency
Original:  Specifically, these%DIFDELCMD < \textit{%%% two-stage perceptual systems first foveate an image , inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorization   .
Revised:  Specifically, these%DIFDELCMD < \textit{%%% two-stage perceptual systems first foveate an image , inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorization image transform followed by a second-stage .
Edit intent: meaning-changed
Original:  Specifically, these%DIFDELCMD < \textit{%%% two-stage perceptual systems first foveate an image , inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorization   .
Revised:  Specifically, these%DIFDELCMD < \textit{%%% two-stage perceptual systems first foveate an image , inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorization learnable .
Edit intent: clarity
Original:  Specifically, these%DIFDELCMD < \textit{%%% two-stage perceptual systems first foveate an image , inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorization   .
Revised:  Specifically, these%DIFDELCMD < \textit{%%% two-stage perceptual systems first foveate an image , inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorization convolutional neural network, and we varied the first stage component. The primary model has a foveated-textural input stage, which we compare to a model with foveated-blurred input and a model with spatially-uniform blurred input (both matched for perceptual compression), and a final reference model with minimal input-based compression .
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Original:  We find that : 1-- Systems trained on foveated inputs (Foveation-Nets) have similar generalization as networks trained on matched-resource networks without foveated input(Standard-Nets), yet show greater cross-generalization.2-- Foveation-Nets show higher robustness than Standard-Nets to scotoma (fovea removed) occlusions, driven by the first foveation stage. 3-- Subsequent representations learned in the CNN of Foveation-Nets weigh center information more strongly than Standard-Nets. 4-- Foveation-Nets show less sensitivity to low-spatial frequency information than Standard-Nets. Furthermore, when we added biological and artificial augmentation mechanisms to each system through simulated eye-movements or random cropping and mirroring respectively, we found that these effects were amplified. Taken together, we find evidence that foveated perceptual systemslearn a visual representation that is distinct from non-foveated perceptual systems, with implications in generalization, robustness, and perceptual sensitivity. These results provide computational support for the idea that the foveated nature of the human visual system might confer a functional advantage for scene representation .
Revised:  We find that the model with foveated texture-based input -- most akin to the human visual system -- shows similar scene classification accuracy as the reference model despite its compressed input, with greater i.i.d. generalization than the foveated nature of the human visual system might confer a functional advantage for scene representation .
Edit intent: clarity
Original:  These results provide computational support for the idea that the foveated nature of the human visual system might confer a functional advantage for scene representation .
Revised:  These results provide computational support for the idea that the other models. Second, the foveated-texture model maintains sensitivity to high-spatial frequency information and greater robustness to occlusion properties, similar to the reference, and unlike the comparison models. Finally, both the foveated systems, either texture or blur-based, show a stronger center image-bias relative to the spatially-uniform systems. These results demonstrate that foveation with peripheral texture-based computations yields an efficient, distinct, and robust representational format of scene information, and provides computational insight into the representational consequences that texture-based peripheral encoding may have for processing in the human visual system might confer a functional advantage for scene representation .
Edit intent: clarity
Original: The goal of this work is to characterize the representational impact that foveation operations have for machine vision systems, inspired by the foveated human visual system, which has higher acuity at the center of gaze and a texture-like encoding mechanism in the periphery.
Revised: The goal of this work is to characterize the representational impact that foveation operations have for machine vision systems, inspired by the foveated human visual system, which has higher acuity at the center of gaze and texture-like encoding mechanism in the periphery.
Edit intent: fluency
Original: The goal of this work is to characterize the representational impact that foveation operations have for machine vision systems, inspired by the foveated human visual system, which has higher acuity at the center of gaze and a texture-like encoding mechanism in the periphery.
Revised: The goal of this work is to characterize the representational impact that foveation operations have for machine vision systems, inspired by the foveated human visual system, which has higher acuity at the center of gaze and a texture-like encoding in the periphery.
Edit intent: clarity
Original:  We find that the model with foveated texture-based input -- most akin to the human visual system -- shows similar scene classification accuracy as the reference model despite its compressed input, with greater i.i.d. generalization than the other models . Second, the foveated-texture model maintains sensitivity to high-spatial frequency information and greater robustness to occlusion properties, similar to the reference, and unlike the comparison models.
Revised:  We find that : 1) the foveated-texture model shows similar scene classification accuracy as the reference model despite its compressed input, with greater i.i.d. generalization than the other models . Second, the foveated-texture model maintains sensitivity to high-spatial frequency information and greater robustness to occlusion properties, similar to the reference, and unlike the comparison models.
Edit intent: coherence
Original:  We find that the model with foveated texture-based input -- most akin to the human visual system -- shows similar scene classification accuracy as the reference model despite its compressed input, with greater i.i.d. generalization than the other models . Second, the foveated-texture model maintains sensitivity to high-spatial frequency information and greater robustness to occlusion properties, similar to the reference, and unlike the comparison models.
Revised:  We find that the model with foveated texture-based input -- most akin to the human visual system -- shows similar scene classification accuracy as the reference model despite its compressed input, with greater i.i.d. generalization than the other models ; 2) the foveated-texture model maintains sensitivity to high-spatial frequency information and greater robustness to occlusion properties, similar to the reference, and unlike the comparison models.
Edit intent: coherence
Original:  We find that the model with foveated texture-based input -- most akin to the human visual system -- shows similar scene classification accuracy as the reference model despite its compressed input, with greater i.i.d. generalization than the other models . Second, the foveated-texture model maintains sensitivity to high-spatial frequency information and greater robustness to occlusion properties, similar to the reference, and unlike the comparison models.
Revised:  We find that the model with foveated texture-based input -- most akin to the human visual system -- shows similar scene classification accuracy as the reference model despite its compressed input, with greater i.i.d. generalization than the other models . Second, the foveated-texture model has greater sensitivity to high-spatial frequency information and greater robustness to occlusion properties, similar to the reference, and unlike the comparison models.
Edit intent: clarity
Original:  We find that the model with foveated texture-based input -- most akin to the human visual system -- shows similar scene classification accuracy as the reference model despite its compressed input, with greater i.i.d. generalization than the other models . Second, the foveated-texture model maintains sensitivity to high-spatial frequency information and greater robustness to occlusion properties, similar to the reference, and unlike the comparison models. Finally, both the foveated systems, either texture or blur-based, show a stronger center image-bias relative to the spatially-uniform systems .
Revised:  We find that the model with foveated texture-based input -- most akin to the human visual system -- shows similar scene classification accuracy as the reference model despite its compressed input, with greater i.i.d. generalization than the other models . Second, the foveated-texture model maintains sensitivity to high-spatial frequency information and greater robustness to occlusion , w.r.t the comparison models; 3) both the foveated systems, either texture or blur-based, show a stronger center image-bias relative to the spatially-uniform systems .
Edit intent: coherence
Original:  Finally, both the foveated systems, either texture or blur-based, show a stronger center image-bias relative to the spatially-uniform systems . These results demonstrate that foveation with peripheral texture-based computations yields an efficient, distinct, and robust representational format of scene information, and provides  computational insight into the representational consequences that texture-based peripheral encoding may have for processing in the human visual system .
Revised:  Finally, both the foveated systems, either texture or blur-based, show a stronger center image-bias relative to the spatially-uniform systems even with a weight sharing constraint. Critically, these results are preserved over different classical CNN architectures throughout their learning dynamics. Altogether, this suggests that foveation with peripheral texture-based computations yields an efficient, distinct, and robust representational format of scene information, and provides computational insight into the representational consequences that texture-based peripheral encoding may have for processing in the human visual system .
Edit intent: meaning-changed
Original:  These results demonstrate that foveation with peripheral texture-based computations yields an efficient, distinct, and robust representational format of scene information, and provides  computational insight into the representational consequences that texture-based peripheral encoding may have for processing in the human visual system .
Revised:  These results demonstrate that foveation with peripheral texture-based computations yields an efficient, distinct, and robust representational format of scene information, and provides symbiotic computational insight into the representational consequences that texture-based peripheral encoding may have for processing in the human visual system .
Edit intent: meaning-changed
Original:  These results demonstrate that foveation with peripheral texture-based computations yields an efficient, distinct, and robust representational format of scene information, and provides  computational insight into the representational consequences that texture-based peripheral encoding may have for processing in the human visual system .
Revised:  These results demonstrate that foveation with peripheral texture-based computations yields an efficient, distinct, and robust representational format of scene information, and provides computational insight into the representational consequences that texture-based peripheral encoding may have for processing in the human visual system , while also potentially inspiring the next generation of computer vision models via spatially-adaptive computation. Code + Data available here: URL
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Original: We study an extension of optimal transport techniques to stationary Markov chains from a computational perspective. In this context, naively applying optimal transport to the stationary distributions of the Markov chainsof interest would not capture the Markovian dynamics. Instead, we study a new problem, called the optimal transition coupling problem, in which the optimal transport problem is constrained to the set of stationary Markovian couplings satisfying a certain transition matrix condition.
Revised: We study an extension of optimal transport techniques to stationary Markov chains . Solutions of the optimal transition coupling problem, in which the optimal transport problem is constrained to the set of stationary Markovian couplings satisfying a certain transition matrix condition.
Edit intent: clarity
Original:  Instead, we study a new problem, called the optimal transition coupling problem, in which the optimal transport problem is constrained to the set of stationary Markovian couplings satisfying a certain transition matrix condition. After drawing a connection between this problem and Markov decision processes, we prove that solutions can be obtained via the policy iterationalgorithm .
Revised:  Instead, we study a new problem, called the optimal transition coupling (OTC) problem correspond to alignments of the two chains that minimize long-term average cost. We establish a connection between this problem and Markov decision processes, we prove that solutions can be obtained via the policy iterationalgorithm .
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Original:  For settings with large state spaces, we also define a regularized problem, propose a faster, approximate algorithm , and provide bounds on the computational complexityof each iteration. Finally, we validate our theoretical results empirically  , demonstrating that the approximate algorithm exhibits faster overall runtime with low error in a simulation study .
Revised:  For settings with large state spaces, we also define a regularized problem, propose a faster, approximate algorithm , and provide bounds on its per-iteration complexity. We establish a stability result for both the regularized and unregularized algorithms, from which a statistical consistency result follows as a corollary. We validate our theoretical results empirically , demonstrating that the approximate algorithm exhibits faster overall runtime with low error in a simulation study .
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Original:  Finally, we validate our theoretical results empirically  , demonstrating that the approximate algorithm exhibits faster overall runtime with low error in a simulation study .
Revised:  Finally, we validate our theoretical results empirically , demonstrating that the approximate algorithm exhibits faster overall runtime with low error . Finally, we extend the setting and application of our methods to hidden Markov models, and illustrate the potential use of the proposed algorithms in practice with an application to computer-generated music .
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