wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our proposed solution builds on graph neural networks to learn scalable agent models and value decompositions under varying team sizes, which can be jointly trained with a reinforcement learning agent using discounted returns objectives .
Revised:  Our solution builds on graph neural networks to learn scalable agent models and value decompositions under varying team sizes, which can be jointly trained with a reinforcement learning agent using discounted returns objectives .
Edit intent: clarity
Original:  Our proposed solution builds on graph neural networks to learn scalable agent models and value decompositions under varying team sizes, which can be jointly trained with a reinforcement learning agent using discounted returns objectives .
Revised:  Our proposed solution builds on graph neural networks to learn agent models and value decompositions under varying team sizes, which can be jointly trained with a reinforcement learning agent using discounted returns objectives .
Edit intent: clarity
Original:  Our proposed solution builds on graph neural networks to learn scalable agent models and value decompositions under varying team sizes, which can be jointly trained with a reinforcement learning agent using discounted returns objectives .
Revised:  Our proposed solution builds on graph neural networks to learn scalable agent models and joint action-value decompositions under varying team sizes, which can be jointly trained with a reinforcement learning agent using discounted returns objectives .
Edit intent: clarity
Original:  Our proposed solution builds on graph neural networks to learn scalable agent models and value decompositions under varying team sizes, which can be jointly trained with a reinforcement learning agent using discounted returns objectives .
Revised:  Our proposed solution builds on graph neural networks to learn scalable agent models and value decompositions under varying team sizes, which can be trained with reinforcement learning using a discounted returns objective .
Edit intent: clarity
Original:  We demonstrate empirically that our approach results in agent policies which can robustly adapt to dynamic team composition , and is able to effectively generalize to larger teams than were seen during training.
Revised:  We demonstrate empirically that our approach results in agent policies which can robustly adapt to dynamic team composition and is able to effectively generalize to larger teams than were seen during training.
Edit intent: fluency
Original: Ad hoc teamwork is the challenging problem of designing an autonomous agent which can adapt quickly to collaborate with previously unknown teammates . Prior work in this area has focused on closed teams in which the number of agents is fixed.
Revised: Ad hoc teamwork is the challenging problem of designing an autonomous agent that can adapt quickly to collaborate with previously unknown teammates . Prior work in this area has focused on closed teams in which the number of agents is fixed.
Edit intent: fluency
Original:  In this work, we consider open teams by allowing agents of varying types to enter and leave the team without prior notification.
Revised:  In this work, we consider open teams by allowing agents with different fixed policies to enter and leave the team without prior notification.
Edit intent: clarity
Original:  In this work, we consider open teams by allowing agents of varying types to enter and leave the team without prior notification.
Revised:  In this work, we consider open teams by allowing agents of varying types to enter and leave the environment without prior notification.
Edit intent: clarity
Original:  Our solution builds on graph neural networks to learn agent models and joint action-value decompositions under varying team sizes, which can be trained with reinforcement learning using a discounted returns objective.
Revised:  Our solution builds on graph neural networks to learn agent and joint-action value models under varying team sizes, which can be trained with reinforcement learning using a discounted returns objective.
Edit intent: clarity
Original:  We demonstrate empirically that our approach effectively models the impact of other agents actions on the controlled agent's returns to produce policies which can robustly adapt to dynamic team composition and is able to effectively generalize to larger teams than were seen during training .
Revised:  We demonstrate empirically that our approach successfully models the effects other agents have on the learner, which leads to policies that robustly adapt to dynamic team composition and is able to effectively generalize to larger teams than were seen during training .
Edit intent: clarity
Original: Ad hoc teamwork is the challenging problem of designing an autonomous agent that can adapt quickly to collaborate with teammates without prior coordination mechanisms, including joint training.
Revised: Ad hoc teamwork is the challenging problem of designing an autonomous agent which can adapt quickly to collaborate with teammates without prior coordination mechanisms, including joint training.
Edit intent: clarity
Original:  Our solution builds on graph neural networks to learn agent  and joint-action value models under varying team compositions.
Revised:  Our solution builds on graph neural networks to learn agent models and joint-action value models under varying team compositions.
Edit intent: fluency
Original:  We contribute a novel action-value computation that integrates the agent  and joint-action value model to produce action-value estimates.
Revised:  We contribute a novel action-value computation that integrates the agent model and joint-action value model to produce action-value estimates.
Edit intent: fluency
Original:  We empirically demonstrate that our approach successfully models the effects other agents have on the learner, which leads to policies that robustly adapt to dynamic team compositions and significantly outperform several alternative methods.
Revised:  We empirically demonstrate that our approach successfully models the effects other agents have on the learner, leading to policies that robustly adapt to dynamic team compositions and significantly outperform several alternative methods.
Edit intent: coherence
Original:  A spectral-Galerkin method is studied where the basis functions are the Neumann eigenfunctions of the Laplacian.
Revised:  A Galerkin method is studied where the basis functions are the Neumann eigenfunctions of the Laplacian.
Edit intent: clarity
Original:  Since the eigenvalues are monotone with respect to a real-valued refractive index this implies that they can be used for non-destructive testing.
Revised:  Since the eigenvalues are monotone with respect to a real-valued refractive index implies that they can be used for non-destructive testing.
Edit intent: coherence
Original: We introduce a setting where a receiver aims to perfectly recover a source known privately to a strategic sender over a possibly noisy channel.
Revised: We introduce a setting where a receiver aims to perfectly recover a source known privately to a strategic sender over a possibly noisy channel.
Edit intent: fluency
Original:  Due to the strategic nature of the sender not all the transmitted information is truthful, which leads to  question: how much true information can be recovered by the receiver from such a sender?
Revised:  Due to the strategic nature of the sender not all the transmitted information is truthful, which leads to the question: how much true information can be recovered by the receiver from such a sender?
Edit intent: fluency
Original:  In the latter case, we show that the capacityis sandwiched between the independence number and the Shannon capacity of a suitably defined graph.
Revised:  In the latter case, we show that the capacityis sandwiched between the independence number and the Shannon capacity of an appropriate graph.
Edit intent: clarity
Original:  These results  lead to an exact characterization of the information extraction capacity in  large number of cases.
Revised:  These results also lead to an exact characterization of the information extraction capacity in large number of cases.
Edit intent: coherence
Original:  These results  lead to an exact characterization of the information extraction capacity in  large number of cases.
Revised:  These results lead to an exact characterization of the information extraction capacity in a large number of cases.
Edit intent: fluency
Original:  Numerical results illustrate the effect of the fractional derivative and the influence of the fractional parameter  .
Revised:  Numerical results illustrate the effect of the fractional derivative and the influence of the fractional parameter in numerical examples .
Edit intent: clarity
Original: This paper initiates the study of online algorithms for the maintaining a maximum weight b-matching problem, a generalization of maximum weight matching where each node has at most b > 0 adjacent matching edges.
Revised: This paper initiates the study of online algorithms for the maximum weight b-matching problem, a generalization of maximum weight matching where each node has at most b > 0 adjacent matching edges.
Edit intent: clarity
Original:  Based on our analysis and results in the tabular setting  we introduce two scalable versions of our algorithm, Centrally-Weighted (CW) QMIX and Optimistically-Weighted (OW) QMIX and demonstrate improved performance on both predator-prey and challenging multi-agent StarCraft benchmark tasks.
Revised:  Based on our analysis and results in the tabular setting , we introduce two scalable versions of our algorithm, Centrally-Weighted (CW) QMIX and Optimistically-Weighted (OW) QMIX and demonstrate improved performance on both predator-prey and challenging multi-agent StarCraft benchmark tasks.
Edit intent: fluency
Original:  In this paper, we introduce a new approach to formally verify the most commonly considered safety specification for ReLU NNs -- i.e. polytopic specifications on the input and output of the network.
Revised:  In this paper, we introduce a new approach to formally verify the most commonly considered safety specifications for ReLU NNs -- i.e. polytopic specifications on the input and output of the network.
Edit intent: fluency
Original: In this paper, we propose a joint model for simultaneous speaker counting, speech recognition, and speaker identification on monaural overlapped speech.
Revised: We propose an end-to-end speaker-attributed automatic speech recognition model that unifies speaker counting, speech recognition, and speaker identification on monaural overlapped speech.
Edit intent: clarity
Original:  We extend the SOT model by introducing a speaker inventory as an auxiliary input to produce speaker labels as well as multi-speaker transcriptions.
Revised:  We extend SOT by introducing a speaker inventory as an auxiliary input to produce speaker labels as well as multi-speaker transcriptions.
Edit intent: clarity
Original:  In a best-effort scenario, we attempt to systematically identify all relevant apps and study their characteristics with the objective to provide a first taxonomy of Covid-related apps, broadening the relevance beyond the implementation of contact tracing.
Revised:  In a best-effort scenario, we attempt to systematically identify all relevant apps and study their characteristics with the objective to provide a First taxonomy of Covid-related apps, broadening the relevance beyond the implementation of contact tracing.
Edit intent: fluency
Original:  Overall, our study yields a number of empirical insights that contribute to enlarge the knowledge on Covid-related apps: (1) Developer communities contributed rapidly to the Covid-19, with  apps released as early as February 2020;
Revised:  Overall, our study yields a number of empirical insights that contribute to enlarge the knowledge on Covid-related apps: (1) Developer communities contributed rapidly to the Covid-19, with apps released as early as January 2020;
Edit intent: fluency
Original:  However,  different causes of an interaction are bundled together as a unified representation in these algorithms .
Revised:  However, different causes of an interaction are bundled together as unified representations, hence robustness and interpretability are not guaranteed when underlying causes are changing .
Edit intent: coherence
Original:  In this paper, we present DICE, a general framework that learns representations where user interest and item popularity are structurally disentangled .
Revised:  In this paper, we present DICE, a general framework that learns representations where interest and conformity are structurally disentangled .
Edit intent: clarity
Original:  Our algorithm outperforms state-of-the-art baselines with remarkable improvements on two real-world datasets with various backbone models.
Revised:  Our algorithm outperforms state-of-the-art baselines with remarkable improvements on two real-world datasets on top of various backbone models.
Edit intent: clarity
Original: A numerical solution for the Fokker-Planck equation using a two-level scheme is presented.
Revised: A numerical solution to the Fokker-Planck equation using a two-level scheme is presented.
Edit intent: fluency
Original:  A Chang-Cooper discretization scheme is used to ensure the positiveness (resp. conservation of total probability) and second-order accuracy.
Revised:  A Chang-Cooper discretization scheme is used to ensure the positiveness and conservation of the total probability with second-order accuracy.
Edit intent: coherence
Original:  We investigate a two-level scheme with factor three coarsening strategy and have a significant reduction in computations and CPU time.
Revised:  We investigate a two-level scheme with factor-three-coarsening strategy and have a significant reduction in computations and CPU time.
Edit intent: fluency
Original:  When combined with a machine-learning analysis of protein-ligand interaction fingerprints ( IFP ), molecular features that affect ligand unbinding kinetics can be identified.
Revised:  When combined with a machine-learning analysis of protein-ligand interaction fingerprints ( IFPs ), molecular features that affect ligand unbinding kinetics can be identified.
Edit intent: clarity
Original: Being permanently confronted with an uncertain world , brains have faced evolutionary pressure to represent this uncertainty in order to respond appropriately .
Revised: Brains need to deal with an uncertain world , brains have faced evolutionary pressure to represent this uncertainty in order to respond appropriately .
Edit intent: clarity
Original: Being permanently confronted with an uncertain world , brains have faced evolutionary pressure to represent this uncertainty in order to respond appropriately .
Revised: Being permanently confronted with an uncertain world .
Edit intent: clarity
Original:  Designing such collections of building block signals -- or more formally, dictionaries of atoms -- that specifically account for the underlying graph structure as well as any available representative training signals has been an active area of research over the last decade.
Revised:  Designing collections of building block signals -- or more formally, dictionaries of atoms -- that specifically account for the underlying graph structure as well as any available representative training signals has been an active area of research over the last decade.
Edit intent: clarity
Original:  This algorithm is presented as two levels of optimizations of doing k-fold cross-validation .
Revised:  This algorithm is presented as two levels of optimizations of doing k-fold CV .
Edit intent: clarity
Original:  The first level of our proposed optimization addresses the most computationally expensive part (i) and makes it remain constant irrespective of k.
Revised:  The first level of our optimization addresses the most computationally expensive part (i) and makes it remain constant irrespective of k.
Edit intent: clarity
Original:  Results: Proposed cross-validation schemes give better and more stable test performance in all the six different real-world datasets, three task types.
Revised:  Results: Proposed CV schemes give better and more stable test performance in all the six different real-world datasets, three task types.
Edit intent: clarity
Original:  Conclusions: In most situations k-fold cross-validation of ESNs and many other RC models can be done for virtually the same time  complexity as a simple single-split validation.
Revised:  Conclusions: In most situations k-fold CV of ESNs and many other RC models can be done for virtually the same time complexity as a simple single-split validation.
Edit intent: clarity
Original:  This enables cross-validation to become a standard practice in reservoir computing .
Revised:  This enables cross-validation to become a standard practice in RC .
Edit intent: clarity
Original:  Although learning computational thinking with App Inventor has been investigated, a question that remains is which programming concepts are typically used and how this compares to other block-based programming environments.
Revised:  Although learning programming with App Inventor has been investigated, a question that remains is which programming concepts are typically used and how this compares to other block-based programming environments.
Edit intent: clarity
Original:  Empirica's architecture is designed to allow for parameterizable experimental designs, re-usable protocols, and rapid development.
Revised:  Empirica's architecture is designed to allow for parameterizable experimental designs, reusable protocols, and rapid development.
Edit intent: fluency
Original: High frame rate videos are increasingly getting popular in recent years majorly driven by strong requirements by the entertainment and streaming industries to provide high quality of experiences to consumers.
Revised: High frame rate videos are increasingly getting popular in recent years , driven by the strong requirements of the entertainment and streaming industries to provide high quality of experiences to consumers.
Edit intent: coherence
Original:  To achieve the best trade-off between the bandwidth requirements and video quality in terms of frame rate adaptation, it is imperative to understand the effects of frame rate on video quality.
Revised:  To achieve the best trade-offs between the bandwidth requirements and video quality in terms of frame rate adaptation, it is imperative to understand the effects of frame rate on video quality.
Edit intent: fluency
Original:  We then devise a novel statistical entropic differencing method based on  Generalized Gaussian Distribution model in spatial and temporal band-pass domain , which measures the difference in quality between the reference and distorted videos.
Revised:  We then devise a novel statistical entropic differencing method based on a Generalized Gaussian Distribution model in spatial and temporal band-pass domain , which measures the difference in quality between the reference and distorted videos.
Edit intent: fluency
Original:  We then devise a novel statistical entropic differencing method based on  Generalized Gaussian Distribution model in spatial and temporal band-pass domain , which measures the difference in quality between the reference and distorted videos.
Revised:  We then devise a novel statistical entropic differencing method based on Generalized Gaussian Distribution model expressed in the spatial and temporal band-pass domain , which measures the difference in quality between the reference and distorted videos.
Edit intent: clarity
Original:  We then devise a novel statistical entropic differencing method based on  Generalized Gaussian Distribution model in spatial and temporal band-pass domain , which measures the difference in quality between the reference and distorted videos.
Revised:  We then devise a novel statistical entropic differencing method based on Generalized Gaussian Distribution model in spatial and temporal band-pass domains , which measures the difference in quality between the reference and distorted videos.
Edit intent: fluency
Original:  We then devise a novel statistical entropic differencing method based on  Generalized Gaussian Distribution model in spatial and temporal band-pass domain , which measures the difference in quality between the reference and distorted videos.
Revised:  We then devise a novel statistical entropic differencing method based on Generalized Gaussian Distribution model in spatial and temporal band-pass domain , which measures the difference in quality between reference and distorted videos.
Edit intent: fluency
Original:  When lowering the amount of labeled data to one hour, our model outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data.
Revised:  When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data.
Edit intent: clarity
Original:  Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/ noisy test sets.
Revised:  Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/ other test sets.
Edit intent: clarity
Original:  In this work, we introduce an active inference formulation of the Watt centrifugal governor, a system often portrayed as the canonical `` anti-representational '' metaphor for cognition.
Revised:  In this work, we introduce an active inference formulation of the Watt centrifugal governor, a system often portrayed as the canonical " anti-representational '' metaphor for cognition.
Edit intent: fluency
Original:  In this work, we introduce an active inference formulation of the Watt centrifugal governor, a system often portrayed as the canonical `` anti-representational '' metaphor for cognition.
Revised:  In this work, we introduce an active inference formulation of the Watt centrifugal governor, a system often portrayed as the canonical `` anti-representational " metaphor for cognition.
Edit intent: fluency
Original:  We identify a generative model of a steam engine for the governor, and derive a set of equations describing ``perception'' and ``action'' processes as a form of prediction error minimisation.
Revised:  We identify a generative model of a steam engine for the governor, and derive a set of equations describing "perception" and "action" processes as a form of prediction error minimisation.
Edit intent: fluency
Original:  On the challenging GQA dataset, this approach is competitive with non-symbolic neural models while also interpretable by construction and composable with arbitrary pre-trained visual representation learning .
Revised:  On the challenging GQA dataset, this framework is used to perform in-depth, disentangled comparisons between well-known VQA models leading to informative insights regarding the participating models as well as the task .
Edit intent: clarity
Original:  Various benchmarks for reasoning across language and vision like VQA, VCR and more recently GQA for compositional question answering facilitate scientific progress from perception models to visual reasoning.
Edit intent: coherence
Original:  However, recent advances  are still primarily driven by perception improvements (e.g. scene graph generation) rather than reasoning.
Revised:  However, recent advances in this area are still primarily driven by perception improvements (e.g. scene graph generation) rather than reasoning.
Edit intent: clarity
Original: Transformer-based models have achieved state-of-the-art results in many natural language processing (NLP) tasks.
Revised: Transformer-based models have achieved state-of-the-art results in many natural language processing tasks.
Edit intent: clarity
Original:  The self-attention architecture allows us to combine information from all elements of a sequence into context-aware representations.
Revised:  The self-attention architecture allows transformer to combine information from all elements of a sequence into context-aware representations.
Edit intent: fluency