wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our multiple levels of consistency describe in what sense an online algorithm plays `` just like a fixed Nash equilibrium '' . These notions further illustrate the difference in perfect and imperfect information settings, as the same consistency guarantees have different worst-case online performance in perfect and imperfect information games.
Revised:  Our multiple levels of consistency describe in what sense an online algorithm plays `` just like a fixed Nash equilibrium " . These notions further illustrate the difference in perfect and imperfect information settings, as the same consistency guarantees have different worst-case online performance in perfect and imperfect information games.
Edit intent: fluency
Original:  Our multiple levels of consistency describe in what sense an online algorithm plays `` just like a fixed Nash equilibrium '' . These notions further illustrate the difference in perfect and imperfect information settings, as the same consistency guarantees have different worst-case online performance in perfect and imperfect information games.
Revised:  Our multiple levels of consistency describe in what sense an online algorithm plays `` just like a fixed Nash equilibrium '' . These notions further illustrate the difference between perfect and imperfect information settings, as the same consistency guarantees have different worst-case online performance in perfect and imperfect information games.
Edit intent: fluency
Original:  Our definition of soundness and the consistency hierarchy finally provide appropriate tools to analyze online algorithms in  imperfect information games.
Revised:  The definitions of soundness and the consistency hierarchy finally provide appropriate tools to analyze online algorithms in imperfect information games.
Edit intent: fluency
Original:  Our definition of soundness and the consistency hierarchy finally provide appropriate tools to analyze online algorithms in  imperfect information games.
Revised:  Our definition of soundness and the consistency hierarchy finally provide appropriate tools to analyze online algorithms in repeated imperfect information games.
Edit intent: clarity
Original:  We thus inspect some of the previous online algorithms in a new light, bringing new insights into their worst case performance guarantees.
Revised:  We thus inspect some of the previous online algorithms in a new light, bringing new insights into their worst-case performance guarantees.
Edit intent: fluency
Original: In this paper  the notions of {\em trapping} and {\em confining} the robber on a graph are introduced.
Revised: In this paper , the notions of {\em trapping} and {\em confining} the robber on a graph are introduced.
Edit intent: fluency
Original:  We  present some necessary conditions for graphs G not containing the path on k vertices (  for some k\ge 4 ) as an induced subgraph so that k-3 cops do not have a winning strategy on G. Utilizing the latter conditions together with a characterization of cographs , we show that on cograph the robber can always be confined by one cop.
Revised:  We particularly present some necessary conditions for graphs G not containing the path on k vertices ( for some k\ge 4 ) as an induced subgraph so that k-3 cops do not have a winning strategy on G. Utilizing the latter conditions together with a characterization of cographs , we show that on cograph the robber can always be confined by one cop.
Edit intent: coherence
Original:  We  present some necessary conditions for graphs G not containing the path on k vertices (  for some k\ge 4 ) as an induced subgraph so that k-3 cops do not have a winning strategy on G. Utilizing the latter conditions together with a characterization of cographs , we show that on cograph the robber can always be confined by one cop.
Revised:  We present some necessary conditions for graphs G not containing the path on k vertices ( for some k\ge 4 , so that k-3 cops do not have a winning strategy on G. Utilizing the latter conditions together with a characterization of cographs , we show that on cograph the robber can always be confined by one cop.
Edit intent: coherence
Original:  We  present some necessary conditions for graphs G not containing the path on k vertices (  for some k\ge 4 ) as an induced subgraph so that k-3 cops do not have a winning strategy on G. Utilizing the latter conditions together with a characterization of cographs , we show that on cograph the robber can always be confined by one cop.
Revised:  We present some necessary conditions for graphs G not containing the path on k vertices ( for some k\ge 4 ) as an induced subgraph so that k-3 cops do not have a strategy to capture or confine the robber on G. Utilizing the latter conditions together with a characterization of cographs , we show that on cograph the robber can always be confined by one cop.
Edit intent: clarity
Original:  We particularly present some necessary conditions for graphs G not containing the path on k vertices (referred to as P_k-free graphs) for some k\ge 4, so that k-3 cops do not have a strategy to capture or confine the robber on G. Utilizing the latter conditions, we show that the number of vertices of a connected cograph on which a cop does not have a strategy to confine the robber  a tight lower-bound of eight.
Revised:  We present some structural necessary conditions for graphs G not containing the path on k vertices (referred to as P_k-free graphs) for some k\ge 4, so that k-3 cops do not have a strategy to capture or confine the robber on G. Utilizing the latter conditions, we show that the number of vertices of a connected cograph on which a cop does not have a strategy to confine the robber a tight lower-bound of eight.
Edit intent: clarity
Original:  We particularly present some necessary conditions for graphs G not containing the path on k vertices (referred to as P_k-free graphs) for some k\ge 4, so that k-3 cops do not have a strategy to capture or confine the robber on G. Utilizing the latter conditions, we show that the number of vertices of a connected cograph on which a cop does not have a strategy to confine the robber  a tight lower-bound of eight.
Revised:  We particularly present some necessary conditions for graphs G not containing the path on k vertices (referred to as P_k-free graphs) for some k\ge 4, so that k-3 cops do not have a strategy to capture or confine the robber on G. Utilizing such conditions, we show that the number of vertices of a connected cograph on which a cop does not have a strategy to confine the robber a tight lower-bound of eight.
Edit intent: clarity
Original:  We particularly present some necessary conditions for graphs G not containing the path on k vertices (referred to as P_k-free graphs) for some k\ge 4, so that k-3 cops do not have a strategy to capture or confine the robber on G. Utilizing the latter conditions, we show that the number of vertices of a connected cograph on which a cop does not have a strategy to confine the robber  a tight lower-bound of eight.
Revised:  We particularly present some necessary conditions for graphs G not containing the path on k vertices (referred to as P_k-free graphs) for some k\ge 4, so that k-3 cops do not have a strategy to capture or confine the robber on G. Utilizing the latter conditions, we show that the number of vertices of a connected cograph on which one cop does not have a strategy to confine the robber a tight lower-bound of eight.
Edit intent: clarity
Original:  We particularly present some necessary conditions for graphs G not containing the path on k vertices (referred to as P_k-free graphs) for some k\ge 4, so that k-3 cops do not have a strategy to capture or confine the robber on G. Utilizing the latter conditions, we show that the number of vertices of a connected cograph on which a cop does not have a strategy to confine the robber  a tight lower-bound of eight.
Revised:  We particularly present some necessary conditions for graphs G not containing the path on k vertices (referred to as P_k-free graphs) for some k\ge 4, so that k-3 cops do not have a strategy to capture or confine the robber on G. Utilizing the latter conditions, we show that the number of vertices of a connected cograph on which a cop does not have a strategy to confine the robber has a tight lower-bound of eight.
Edit intent: fluency
Original: In this paper, we present a new  sound source DOA estimation and tracking system based on the well known SRP-PHAT algorithm and a three-dimensional Convolutional Neural Network.
Revised: In this paper, we present a new sound source DOA estimation and tracking system based on the well-known SRP-PHAT algorithm and a three-dimensional Convolutional Neural Network.
Edit intent: fluency
Original: Robust automatic speech recognition (ASR) system exploits state-of-the-art deep neural networks (DNN) based acoustic model (AM) trained with Lattice Free-Maximum Mutual Information (LF-MMI) criterion and n-gram language models.
Revised: Robust automatic speech recognition (ASR) system exploits deep neural network (DNN) based acoustic model (AM) trained with Lattice Free-Maximum Mutual Information (LF-MMI) criterion and n-gram language models.
Edit intent: clarity
Original: Robust automatic speech recognition (ASR) system exploits state-of-the-art deep neural networks (DNN) based acoustic model (AM) trained with Lattice Free-Maximum Mutual Information (LF-MMI) criterion and n-gram language models.
Revised: Robust automatic speech recognition (ASR) system exploits state-of-the-art deep neural networks (DNN) based acoustic models (AM) trained with Lattice Free-Maximum Mutual Information (LF-MMI) criterion and n-gram language models.
Edit intent: fluency
Original:  Impact of the parameter quantization on the overall word recognition performance is studied in this paper.
Revised:  The impact of parameter quantization on the overall word recognition performance is studied in this paper.
Edit intent: fluency
Original:  Following three approaches are presented: (i) AM trained in Kaldi framework with conventional factorized TDNN (TDNN-F) architecture . (ii) the TDNN  built in Kaldi is loaded into the Pytorch toolkit using a C++ wrapper .
Revised:  Following approaches are presented: (i) AM trained in Kaldi framework with conventional factorized TDNN (TDNN-F) architecture . (ii) the TDNN built in Kaldi is loaded into the Pytorch toolkit using a C++ wrapper .
Edit intent: coherence
Original:  Following three approaches are presented: (i) AM trained in Kaldi framework with conventional factorized TDNN (TDNN-F) architecture . (ii) the TDNN  built in Kaldi is loaded into the Pytorch toolkit using a C++ wrapper .
Revised:  Following three approaches are presented: (i) AM trained in Kaldi framework with conventional factorized TDNN (TDNN-F) architecture , (ii) the TDNN built in Kaldi is loaded into the Pytorch toolkit using a C++ wrapper .
Edit intent: fluency
Original:  Following three approaches are presented: (i) AM trained in Kaldi framework with conventional factorized TDNN (TDNN-F) architecture . (ii) the TDNN  built in Kaldi is loaded into the Pytorch toolkit using a C++ wrapper .
Revised:  Following three approaches are presented: (i) AM trained in Kaldi framework with conventional factorized TDNN (TDNN-F) architecture . (ii) the TDNN built in Kaldi loaded into the Pytorch toolkit using a C++ wrapper .
Edit intent: clarity
Original:  Following three approaches are presented: (i) AM trained in Kaldi framework with conventional factorized TDNN (TDNN-F) architecture . (ii) the TDNN  built in Kaldi is loaded into the Pytorch toolkit using a C++ wrapper .
Revised:  Following three approaches are presented: (i) AM trained in Kaldi framework with conventional factorized TDNN (TDNN-F) architecture . (ii) the TDNN built in Kaldi is loaded into the PyTorch toolkit using a C++ wrapper .
Edit intent: fluency
Original:  It seems that there should be other more interesting applications to the presented approach.
Revised:  It seems that there should be other interesting applications to the presented approach.
Edit intent: clarity
Original: Current methods for learning visually grounded language from videos often rely on time-consuming and expensive data collection , such as human annotated textual summaries or machine generated automatic speech recognition  transcripts.
Revised: Current methods for learning visually grounded language from videos often rely on time-consuming and expensive data collection , such as human generated captions or machine generated automatic speech recognition transcripts.
Edit intent: clarity
Original: Current methods for learning visually grounded language from videos often rely on time-consuming and expensive data collection , such as human annotated textual summaries or machine generated automatic speech recognition  transcripts.
Revised: Current methods for learning visually grounded language from videos often rely on time-consuming and expensive data collection , such as human annotated textual summaries or machine generated automatic speech recognition (ASR) transcripts.
Edit intent: clarity
Original:  In this work, we introduce  Audio-Video Language Network (AVLnet), a self-supervised network that learns a shared audio-visual embedding space directly from raw video inputs.
Revised:  In this work, we introduce the Audio-Video Language Network (AVLnet), a self-supervised network that learns a shared audio-visual embedding space directly from raw video inputs.
Edit intent: fluency
Original:  We circumvent the need for annotation and instead learn audio-visual language representations directly from randomly segmented video clips and their raw audio waveforms.
Revised:  To circumvent the need for annotation and instead learn audio-visual language representations directly from randomly segmented video clips and their raw audio waveforms.
Edit intent: fluency
Original:  We circumvent the need for annotation and instead learn audio-visual language representations directly from randomly segmented video clips and their raw audio waveforms.
Revised:  We circumvent the need for annotation and instead learn audio-visual representations from randomly segmented video clips and their raw audio waveforms.
Edit intent: clarity
Original:  Finally, we perform analysis of AVLnet's learned representations, showing our model has learned to relate visual objects with salient words and natural sounds .
Revised:  Finally, we perform analysis of AVLnet's learned representations, showing our model utilizes speech and natural sounds .
Edit intent: clarity
Original:  We want to be able to tell efficiently systems with many non-trivial solutions or near solutions x \ne 0 from systems that are far from having a solution.
Revised:  Our goal is to tell efficiently systems with many non-trivial solutions or near solutions x \ne 0 from systems that are far from having a solution.
Edit intent: clarity
Original:  We want to be able to tell efficiently systems with many non-trivial solutions or near solutions x \ne 0 from systems that are far from having a solution.
Revised:  We want to be able to tell efficiently systems with many non-trivial solutions or near-solutions x \ne 0 from systems that are far from having a solution.
Edit intent: fluency
Original:  To this end, we propose a novel topologically-aware message passing scheme based on subgraph isomorphism counting .
Revised:  To this end, we propose a novel topologically-aware message passing scheme based on substructure encoding .
Edit intent: clarity
Original:  We show that our architectureallows incorporating domain-specific inductive biases and that it is strictly more expressive than the WL test  .
Revised:  We theoretically analyse the expressive power of our architecture, showing that it is strictly more expressive than the WL test .
Edit intent: clarity
Original:  Importantly, in contrast to recent works on the expressivity of GNNs, we do not attempt to adhere to the WL hierarchy;
Revised:  Importantly, we do not attempt to adhere to the WL hierarchy;
Edit intent: coherence
Original:  We extensively evaluate our method on graph classification and regression tasks and show state-of-the-art results on multiple datasets including molecular graphs and social networks. 
Revised:  We perform an extensive experimental evaluation on graph classification and regression tasks and show state-of-the-art results on multiple datasets including molecular graphs and social networks. 
Edit intent: clarity
Original:  We extensively evaluate our method on graph classification and regression tasks and show state-of-the-art results on multiple datasets including molecular graphs and social networks. 
Revised:  We extensively evaluate our method on graph classification and regression tasks and obtain state-of-the-art results on multiple datasets including molecular graphs and social networks. 
Edit intent: clarity
Original:  We extensively evaluate our method on graph classification and regression tasks and show state-of-the-art results on multiple datasets including molecular graphs and social networks. 
Revised:  We extensively evaluate our method on graph classification and regression tasks and show state-of-the-art results in diverse real-world settings including molecular graphs and social networks. 
Edit intent: clarity
Original:  Our experiments (using various neural sequence-to-sequence models) reveal that while the task is challenging, neural models can indeed capture non-trivial mathematical reasoning.
Revised:  Our experiments and analysis reveal that while the task is challenging, neural models can indeed capture non-trivial mathematical reasoning.
Edit intent: clarity
Original:  Our experiments (using various neural sequence-to-sequence models) reveal that while the task is challenging, neural models can indeed capture non-trivial mathematical reasoning.
Revised:  Our experiments (using various neural sequence-to-sequence models) reveal that while the task is challenging, neural models can capture non-trivial mathematical reasoning.
Edit intent: clarity
Original:  We further propose a hierarchical transformer model that outperforms the transformer baseline.
Revised:  We further design a hierarchical transformer model that outperforms the transformer baseline.
Edit intent: clarity
Original:  We further propose a hierarchical transformer model that outperforms the transformer baseline.
Revised:  We further propose a hierarchical transformer that outperforms the transformer baseline.
Edit intent: clarity
Original: Model-based evaluation has been extensively used to estimate performance and reliability metrics of computer systems, especially critical systems , for which experimental approaches are not always applicable.
Revised: Model-based evaluation is extensively used to estimate performance and reliability metrics of computer systems, especially critical systems , for which experimental approaches are not always applicable.
Edit intent: fluency
Original:  A good prototype choice is essential and we propose a practical technique for obtaining iconic examples.
Revised:  A good prototype choice is essential and we propose a technique for obtaining iconic examples.
Edit intent: clarity
Original:  We propose a new modelling technique based on variational autoencoders which uses only the local observations of the agent under control: its observed world state, chosen actions, and received rewards .
Revised:  We propose a new modelling technique based on variational autoencoders which uses only the local observations of the modelling agent ( its observed world state, chosen actions, and received rewards .
Edit intent: clarity
Original:  The model is jointly trained with the agent's decision policy using deep reinforcement learning techniques .
Revised:  The model is jointly trained with the agent's decision policy which is trained via deep reinforcement learning techniques .
Edit intent: clarity
Original:  We provide a comprehensive evaluation and ablation study in diverse multi-agent tasks, showing that our method achieves significantly higher returns than a baseline method which does not use opponent modelling, and comparable performance to an ideal baseline which has full access to opponent information .
Revised:  We provide a comprehensive evaluation and ablation study in diverse multi-agent tasks, showing that our method achieves comparable performance to an ideal baseline which has full access to opponent information .
Edit intent: clarity
Original: Modelling the behaviours of other agents (opponents) is essential for understanding how agents interact and making effective decisions.
Revised: Modelling the behaviours of other agents is essential for understanding how agents interact and making effective decisions.
Edit intent: clarity
Original:  Existing methods for opponent modelling commonly assume knowledge of the local observations and chosen actions of the modelled opponents, which can significantly limit their applicability.
Revised:  Existing methods for agent modelling commonly assume knowledge of the local observations and chosen actions of the modelled opponents, which can significantly limit their applicability.
Edit intent: clarity
Original:  The embeddings are used to augment the modelling agent's decision policy which is trained via deep reinforcement learning;
Revised:  The embeddings are used to augment the controlled agent's decision policy which is trained via deep reinforcement learning;
Edit intent: clarity
Original:  thus  the policy does not require access to opponent observations .
Revised:  thus , during execution, the policy does not require access to opponent observations .
Edit intent: clarity
Original:  We provide a comprehensive evaluation and ablation study in diverse multi-agent tasks , showing that our method achieves comparable performance to an ideal baseline which has full access to opponent's information, and significantly higher returns than a baseline method which does not use the learned embeddings .
Revised:  We provide a comprehensive evaluation and ablation study in diverse multi-agent environments , showing that our method achieves comparable performance to an ideal baseline which has full access to opponent's information, and significantly higher returns than a baseline method which does not use the learned embeddings .
Edit intent: clarity
Original:  We provide a comprehensive evaluation and ablation study in diverse multi-agent tasks , showing that our method achieves comparable performance to an ideal baseline which has full access to opponent's information, and significantly higher returns than a baseline method which does not use the learned embeddings .
Revised:  We provide a comprehensive evaluation and ablation study in diverse multi-agent tasks , showing that our method achieves significantly higher returns than a baseline method which does not use the learned embeddings .
Edit intent: clarity
Original:  We provide a comprehensive evaluation and ablation study in diverse multi-agent tasks , showing that our method achieves comparable performance to an ideal baseline which has full access to opponent's information, and significantly higher returns than a baseline method which does not use the learned embeddings .
Revised:  We provide a comprehensive evaluation and ablation study in diverse multi-agent tasks , showing that our method achieves comparable performance to an ideal baseline which has full access to opponent's information, and significantly higher returns than baseline methods which do not use the learned embeddings .
Edit intent: fluency
Original:  We provide a comprehensive evaluation and ablation study in diverse multi-agent tasks , showing that our method achieves comparable performance to an ideal baseline which has full access to opponent's information, and significantly higher returns than a baseline method which does not use the learned embeddings .
Revised:  We provide a comprehensive evaluation and ablation study in diverse multi-agent tasks , showing that our method achieves comparable performance to an ideal baseline which has full access to opponent's information, and significantly higher returns than a baseline method which does not use the learned representations .
Edit intent: clarity