wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this paper, we propose a pre-training and fine-tuning framework  PF-HIN  to capture the features of a HIN . Unlike traditional GNNs that have to train the whole model for each downstream task , PF-HIN only needs to fine-tune the model using the pre-trained parameters and minimal extra task-specific parameters, thus improving the model efficiency and effectiveness.
Revised:  In this paper, we propose a pre-training and fine-tuning framework PF-HIN , to capture the features of a HIN . Unlike traditional GNNs that have to train the whole model for each downstream task , PF-HIN only needs to fine-tune the model using the pre-trained parameters and minimal extra task-specific parameters, thus improving the model efficiency and effectiveness.
Edit intent: fluency
Original:  In this paper, we propose a pre-training and fine-tuning framework  PF-HIN  to capture the features of a HIN . Unlike traditional GNNs that have to train the whole model for each downstream task , PF-HIN only needs to fine-tune the model using the pre-trained parameters and minimal extra task-specific parameters, thus improving the model efficiency and effectiveness.
Revised:  In this paper, we propose a pre-training and fine-tuning framework PF-HIN to capture the features of a heterogeneous information network . Unlike traditional GNNs that have to train the whole model for each downstream task , PF-HIN only needs to fine-tune the model using the pre-trained parameters and minimal extra task-specific parameters, thus improving the model efficiency and effectiveness.
Edit intent: meaning-changed
Original:  In this paper, we propose a pre-training and fine-tuning framework  PF-HIN  to capture the features of a HIN . Unlike traditional GNNs that have to train the whole model for each downstream task , PF-HIN only needs to fine-tune the model using the pre-trained parameters and minimal extra task-specific parameters, thus improving the model efficiency and effectiveness.
Revised:  In this paper, we propose a pre-training and fine-tuning framework PF-HIN to capture the features of a HIN . Unlike traditional network representation learning models that have to train the whole model for each downstream task , PF-HIN only needs to fine-tune the model using the pre-trained parameters and minimal extra task-specific parameters, thus improving the model efficiency and effectiveness.
Edit intent: meaning-changed
Original:  In this paper, we propose a pre-training and fine-tuning framework  PF-HIN  to capture the features of a HIN . Unlike traditional GNNs that have to train the whole model for each downstream task , PF-HIN only needs to fine-tune the model using the pre-trained parameters and minimal extra task-specific parameters, thus improving the model efficiency and effectiveness.
Revised:  In this paper, we propose a pre-training and fine-tuning framework PF-HIN to capture the features of a HIN . Unlike traditional GNNs that have to train the entire model all over again for every downstream task and dataset , PF-HIN only needs to fine-tune the model using the pre-trained parameters and minimal extra task-specific parameters, thus improving the model efficiency and effectiveness.
Edit intent: clarity
Original:  In this paper, we propose a pre-training and fine-tuning framework  PF-HIN  to capture the features of a HIN . Unlike traditional GNNs that have to train the whole model for each downstream task , PF-HIN only needs to fine-tune the model using the pre-trained parameters and minimal extra task-specific parameters, thus improving the model efficiency and effectiveness.
Revised:  In this paper, we propose a pre-training and fine-tuning framework PF-HIN to capture the features of a HIN . Unlike traditional GNNs that have to train the whole model for each downstream task , PF-HIN only needs to fine-tune the model and a small number of extra task-specific parameters, thus improving the model efficiency and effectiveness.
Edit intent: clarity
Original:  In this paper, we propose a pre-training and fine-tuning framework  PF-HIN  to capture the features of a HIN . Unlike traditional GNNs that have to train the whole model for each downstream task , PF-HIN only needs to fine-tune the model using the pre-trained parameters and minimal extra task-specific parameters, thus improving the model efficiency and effectiveness.
Revised:  In this paper, we propose a pre-training and fine-tuning framework PF-HIN to capture the features of a HIN . Unlike traditional GNNs that have to train the whole model for each downstream task , PF-HIN only needs to fine-tune the model using the pre-trained parameters and minimal extra task-specific parameters, thus improving model efficiency and effectiveness.
Edit intent: fluency
Original:  Specifically, in pre-training phase , we first use a ranking-based BFS strategy to form the input node sequence. Then inspired by BERT, we adopt deep bi-directional transformer encoders to train the model, which is a variant of GNN aggregator that is more powerful than traditional deep neural networks like CNN and LSTM. The model is pre-trained based on two tasks, i.e., masked node modeling (MNM) and adjacent node prediction (ANP).
Revised:  Specifically, in pre-training phase , we first transform the neighborhood of a given node into a sequence. PF-HIN is pre-trained based on two tasks, i.e., masked node modeling (MNM) and adjacent node prediction (ANP).
Edit intent: clarity
Original:  The model is pre-trained based on two tasks, i.e., masked node modeling (MNM) and adjacent node prediction (ANP). Additionally, we leverage factorized embedding parameterization and cross-layer parameter sharing to reduce the parameters.
Revised:  The model is pre-trained based on two tasks, i.e., masked node modeling (MNM) and adjacent node prediction . We adopt deep bi-directional transformer encoders to train the model, and leverage factorized embedding parameterization and cross-layer parameter sharing to reduce the parameters.
Edit intent: meaning-changed
Original:  In  fine-tuning stage, we choose four benchmark downstream tasks, i.e., link prediction, similarity search, node classification and node clustering. We use node sequence pairs as input for link prediction and similarity search, and a single node sequence as input for node classification and clustering. The experimental results of the above taskson four real-world datasetsverify the advancement of PF-HIN , as it outperforms state-of-the-art alternatives consistently and significantly .
Revised:  In fine-tuning stage, we choose four benchmark downstream tasks, i.e., link prediction, similarity search, node classification , and node clustering. PF-HIN , as it outperforms state-of-the-art alternatives consistently and significantly .
Edit intent: coherence
Original:  The experimental results of the above taskson four real-world datasetsverify the advancement of PF-HIN , as it outperforms state-of-the-art alternatives consistently and significantly .
Revised:  The experimental results of the above taskson four real-world datasetsverify the advancement of PF-HIN , as it outperforms state-of-the-art alternatives on each of these tasks, on four datasets .
Edit intent: clarity
Original:  In this paper, we study the effects of clustering on the spread of sequential strains of a pathogen using the generating function formulation under a complete cross immunity coupling. We derive conditions for the epidemic threshold of the first strain and the threshold of coexistence of the second strain.
Revised:  In this paper, we study the effects of clustering on the spread of sequential strains of a pathogen using the generating function formulation under a complete cross-immunity coupling, deriving conditions for the epidemic threshold of the first strain and the threshold of coexistence of the second strain.
Edit intent: fluency
Original:  Clustering reduces the coexistence threshold of the second strain and its outbreak size . We apply our model to the study of multilayer clustered networks and observe the fracturing of the residual graph experimentally .
Revised:  Clustering reduces the coexistence threshold of the second strain and its outbreak size in Poisson networks, whilst exhibiting the opposite effects on uniform-degree models. We conclude that clustering within a population must increase the ability of the second wave of an epidemic to spread over a network. We apply our model to the study of multilayer clustered networks and observe the fracturing of the residual graph experimentally .
Edit intent: meaning-changed
Original:  We study the above questions on two datasets with thousands of labelled news  , and our results show that: (1) GNNs can indeed achieve comparable or superior performance without any text information to state-of-the-art methods.
Revised:  We study the above questions on two datasets with thousands of labelled news items , and our results show that: (1) GNNs can indeed achieve comparable or superior performance without any text information to state-of-the-art methods.
Edit intent: clarity
Original:  We study the above questions on two datasets with thousands of labelled news  , and our results show that: (1) GNNs can indeed achieve comparable or superior performance without any text information to state-of-the-art methods.
Revised:  We study the above questions on two datasets with thousands of labelled news , and our results show that: (1) GNNs can achieve comparable or superior performance without any text information to state-of-the-art methods.
Edit intent: clarity
Original: This paper considers a novel formulation of inverse reinforcement learning ~ (IRL) with behavioral economics constraints to address inverse sequential hypothesis testing (SHT) and inverse search in Bayesian agents. We first estimate the stopping and search costs by observing the actions of these agents using Bayesian revealed preference from microeconomicsand rational inattention from behavioral economics.
Revised: This paper considers a novel formulation of inverse reinforcement learning ~ (IRL) framework for Bayesian stopping time problems. By observing the actions of these agents using Bayesian revealed preference from microeconomicsand rational inattention from behavioral economics.
Edit intent: clarity
Original:  We first estimate the stopping and search costs by observing the actions of these agents using Bayesian revealed preference from microeconomicsand rational inattention from behavioral economics. We also solve the inverse problem of the more general rationally inattentive SHT where the agent incorporates controlled sensing by optimally choosing from various sensing modes. Second,we design statistical hypothesis tests with bounded Type-I and Type-II error probabilities to detect if the agents are Bayesian utility maximizers when their actions are measured in noise. By dynamically tuning the prior specified to the agents, we formulate an%DIFDELCMD < {\em %%% active IRL framework which enhances these detection tests and minimizes their Type-II and Type-I error probabilities of utility maximization detection.
Revised:  We first estimate the stopping and search costs by observing the actions of %DIFDELCMD < {\em %%% active IRL framework which enhances these detection tests and minimizes their Type-II and Type-I error probabilities of utility maximization detection.
Edit intent: meaning-changed
Original:  By dynamically tuning the prior specified to the agents, we formulate an%DIFDELCMD < {\em %%% active IRL framework which enhances these detection tests and minimizes their Type-II and Type-I error probabilities of utility maximization detection.
Revised:  By dynamically tuning the prior specified to the agents, we formulate an%DIFDELCMD < {\em %%% IRL framework which enhances these detection tests and minimizes their Type-II and Type-I error probabilities of utility maximization detection.
Edit intent: clarity
Original:  By dynamically tuning the prior specified to the agents, we formulate an%DIFDELCMD < {\em %%% active IRL framework which enhances these detection tests and minimizes their Type-II and Type-I error probabilities of utility maximization detection. Finally, we give a finite sample complexity result which provides finite sample bounds on the error probabilitiesof the detection tests .
Revised:  By dynamically tuning the prior specified to the agents, we formulate an%DIFDELCMD < {\em %%% active a Bayesian decision maker, we provide a necessary and sufficient condition to identify if these actions are consistent with optimizing a cost function; then we construct set valued estimates of the cost function. To achieve this IRL objective, we use novel ideas from Bayesian revealed preferences stemming from microeconomics. To illustrate our IRL scheme,we consider two important examples of stopping time problems, namely, sequential hypothesis testing and Bayesian search. Finally, for finite datasets, we propose an IRL detection algorithm and give finite sample bounds on its error probabilities. Also we discuss how to identify \epsilon-optimal Bayesian decision makers and perform IRL .
Edit intent: meaning-changed
Original:  The  algorithm inherits previous algorithmic improvements, such as tightly-coupled multi-shifts and Aggressive Early Deflation (AED), and also incorporates several new ideas that significantly improve the performance.
Revised:  The task-based algorithm inherits previous algorithmic improvements, such as tightly-coupled multi-shifts and Aggressive Early Deflation (AED), and also incorporates several new ideas that significantly improve the performance.
Edit intent: meaning-changed
Original:  Due to the mesh-free nature of deep learning, SP-NND is capable of solving high-dimensional problems on complicated domains with multiple solutions, while existing methods focus on merely one or two-dimensional regular domains and are more expensive than SP-NND in operation counts.
Revised:  Due to the mesh-free nature of deep learning, the proposed method is capable of solving high-dimensional problems on complicated domains with multiple solutions, while existing methods focus on merely one or two-dimensional regular domains and are more expensive than SP-NND in operation counts.
Edit intent: clarity
Original:  Due to the mesh-free nature of deep learning, SP-NND is capable of solving high-dimensional problems on complicated domains with multiple solutions, while existing methods focus on merely one or two-dimensional regular domains and are more expensive than SP-NND in operation counts.
Revised:  Due to the mesh-free nature of deep learning, SP-NND is capable of solving high-dimensional problems on complicated domains with multiple solutions, while existing methods focus on merely one or two-dimensional regular domains and are more expensive in operation counts.
Edit intent: clarity
Original:  In this paper, we describe our approach to 1) collect brushstrokes and hand-brush motion samples from an artist, and 2) train a generative model to generate brushstrokes that pertains to the artist's style, and 3) integrate the learned model on a robot arm to paint on a canvas .
Revised:  In this paper, we describe our approach to 1) collect brushstrokes and hand-brush motion samples from an artist, and 2) train a generative model to generate brushstrokes that pertains to the artist's style, and 3) fine tune a stroke-based rendering model to work with our robotic painting setup. We will report on the integration of these three steps in a separate publication .
Edit intent: clarity
Original:  In a preliminary study, 71\% of human evaluators find our robot's paintings pertaining to the characteristics of the artist's style  .
Revised:  In a preliminary study, 71\% of human evaluators find our robot's paintings pertaining to the characteristics of the artist's style . Moreover, 58\% of participants could not distinguish a painting made by our method from a visually similar painting created by a human artist .
Edit intent: meaning-changed
Original:  After EEG signal acquisition, a closed-loop EEG-based BCI system also includes signal processing, feature engineering, and classification /regression blocks before sending out the control signal  , whereas previous approaches only considered TL in one or two such components.
Revised:  After electroencephalogram (EEG) signal acquisition, a closed-loop EEG-based BCI system also includes signal processing, feature engineering, and classification /regression blocks before sending out the control signal , whereas previous approaches only considered TL in one or two such components.
Edit intent: clarity
Original:  After EEG signal acquisition, a closed-loop EEG-based BCI system also includes signal processing, feature engineering, and classification /regression blocks before sending out the control signal  , whereas previous approaches only considered TL in one or two such components.
Revised:  After EEG signal acquisition, a closed-loop MI-based BCI system also includes signal processing, feature engineering, and classification /regression blocks before sending out the control signal , whereas previous approaches only considered TL in one or two such components.
Edit intent: meaning-changed
Original:  After EEG signal acquisition, a closed-loop EEG-based BCI system also includes signal processing, feature engineering, and classification /regression blocks before sending out the control signal  , whereas previous approaches only considered TL in one or two such components.
Revised:  After EEG signal acquisition, a closed-loop EEG-based BCI system also includes signal processing, feature engineering, and classification blocks before sending out the control signal , whereas previous approaches only considered TL in one or two such components.
Edit intent: clarity
Original:  After EEG signal acquisition, a closed-loop EEG-based BCI system also includes signal processing, feature engineering, and classification /regression blocks before sending out the control signal  , whereas previous approaches only considered TL in one or two such components.
Revised:  After EEG signal acquisition, a closed-loop EEG-based BCI system also includes signal processing, feature engineering, and classification /regression blocks before sending out the control signal to an external device , whereas previous approaches only considered TL in one or two such components.
Edit intent: clarity
Original:  After electroencephalogram (EEG) signal acquisition  , a closed-loop MI-based BCI system also includes signal processing, feature engineering, and classification blocks before sending out the control signal to an external device, whereas previous approaches only considered TL in one or two such components.
Revised:  While a closed-loop MI-based BCI system, after electroencephalogram (EEG) signal acquisition , a closed-loop MI-based BCI system also includes signal processing, feature engineering, and classification blocks before sending out the control signal to an external device, whereas previous approaches only considered TL in one or two such components.
Edit intent: coherence
Original:  After electroencephalogram (EEG) signal acquisition  , a closed-loop MI-based BCI system also includes signal processing, feature engineering, and classification blocks before sending out the control signal to an external device, whereas previous approaches only considered TL in one or two such components.
Revised:  After electroencephalogram (EEG) signal acquisition and temporal filtering, includes spatial filtering , a closed-loop MI-based BCI system also includes signal processing, feature engineering, and classification blocks before sending out the control signal to an external device, whereas previous approaches only considered TL in one or two such components.
Edit intent: meaning-changed
Original:  After electroencephalogram (EEG) signal acquisition  , a closed-loop MI-based BCI system also includes signal processing, feature engineering, and classification blocks before sending out the control signal to an external device, whereas previous approaches only considered TL in one or two such components.
Revised:  After electroencephalogram (EEG) signal acquisition , feature engineering, and classification blocks before sending out the control signal to an external device, whereas previous approaches only considered TL in one or two such components.
Edit intent: coherence
Original:  After electroencephalogram (EEG) signal acquisition  , a closed-loop MI-based BCI system also includes signal processing, feature engineering, and classification blocks before sending out the control signal to an external device, whereas previous approaches only considered TL in one or two such components.
Revised:  After electroencephalogram (EEG) signal acquisition , a closed-loop MI-based BCI system also includes signal processing, feature engineering, and classification blocks before sending out the control signal to an external device, previous approaches only considered TL in one or two such components.
Edit intent: clarity
Original:  We show that collaboration indeed decreases regret for this algorithm, when the number of malicious agents is small compared to the number of arms, and crucially without assumptions on the malicious agents' behavior . Thus, our algorithm is robust against any malicious recommendation strategy.
Revised:  We show that collaboration indeed decreases regret for this algorithm, when the number of malicious agents is small compared to the number of arms, and crucially without assumptions on the malicious agents' behavior , thus ensuring that our algorithm is robust against any malicious recommendation strategy.
Edit intent: coherence
Original: The Audio-Visual Scene-aware Dialog (AVSD) task requires an agent to indulge in a natural conversation with a human about a given video. Specifically, apart from the video frames, the agent receives the audio, brief captions, and a dialog history, and the task is to produce the correct answer to a question about the video. Due to the diversity in the type of inputs, this task poses a very challenging multimodal reasoning problem. Current approaches to AVSD either use global video-level features or those from a few sampled frames, and thus lack the ability to explicitly capture relevant visual regions or their interactions for answer generation. To this end, we propose a novel spatio-temporal scene graph representation (STSGR) modeling fine-grained information flows within videos.
Revised: The Audio-Visual Scene-aware Dialog (AVSD) task requires an agent to indulge in a natural conversation with a human about the audio-visual content. This task thus poses a challenging multi-modal representation learning and reasoning scenario, advancements into which could influence several human-machine interaction applications. To solve this task, we introduce a semantics-controlled multi-modal shuffled Transformer reasoning framework, consisting of a sequence of Transformer modules, each taking a modality as input and producing representations conditioned on the input question. Our proposed Transformer variant uses a shuffling scheme on their multi-head outputs, demonstrating better regularization. To encode fine-grained visual information, we present a novel dynamic scene graph representation (STSGR) modeling fine-grained information flows within videos.
Edit intent: clarity
Original:  To this end, we propose a novel spatio-temporal scene graph representation (STSGR) modeling fine-grained information flows within videos. Specifically, on an input video sequence, STSGR (i) creates a two-stream visual and semantic scene graph on every frame, (ii) conducts intra-graph reasoning using node and edge convolutions generating visual memories, and (iii) applies inter-graph aggregation to capture their temporal evolutions.
Revised:  To this end, we propose a novel spatio-temporal scene graph representation learning pipeline that consists of an intra-frame reasoning layer producing spatio-semantic graph representations for every frame, (ii) conducts intra-graph reasoning using node and edge convolutions generating visual memories, and (iii) applies inter-graph aggregation to capture their temporal evolutions.
Edit intent: clarity