wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our protocol gives rise to a new family of interactive learning algorithms that offer complementary advantages against traditional algorithms like imitation learning (IL) and reinforcement learning (RL)  . We develop  an algorithm that practically implements this protocoland employ it to train agents in two challenging request-fulfilling problems using purely language-description feedback. Empirical results demonstrate the strengths of our algorithm: compared to RL baselines, it is more sample-efficient;
Revised:  Our protocol gives rise to a new family of interactive learning algorithms that offer complementary advantages against traditional algorithms like imitation learning (IL) and reinforcement learning (RL) . We develop an algorithm that practically implements this protocoland employ it to train agents in two challenging request-fulfilling problems demonstrate the strengths of our algorithm: compared to RL baselines, it is more sample-efficient;
Edit intent: coherence
Original:  compared to IL baselines, it achieves competitive success rates while not requiring feedback providers to have agent-specific expertise. We also provide theoretical guarantees of the algorithm under certain assumptions on the teacher and the environment.
Revised:  compared to IL baselines, it achieves competitive success rates without requiring the teaching agent to be able to demonstrate the desired behavior using the learning agent's actions. Apart from empirical evaluation, we also provide theoretical guarantees of the algorithm under certain assumptions on the teacher and the environment.
Edit intent: meaning-changed
Original:  In this paper, a novel data-efficient approach to transfer the knowledge from a teacher model to a student model is presented. Here, the teacher model uses self-regulation to select appropriate samplesfor training and identifies their significance in the process.
Revised:  In this paper, a novel data-efficient approach to transfer the knowledge from a teacher model to perform distillation using only a subset of the training data, making it more data-efficient. For this purpose, the training of the teacher model is modified to include self-regulation to select appropriate samplesfor training and identifies their significance in the process.
Edit intent: meaning-changed
Original:  Here, the teacher model uses self-regulation to select appropriate samplesfor training and identifies their significance in the process. During distillation, the significance information can be used along with the soft-targets to supervise the students. Depending on the use of self-regulation and sample significance information in supervising the knowledge transfer process, three types of distillations are proposed - significance-based , regulated, and hybrid , respectively.
Revised:  Here, the teacher model uses self-regulation wherein a sample in the training set is used for updating model parameters in the backward pass either if it is misclassified or the model is not confident enough in its prediction. This modification restricts the participation of samples, unlike the conventional training method. The number of times a sample participates in the self-regulated training process is a measure of its significance towards the model's knowledge. The significance values are used to weigh the losses incurred on the corresponding samples in the distillation process. This method is named significance-based , regulated, and hybrid , respectively.
Edit intent: meaning-changed
Original:  Depending on the use of self-regulation and sample significance information in supervising the knowledge transfer process, three types of distillations are proposed - significance-based , regulated, and hybrid , respectively.
Revised:  Depending on the use of self-regulation and sample significance information in supervising the knowledge transfer process, three types of distillations are proposed - significance-based distillation. Two other methods are proposed for comparison where the student model learns by distillation and incorporating self-regulation as the teacher model, either utilizing the significance information computed during the teacher's training or not. These methods are named hybrid and regulated distillations , respectively.
Edit intent: meaning-changed
Original:  In the mammalian brain, the hippocampus is understood to play a significant role in this process, by learning rapidly and consolidating knowledge to the neocortex  over a short term period.
Revised:  In the mammalian brain, the hippocampus is understood to play a significant role in this process, by learning rapidly and consolidating knowledge to the neocortex incrementally over a short term period.
Edit intent: meaning-changed
Original:  In this research we tested whether an artificial hippocampal algorithm , AHA , could be used with a conventional ML model analogous to the neocortex, to achieve one-shot learning both short and long term.
Revised:  In this research we tested whether an artificial hippocampal algorithm (AHA) , could be used with a conventional ML model analogous to the neocortex, to achieve one-shot learning both short and long term.
Edit intent: fluency
Original:  In this research we tested whether an artificial hippocampal algorithm , AHA , could be used with a conventional ML model analogous to the neocortex, to achieve one-shot learning both short and long term.
Revised:  In this research we tested whether an artificial hippocampal algorithm , AHA , could be used with a conventional Machine Learning (ML) model that learns incrementally analogous to the neocortex, to achieve one-shot learning both short and long term.
Edit intent: meaning-changed
Original:  With the growing complexity of CSI  in massive MIMO system , traditional compressive sensing based CSI feedback has become a bottleneck problem that is limited in piratical . Recently, numerous deep learning based CSI feedback approaches demonstrate the efficiency and potential.
Revised:  With the growing complexity of CSI , CSI feedback in massive MIMO system , traditional compressive sensing based CSI feedback has become a bottleneck problem that is limited in piratical . Recently, numerous deep learning based CSI feedback approaches demonstrate the efficiency and potential.
Edit intent: fluency
Original:  With the growing complexity of CSI  in massive MIMO system , traditional compressive sensing based CSI feedback has become a bottleneck problem that is limited in piratical . Recently, numerous deep learning based CSI feedback approaches demonstrate the efficiency and potential.
Revised:  With the growing complexity of CSI in massive MIMO system has become a bottleneck problem, the traditional compressive sensing based CSI feedback has become a bottleneck problem that is limited in piratical . Recently, numerous deep learning based CSI feedback approaches demonstrate the efficiency and potential.
Edit intent: coherence
Original:  With the growing complexity of CSI  in massive MIMO system , traditional compressive sensing based CSI feedback has become a bottleneck problem that is limited in piratical . Recently, numerous deep learning based CSI feedback approaches demonstrate the efficiency and potential.
Revised:  With the growing complexity of CSI in massive MIMO system , traditional compressive sensing based CSI feedback approaches have limited performance . Recently, numerous deep learning based CSI feedback approaches demonstrate the efficiency and potential.
Edit intent: clarity
Original:  With the growing complexity of CSI  in massive MIMO system , traditional compressive sensing based CSI feedback has become a bottleneck problem that is limited in piratical . Recently, numerous deep learning based CSI feedback approaches demonstrate the efficiency and potential.
Revised:  With the growing complexity of CSI in massive MIMO system , traditional compressive sensing based CSI feedback has become a bottleneck problem that is limited in piratical . Recently, numerous deep learning based CSI feedback approaches demonstrate their efficiency and potential.
Edit intent: fluency
Original:  In this paper , from the intrinsic properties of CSI data itself, we devised the corresponding deep learning building blocks to compose a novel neural network CQNet and experiment result shows CQNet outperform the state-of-the-art method with less computational overhead by achieving an average performance improvement of 8.07 \% in both outdoor and indoor scenarios .
Revised:  This paper presents a novel neural network CLNet tailored for CSI feedback problem based on the intrinsic properties of CSI data itself, we devised the corresponding deep learning building blocks to compose a novel neural network CQNet and experiment result shows CQNet outperform the state-of-the-art method with less computational overhead by achieving an average performance improvement of 8.07 \% in both outdoor and indoor scenarios .
Edit intent: meaning-changed
Original:  In this paper , from the intrinsic properties of CSI data itself, we devised the corresponding deep learning building blocks to compose a novel neural network CQNet and experiment result shows CQNet outperform the state-of-the-art method with less computational overhead by achieving an average performance improvement of 8.07 \% in both outdoor and indoor scenarios .
Revised:  In this paper , from the intrinsic properties of CSI . The experiment result shows CQNet outperform the state-of-the-art method with less computational overhead by achieving an average performance improvement of 8.07 \% in both outdoor and indoor scenarios .
Edit intent: coherence
Original:  In this paper , from the intrinsic properties of CSI data itself, we devised the corresponding deep learning building blocks to compose a novel neural network CQNet and experiment result shows CQNet outperform the state-of-the-art method with less computational overhead by achieving an average performance improvement of 8.07 \% in both outdoor and indoor scenarios .
Revised:  In this paper , from the intrinsic properties of CSI data itself, we devised the corresponding deep learning building blocks to compose a novel neural network CQNet and experiment result shows that CLNet outperforms the state-of-the-art method with less computational overhead by achieving an average performance improvement of 8.07 \% in both outdoor and indoor scenarios .
Edit intent: clarity
Original:  In this paper , from the intrinsic properties of CSI data itself, we devised the corresponding deep learning building blocks to compose a novel neural network CQNet and experiment result shows CQNet outperform the state-of-the-art method with less computational overhead by achieving an average performance improvement of 8.07 \% in both outdoor and indoor scenarios .
Revised:  In this paper , from the intrinsic properties of CSI data itself, we devised the corresponding deep learning building blocks to compose a novel neural network CQNet and experiment result shows CQNet outperform the state-of-the-art method by average accuracy improvement of 5.41 \% in both outdoor and indoor scenarios .
Edit intent: meaning-changed
Original:  In this paper , from the intrinsic properties of CSI data itself, we devised the corresponding deep learning building blocks to compose a novel neural network CQNet and experiment result shows CQNet outperform the state-of-the-art method with less computational overhead by achieving an average performance improvement of 8.07 \% in both outdoor and indoor scenarios . In addition, this paper also investigates the reasons for the decrease in model accuracy at large compression rates and proposes a strategy to embed a quantization layer to achieve effective compression, by which the original accuracy loss of 67.19\% on average is reduced to 21.96\% on average, and the compression rate is increased by 8 times on the original benchmark .
Revised:  In this paper , from the intrinsic properties of CSI data itself, we devised the corresponding deep learning building blocks to compose a novel neural network CQNet and experiment result shows CQNet outperform the state-of-the-art method with less computational overhead by achieving an average performance improvement of 8.07 \% in both outdoor and indoor scenarios with average 24.1\% less computational overhead. Codes are available at GitHub .
Edit intent: meaning-changed
Original: Image restoration problems were traditionally formulated as the minimization of variational models, including data-fidelity and regularization terms, performed by optimization methods with well-established convergence properties. Recently, Plug-and-Play (PnP) methods for image restoration have obtained very good results and popularity by introducing, in iterative proximal algorithms, any off-the-shelf denoiser as priors. Deep Convolutional Neural Network (CNN) denoisers specify external priors (related to an outer training set) which well reflect image statistics; however they fail when dealing with unseen noise variance and image patterns in the given image. Conversely, the so-called internal denoisers induce internal priorsta ilored on the observed data, by forcing specific features on the desired image. We propose a new PnP scheme, based on the Half-Quadratic Splitting proximal algorithm, combining external and internal priors.
Revised: Blur and noise corrupting Computed Tomography (CT) images can hide or distort small but important details, negatively affecting the diagnosis. In this paper, we present a novel gradient-based Plug-and-Play algorithm, constructed on the Half-Quadratic Splitting proximal algorithm, combining external and internal priors.
Edit intent: coherence
Original:  We propose a new PnP scheme, based on the Half-Quadratic Splitting proximal algorithm, combining external and internal priors. Moreover, differently from other existing PnP methods, we propose a deep denoiser acting on the image gradient domain.
Revised:  We propose a new PnP scheme, based on the Half-Quadratic Splitting proximal algorithm, combining external and internal denoisers as priors, defined on the image gradient domain.
Edit intent: clarity
Original:  In the experimental part, we use CNN denoisers and the Total Variation functional specifying external and internal priors, respectively. We prove the effectiveness of the proposed method in restoring blurred noisy  images, both in simulated and real medical settings  .
Revised:  In the experimental part, we use CNN denoisers and the Total Variation functional . The external denoiser is implemented by a deep Convolutional Neural Network (CNN) trained on the gradient domain (and not on the image one, as in state-of-the-art works). We also prove a general fixed-point convergence theorem under weak assumptions on both internal and external denoisers. The experiments confirm the effectiveness of the proposed method in restoring blurred noisy images, both in simulated and real medical settings .
Edit intent: meaning-changed
Original:  We believe that because the encoder already captures the whole speech utterance, which has the token-level relationship implicitly, we can predict a token without explicitly autoregressive language modeling. When the prediction of a token does not rely on other tokens, the parallel prediction of all tokens in the sequence is realizable. Based on this idea, we propose a non-autoregressive speech recognition model called LASO (Listen Attentively, and Spell Once).
Revised:  In contrast, we propose an end-to-end non-autoregressive speech recognition model called LASO (Listen Attentively, and Spell Once).
Edit intent: coherence
Original:  The encoder extracts high-level representations from the speech. The PDS uses positional encodings corresponding to tokensto convert the acoustic representations into token-level representations. The decoder further captures token-level relationships with the self-attention mechanism. At last, the probability distribution on the vocabulary is computed for each token position. Therefore, speech recognition is re-formulated as a position-wise classification problem. Further, we propose a cross-modal transfer learning method to refine semantics from a large-scale pre-trained language model BERT for improving the performance .
Revised:  The encoder extracts high-level representations from the whole speech signal rather than autoregressive modeling on tokens. Without explicitly autoregressive language modeling, this model predicts all tokens in the sequence in parallel so that the inference is efficient. Moreover, we propose a cross-modal transfer learning method to refine semantics from a large-scale pre-trained language model BERT for improving the performance .
Edit intent: clarity
Original:  Further, we propose a cross-modal transfer learning method to refine semantics from a large-scale pre-trained language model BERT for improving the performance .
Revised:  Further, we propose a cross-modal transfer learning method to use a text-modal language model to improve the performance of speech-modal LASO by aligning token semantics. We conduct experiments on two scales of public Chinese speech datasets AISHELL-1 and AISHELL-2. Experimental results show that our proposed model achieves a speedup of about 50\times and competitive performance, compared with the autoregressive transformer models. And the cross-modal knowledge transferring from the text-modal model can improve the performance of the speech-modal model .
Edit intent: meaning-changed
Original:  In contrast, we propose an end-to-end non-autoregressive speech recognition model called LASO (Listen Attentively, and Spell Once).
Revised:  We believe that because the encoder already captures the whole speech utterance, which has the token-level relationship implicitly, we can predict a token without explicitly autoregressive language modeling. When the prediction of a token does not rely on other tokens, the parallel prediction of all tokens in the sequence is realizable. Based on this idea, we propose a non-autoregressive speech recognition model called LASO (Listen Attentively, and Spell Once).
Edit intent: meaning-changed
Original:  The model aggregates encoded speech features into the hidden representations corresponding to each token with attention mechanisms. Thus, the model can capture the token relations by self-attention on the aggregated hidden representations from the whole speechsignal rather than autoregressive modeling on tokens .
Revised:  The model consists of an encoder, a decoder, and a position dependent summarizer (PDS). The three modules are based on basic attention blocks. The encoder extracts high-level representations from the whole speechsignal rather than autoregressive modeling on tokens .
Edit intent: meaning-changed
Original:  Thus, the model can capture the token relations by self-attention on the aggregated hidden representations from the whole speechsignal rather than autoregressive modeling on tokens . Without explicitly autoregressive language modeling, this model predicts all tokens in the sequence in parallel so that the inference is efficient. Moreover, we propose a cross-modal transfer learning method to use a text-modal language model to improve the performanceof speech-modal LASO by aligning token semantics.
Revised:  Thus, the model can capture the token relations by self-attention on the aggregated hidden representations from the speech. The PDS uses positional encodings corresponding to tokens to convert the acoustic representations into token-level representations. The decoder further captures token-level relationships with the self-attention mechanism. At last, the probability distribution on the vocabulary is computed for each token position. Therefore, speech recognition is re-formulated as a position-wise classification problem. Further, we propose a cross-modal transfer learning method to use a text-modal language model to improve the performanceof speech-modal LASO by aligning token semantics.
Edit intent: meaning-changed
Original:  Moreover, we propose a cross-modal transfer learning method to use a text-modal language model to improve the performanceof speech-modal LASO by aligning token semantics. We conduct experiments on two scales of public Chinese speech datasets AISHELL-1 and AISHELL-2. Experimental results show that our proposed model achieves a speedup of about 50\times and competitive performance, compared with the autoregressive transformer models. And the cross-modal knowledge transferring from the text-modal model can improve the performance of the speech-modal model .
Revised:  Moreover, we propose a cross-modal transfer learning method to refine semantics from a large-scale pre-trained language model BERT for improving the performance .
Edit intent: coherence
Original: This paper concerns Saez and Stantcheva's (2016) generalized social marginal welfare weights (GSMWW), which are used to aggregate losses and gains due to the tax system , while incorporating non-utilitarian ethical considerations.
Revised: This paper concerns Saez and Stantcheva's (2016) generalized social marginal welfare weights (GSMWW), which aggregate losses and gains due to the tax system , while incorporating non-utilitarian ethical considerations.
Edit intent: clarity
Original: This paper concerns Saez and Stantcheva's (2016) generalized social marginal welfare weights (GSMWW), which are used to aggregate losses and gains due to the tax system , while incorporating non-utilitarian ethical considerations.
Revised: This paper concerns Saez and Stantcheva's (2016) generalized social marginal welfare weights (GSMWW), which are used to aggregate losses and gains due to tax policies , while incorporating non-utilitarian ethical considerations.
Edit intent: clarity
Original: We study the dynamic membership problem for regular languages: fix a language L, read a word w, build in time O(|w|) a data structure indicating if w is in L, and maintain this structure efficiently under substitution edits on w.
Revised: We study the dynamic membership problem for regular languages: fix a language L, read a word w, build in time O(|w|) a data structure indicating if w is in L, and maintain this structure efficiently under letter substitutions on w.
Edit intent: clarity
Original:  We show that the problem is in O(log log |w|) for languages in an algebraically-defined  class QSG, and that it is in O(1) for another  class QLZG.
Revised:  We show that the problem is in O(log log |w|) for languages in an algebraically-defined , decidable class QSG, and that it is in O(1) for another class QLZG.
Edit intent: meaning-changed
Original:  We show that the problem is in O(log log |w|) for languages in an algebraically-defined  class QSG, and that it is in O(1) for another  class QLZG.
Revised:  We show that the problem is in O(log log |w|) for languages in an algebraically-defined class QSG, and that it is in O(1) for another such class QLZG.
Edit intent: clarity
Original:  We show that languages not in QSG admit a reduction from the prefix problem for a cyclic group, so that they require \Omega(log n  / log log n ) operations in the worst case;
Revised:  We show that languages not in QSG admit a reduction from the prefix problem for a cyclic group, so that they require \Omega(log n (log |w| / log log n ) operations in the worst case;
Edit intent: meaning-changed
Original:  We show that languages not in QSG admit a reduction from the prefix problem for a cyclic group, so that they require \Omega(log n  / log log n ) operations in the worst case;
Revised:  We show that languages not in QSG admit a reduction from the prefix problem for a cyclic group, so that they require \Omega(log n / log log |w| ) operations in the worst case;
Edit intent: meaning-changed
Original:  and that QSG languages not in QLZG admit a reduction from the prefix problem for the monoid U_1, } which we conjecture cannot be maintained in O(1).
Revised:  and that QSG languages not in QLZG admit a reduction from the prefix problem for the monoid U_1, 0, 1} which we conjecture cannot be maintained in O(1).
Edit intent: meaning-changed