wanyu/IteraTeR_full_sent huggingface dataset:
Original: We present Shapeshifter Networks (SSNs), a flexible neural network framework that improves performance and reduces memory requirements on a diverse set of scenarios over standard neural networks.
Revised: Fitting a model into GPU memory during training is an increasing concern as models continue to grow. To address this issue, we present Shapeshifter Networks (SSNs), a flexible neural network framework that improves performance and reduces memory requirements on a diverse set of scenarios over standard neural networks.
Edit intent: meaning-changed
Original: We present Shapeshifter Networks (SSNs), a flexible neural network framework that improves performance and reduces memory requirements on a diverse set of scenarios over standard neural networks. Our approach is based on the observation that many neural networks are severely overparameterized, resulting in significant waste in computational resources as well as being susceptible to overfitting. SSNs address this by learning where and how to share parameters between layers in a neural network while avoiding degenerate solutions that result in underfitting.
Revised: We present Shapeshifter Networks (SSNs), a flexible neural network framework that decouples layers from model weights, enabling us to implement any neural network with an arbitrary number of parameters. In SSNs each layer obtains weights from a parameter store that decides where and how to share parameters between layers in a neural network while avoiding degenerate solutions that result in underfitting.
Edit intent: meaning-changed
Original:  SSNs address this by learning where and how to share parameters between layers in a neural network while avoiding degenerate solutions that result in underfitting. Specifically, we automatically construct parameter groups that identify where parameter sharing is most beneficial. Then, we map each group's weights to construct layerswith learned combinations of candidates from a shared parameter pool. SSNs can share parameters across layers even when they have different sizes , perform different operations , and/or operate on features from different modalities .
Revised:  SSNs address this by learning where and how to allocate parameters to layers. This can result in sharing parameters across layers even when they have different sizes , perform different operations , and/or operate on features from different modalities .
Edit intent: clarity
Original:  SSNs can share parameters across layers even when they have different sizes , perform different operations , and/or operate on features from different modalities .
Revised:  SSNs can share parameters across layers even when they have different sizes , perform different operations . SSNs do not require any modifications to a model's loss function or architecture, making them easy to use. Our approach can create parameter efficient networks by using a relatively small number of weights, or can improve a model's performance by adding additional model capacity during training without affecting the computational resources required at test time .
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Original:  We evaluate our approach on a diverse set of tasks , including image classification, bidirectional image-sentence retrieval, and phrase grounding, creating high performing models even when using as little as 1\% of the parameters .
Revised:  We evaluate SSNs using seven network architectures across diverse tasks that include image classification, bidirectional image-sentence retrieval, and phrase grounding, creating high performing models even when using as little as 1\% of the parameters .
Edit intent: meaning-changed
Original:  We evaluate our approach on a diverse set of tasks , including image classification, bidirectional image-sentence retrieval, and phrase grounding, creating high performing models even when using as little as 1\% of the parameters . We also apply SSNs to knowledge distillation, where we obtain state-of-the-art results when combined with traditional distillation methods .
Revised:  We evaluate our approach on a diverse set of tasks , including image classification, bidirectional image-sentence retrieval, and phrase grounding, creating high performing models even when using as little as 1\% of the parameters .
Edit intent: meaning-changed
Original:  To address this issue, we present Shapeshifter Networks (SSNs), a flexible neural network framework that decouples layers from model weights, enabling us to implement any neural network with an arbitrary number of parameters . In SSNseach layer obtains weights from a parameter store that decides where and how to allocate parameters to layers .
Revised:  Parameter sharing can reduce memory requirements, but existing methods only share parameters between identical layers, limiting their impact. This paper removes these restrictions with a novel task called Neural Parameter Allocation Search (NPAS), where the goal is to generate weights for a network using a given parameter budget. NPAS requires new techniques to morph available parameters to fit any architecture. To address this new task we introduce Shapeshifter Networks (SSNs), which automatically learns where and how to allocate parameters to layers .
Edit intent: meaning-changed
Original:  We evaluate SSNs  using seven network architectures across diverse tasks that include image classification, bidirectional image-sentence retrieval, and phrase grounding, creating high performing models even when using as little as 1\% of the parameters.
Revised:  We evaluate SSNs in key NPAS settings using seven network architectures across diverse tasks that include image classification, bidirectional image-sentence retrieval, and phrase grounding, creating high performing models even when using as little as 1\% of the parameters.
Edit intent: meaning-changed
Original:  We evaluate SSNs  using seven network architectures across diverse tasks that include image classification, bidirectional image-sentence retrieval, and phrase grounding, creating high performing models even when using as little as 1\% of the parameters.
Revised:  We evaluate SSNs using seven network architectures across diverse tasks including image classification, bidirectional image-sentence retrieval, and phrase grounding, creating high performing models even when using as little as 1\% of the parameters.
Edit intent: clarity
Original:  Whereas most prior work on graph neural networks uses linear functions to aggregate information from neighboring nodes, we find that non-linear aggregators such as LSTMs or transformers lead to significant improvements on zero-shot tasks. On two natural language tasks across three datasets, ZSL-KG shows an average improvement of 9.2 points of accuracy versus state-of-the-art methods.
Revised:  Whereas most prior work on graph neural networks uses linear functions to aggregate information from neighboring nodes, we find that non-linear aggregators such as LSTMs or transformers lead to significant improvements on zero-shot learning tasks in language and vision. Our results show ZSL-KG shows an average improvement of 9.2 points of accuracy versus state-of-the-art methods.
Edit intent: clarity
Original:  On two natural language tasks across three datasets, ZSL-KG shows an average improvement of 9.2 points of accuracy versus state-of-the-art methods. In addition, on an object classification task, ZSL-KG shows a 2.2 accuracy point improvement versus the best methods that do not require hand-engineered class representations. Finally, we find that ZSL-KG outperforms the best performing  graph neural networks with linear aggregators by an average of 3.8 points of accuracy across these four datasets .
Revised:  On two natural language tasks across three datasets, ZSL-KG outperforms the best performing graph neural networks with linear aggregators by an average of 3.8 points of accuracy across these four datasets .
Edit intent: coherence
Original:  Finally, we find that ZSL-KG outperforms the best performing  graph neural networks with linear aggregators by an average of 3.8 points of accuracy across these four datasets .
Revised:  Finally, we find that ZSL-KG outperforms the best performing graph-based zero-shot learning framework by an average of 2.1 accuracy points with improvements as high as 3.4 accuracy points. Our ablation study on ZSL-KG with alternate graph neural networks with linear aggregators by an average of 3.8 points of accuracy across these four datasets .
Edit intent: meaning-changed
Original: Sensory learning in the mammalian cortex has long been hypothesized to involve the objective of variational inference (VI). Likely the most well-known algorithm for cortical VI is the Wake-Sleep algorithm (Hinton et al. 1995). However Wake-Sleep problematically assumes that neural activities are independent given lower-layers during generation. Here, we construct a VI system that is both compatible with neurobiology and avoids this assumption. The core of the system is a wake-sleep discriminator that classifies network states as inferred or self-generated. Inference connections learn by opposing this discriminator. This adversarial dynamic solves a core problem within VI, which is to match the distribution of stimulus-evoked (inference) activity to that of self-generated activity. Meanwhile, generative connections learn to predict lower-level activity as in standard VI. We implement this algorithm and show that it can successfully train the approximate inference network for generative models. Our proposed algorithm makes several biological predictions that can be tested.
Revised: A popular theory of perceptual processing holds that the brain learns both a generative model of the world and a paired recognition model using variational Bayesian inference. Most hypotheses of how the brain might learn these models assume that neurons in a population are conditionally independent given their common inputs. This simplification is likely not compatible with the type of local recurrence observed in the brain. Seeking an alternative that is compatible with complex inter-dependencies yet consistent with known biology, we argue here that the cortex may learn with an adversarial algorithm. Many observable symptoms of this approach would resemble known neural phenomena, including wake/sleep cycles and oscillations that vary in magnitude with surprise, and we describe how further predictions could be tested.
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Original:  We demonstrate, through standard numerical analysis and exhaustive numerical experimentation on several complex Poisson and linear elasticity benchmarks, that the new technique enjoys the following properties: well-posedness, robustness  to cut location and material contrast, optimal (h-adaptive) approximation properties, high scalability and easy implementation in large-scale finite element codes.
Revised:  We demonstrate, through standard numerical analysis and exhaustive numerical experimentation on several complex Poisson and linear elasticity benchmarks, that the new technique enjoys the following properties: well-posedness, robustness with respect to cut location and material contrast, optimal (h-adaptive) approximation properties, high scalability and easy implementation in large-scale finite element codes.
Edit intent: clarity
Original:  In our empirical study, we find that European countries overall flatten the curves more effectively compared to the other regions, while Asia & Oceania also achieve some success, but the situations are not as optimistic as in Europe . Africa and America are still facing serious challenges in terms of managing the spread of the virus  and reducing the death rate .
Revised:  In our empirical study, we find that European countries overall flatten the curves more effectively compared to the other regions, while Asia & Oceania also achieve some success, but the situations are not as optimistic elsewhere . Africa and America are still facing serious challenges in terms of managing the spread of the virus and reducing the death rate .
Edit intent: clarity
Original:  In our empirical study, we find that European countries overall flatten the curves more effectively compared to the other regions, while Asia & Oceania also achieve some success, but the situations are not as optimistic as in Europe . Africa and America are still facing serious challenges in terms of managing the spread of the virus  and reducing the death rate .
Revised:  In our empirical study, we find that European countries overall flatten the curves more effectively compared to the other regions, while Asia & Oceania also achieve some success, but the situations are not as optimistic as in Europe . Africa and America are still facing serious challenges in terms of managing the spread of the virus , and reducing the death rate .
Edit intent: fluency
Original:  In our empirical study, we find that European countries overall flatten the curves more effectively compared to the other regions, while Asia & Oceania also achieve some success, but the situations are not as optimistic as in Europe . Africa and America are still facing serious challenges in terms of managing the spread of the virus  and reducing the death rate . In Africa, the rate of the spread of the virus is slower and the death rate is also lower than those of the other regions.
Revised:  In our empirical study, we find that European countries overall flatten the curves more effectively compared to the other regions, while Asia & Oceania also achieve some success, but the situations are not as optimistic as in Europe . Africa and America are still facing serious challenges in terms of managing the spread of the virus and reducing the death rate , although in Africa the virus spreads slower and has a lower death rate than the rate of the spread of the virus is slower and the death rate is also lower than those of the other regions.
Edit intent: meaning-changed
Original:  Random Acceleration MD (RAMD) is a simple method to enhance ligand egress from a macromolecular binding site that does not require the user to choose a ligand egress reaction coordinate. It thus enables the unbiased exploration of ligand egress routes  .
Revised:  Random Acceleration MD (RAMD) is a simple method to enhance ligand egress from a macromolecular binding site , which enables the exploration of ligand egress routes .
Edit intent: coherence
Original:  We studied whether a recurrent neural network with Long Short-Term Memory units can mimic a central aspect of human sentence processing, namely the handling of long-distance agreement dependencies.
Revised:  We studied whether a recurrent neural network with Long Short-Term Memory units can mimic a central aspect of human sentence processing, namely the handling of long-distance agreement (e.g., capturing the correct number agreement between subject and verb when they are separated by other phrases).
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Original:  Although the network  was solely trained to predict the next word in a large corpus, analysis showed the emergence of a small set of specialized units that successfully handled local and long-distance syntactic agreement for grammatical number.
Revised:  Although the network , a recurrent architecture with Long Short-Term Memory units, was solely trained to predict the next word in a large corpus, analysis showed the emergence of a small set of specialized units that successfully handled local and long-distance syntactic agreement for grammatical number.
Edit intent: meaning-changed
Original:  Although the network  was solely trained to predict the next word in a large corpus, analysis showed the emergence of a small set of specialized units that successfully handled local and long-distance syntactic agreement for grammatical number.
Revised:  Although the network was solely trained to predict the next word in a large corpus, analysis showed the emergence of a very sparse set of specialized units that successfully handled local and long-distance syntactic agreement for grammatical number.
Edit intent: meaning-changed
Original: Various data suggest that the brain carries out probabilistic inference. Models that perform inference through sampling are particularly appealing since instead of requiring networks to perform sophisticated mathematical operations, they can simply exploit stochasticity in neuron behavior. However, sampling from complex distributions is a hard problem. In particular, mixing behavior is often very sensitive to the temperature parameter which controls the stochasticity of the sampler .
Revised: Being permanently confronted with an uncertain world, brains have faced evolutionary pressure to represent this uncertainty in order to respond appropriately. Often, this requires visiting multiple interpretations of the available information or multiple solutions to an encountered problem. This gives rise to the so-called mixing problem: since all of these "valid" states represent powerful attractors, but between themselves can be very dissimilar, switching between such states can be difficult .
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Original:  We propose that background oscillations , an ubiquitous phenomenon throughout the brain, can mitigate this issue and thus implement the backbone for sampling-based computations in spiking neural networks.
Revised:  We propose that cortical oscillations can be effectively used to overcome this challenge. By acting as an effective temperature, background spiking activity modulates exploration. Rhythmic changes induced by cortical oscillations can then be interpreted as a form of simulated tempering. We provide a rigorous mathematical discussion of this link and study some of its phenomenological implications in computer simulations. This identifies a new computational role of cortical oscillations and connects them to various phenomena in the brain, such as sampling-based computations in spiking neural networks.
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Original:  We propose that background oscillations , an ubiquitous phenomenon throughout the brain, can mitigate this issue and thus implement the backbone for sampling-based computations in spiking neural networks. We first show that both in current-based and conductance-based neuron models, the level of background activity effectively defines the sampling temperature of the network. This mechanism allows brain networks to flexibly control the sampling behavior, either favoring convergence to local optima or promoting mixing. We then demonstrate that background oscillations can in this way structure stochastic computations into discrete sampling episodes. In each such episode, solutions are first explored at high temperatures before annealing to low temperatures favors convergence to a good solution .
Revised:  We propose that background oscillations , an ubiquitous phenomenon throughout the brain, can mitigate this issue and thus implement the backbone for sampling-based probabilistic inference, memory replay, multisensory cue combination and place cell flickering .
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Original: Although over 95 million people in the world speak the Vietnamese language, there are not any large and qualified datasets for automatic reading comprehension . In addition , machine reading comprehension  for the health domain offers great potential for practical applications;
Revised: Large-scale and high-quality corpora are necessary for evaluating machine reading comprehension models on a low-resource language like Vietnamese. Besides , machine reading comprehension for the health domain offers great potential for practical applications;
Edit intent: clarity
Original:  Crowdworkers created the questions and  answers based on a set of over 2,030 online Vietnamese news articles from the VnExpress news website , where the answers comprised spans extracted from the corresponding articles.
Revised:  Crowd-workers create the questions and answers based on a set of over 2,030 online Vietnamese news articles from the VnExpress news website , where the answers comprised spans extracted from the corresponding articles.
Edit intent: fluency
Original:  Crowdworkers created the questions and  answers based on a set of over 2,030 online Vietnamese news articles from the VnExpress news website , where the answers comprised spans extracted from the corresponding articles.
Revised:  Crowdworkers created the questions and their answers based on a set of over 2,030 online Vietnamese news articles from the VnExpress news website , where the answers comprised spans extracted from the corresponding articles.
Edit intent: fluency
Original:  Crowdworkers created the questions and  answers based on a set of over 2,030 online Vietnamese news articles from the VnExpress news website , where the answers comprised spans extracted from the corresponding articles.
Revised:  Crowdworkers created the questions and answers based on a set of over 2,030 online Vietnamese news articles from the VnExpress news website , where the answers comprise spans extracted from the corresponding articles.
Edit intent: fluency
Original:  The significant difference between humans and the best model (F1-score of 15.93\% ) on the test set of our corpus indicates that improvements in ViNewsQA can be explored in future research .
Revised:  The significant differences between humans and the best model (F1-score of 15.93\% ) on the test set of our corpus indicates that improvements in ViNewsQA can be explored in future research .
Edit intent: fluency
Original:  The significant difference between humans and the best model (F1-score of 15.93\% ) on the test set of our corpus indicates that improvements in ViNewsQA can be explored in future research .
Revised:  The significant difference between humans and the best-performance model (14.53\% of EM and 10.90\% of F1-score ) on the test set of our corpus indicates that improvements in ViNewsQA can be explored in future research .
Edit intent: meaning-changed
Original:  The significant difference between humans and the best model (F1-score of 15.93\% ) on the test set of our corpus indicates that improvements in ViNewsQA can be explored in future research .
Revised:  The significant difference between humans and the best model (F1-score of 15.93\% ) on the test set of our corpus indicate that improvements in ViNewsQA can be explored in future research .
Edit intent: fluency
Original:  The significant difference between humans and the best model (F1-score of 15.93\% ) on the test set of our corpus indicates that improvements in ViNewsQA can be explored in future research .
Revised:  The significant difference between humans and the best model (F1-score of 15.93\% ) on the test set of our corpus indicates that improvements in ViNewsQA could be explored in future research .
Edit intent: fluency
Original:  The significant difference between humans and the best model (F1-score of 15.93\% ) on the test set of our corpus indicates that improvements in ViNewsQA can be explored in future research .
Revised:  The significant difference between humans and the best model (F1-score of 15.93\% ) on the test set of our corpus indicates that improvements in ViNewsQA can be explored in the future study .
Edit intent: clarity
Original:  Then, the proposed score is applied to assess and compare density forecasts of macroeconomic relevant datasets ( unemployment rate ) and of commodity prices (oil and electricity prices) with a particular focus on the recent COVID crisis period.
Revised:  Then, the proposed score and test are applied to assess and compare density forecasts of macroeconomic relevant datasets ( unemployment rate ) and of commodity prices (oil and electricity prices) with a particular focus on the recent COVID crisis period.
Edit intent: clarity
Original:  Then, the proposed score is applied to assess and compare density forecasts of macroeconomic relevant datasets ( unemployment rate ) and of commodity prices (oil and electricity prices) with a particular focus on the recent COVID crisis period.
Revised:  Then, the proposed score is applied to assess and compare density forecasts of macroeconomic relevant datasets ( US employment growth ) and of commodity prices (oil and electricity prices) with a particular focus on the recent COVID crisis period.
Edit intent: meaning-changed