wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This paper proposes two neural network architectures for modeling unsupervised lexical learning from raw acoustic inputs, ciwGAN (Categorical InfoWaveGAN) and fiwGAN (Featural InfoWaveGAN), that combine a DCGAN architecture for audio data (WaveGAN;
Revised:  This paper proposes two neural network architectures for modeling unsupervised lexical learning from raw acoustic inputs, ciwGAN (Categorical InfoWaveGAN) and fiwGAN (Featural InfoWaveGAN), that combine a Deep Convolutional GAN architecture for audio data (WaveGAN;
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Original:  arXiv:1705.07904) with  InfoGAN (arXiv:1606.03657), and propose a new latent space structure that can model featural learning simultaneously with a higher level classification .
Revised:  arXiv:1705.07904) with an information theoretic extension of GAN -- InfoGAN (arXiv:1606.03657), and propose a new latent space structure that can model featural learning simultaneously with a higher level classification .
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Original:  arXiv:1705.07904) with  InfoGAN (arXiv:1606.03657), and propose a new latent space structure that can model featural learning simultaneously with a higher level classification . The architectures introduce a network that learns to retrieve latent codes from generated audio outputs . Lexical learning is thus modeled as emergent from an architecture that forces a deep neural network to output data such that unique information is retrievable from its acoustic outputs.
Revised:  arXiv:1705.07904) with InfoGAN (arXiv:1606.03657), and propose a new latent space structure that can model featural learning simultaneously with a higher level classification and allows for a very low-dimension vector representation of lexical items . Lexical learning is thus modeled as emergent from an architecture that forces a deep neural network to output data such that unique information is retrievable from its acoustic outputs.
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Original:  The architectures introduce a network that learns to retrieve latent codes from generated audio outputs . Lexical learning is thus modeled as emergent from an architecture that forces a deep neural network to output data such that unique information is retrievable from its acoustic outputs.
Revised:  The architectures introduce a network that learns to retrieve latent codes from generated audio outputs . Lexical learning is modeled as emergent from an architecture that forces a deep neural network to output data such that unique information is retrievable from its acoustic outputs.
Edit intent: coherence
Original:   Innovative outputs suggest that phonetic and phonological representations learned by the network can be productively recombined and directly paralleled to productivity in human speech: a fiwGAN network trained on ' suit' and ' dark' outputs innovative ' start', even though it never saw ' start' or even a [st] sequence in the training data.
Revised:  The network occasionally outputs innovative lexical items that violate training data, but are linguistically interpretable and highly informative for cognitive modeling and neural network interpretability. Innovative outputs suggest that phonetic and phonological representations learned by the network can be productively recombined and directly paralleled to productivity in human speech: a fiwGAN network trained on ' suit' and ' dark' outputs innovative ' start', even though it never saw ' start' or even a [st] sequence in the training data.
Edit intent: meaning-changed
Original:   Innovative outputs suggest that phonetic and phonological representations learned by the network can be productively recombined and directly paralleled to productivity in human speech: a fiwGAN network trained on ' suit' and ' dark' outputs innovative ' start', even though it never saw ' start' or even a [st] sequence in the training data.
Revised:  Innovative outputs suggest that phonetic and phonological representations learned by the network can be productively recombined and directly paralleled to productivity in human speech: a fiwGAN network trained on ` suit' and ' dark' outputs innovative ' start', even though it never saw ' start' or even a [st] sequence in the training data.
Edit intent: fluency
Original:   Innovative outputs suggest that phonetic and phonological representations learned by the network can be productively recombined and directly paralleled to productivity in human speech: a fiwGAN network trained on ' suit' and ' dark' outputs innovative ' start', even though it never saw ' start' or even a [st] sequence in the training data.
Revised:  Innovative outputs suggest that phonetic and phonological representations learned by the network can be productively recombined and directly paralleled to productivity in human speech: a fiwGAN network trained on ' suit' and ` dark' outputs innovative ' start', even though it never saw ' start' or even a [st] sequence in the training data.
Edit intent: fluency
Original:   Innovative outputs suggest that phonetic and phonological representations learned by the network can be productively recombined and directly paralleled to productivity in human speech: a fiwGAN network trained on ' suit' and ' dark' outputs innovative ' start', even though it never saw ' start' or even a [st] sequence in the training data.
Revised:  Innovative outputs suggest that phonetic and phonological representations learned by the network can be productively recombined and directly paralleled to productivity in human speech: a fiwGAN network trained on ' suit' and ' dark' outputs innovative ` start', even though it never saw ' start' or even a [st] sequence in the training data.
Edit intent: fluency
Original:   Innovative outputs suggest that phonetic and phonological representations learned by the network can be productively recombined and directly paralleled to productivity in human speech: a fiwGAN network trained on ' suit' and ' dark' outputs innovative ' start', even though it never saw ' start' or even a [st] sequence in the training data.
Revised:  Innovative outputs suggest that phonetic and phonological representations learned by the network can be productively recombined and directly paralleled to productivity in human speech: a fiwGAN network trained on ' suit' and ' dark' outputs innovative ' start', even though it never saw ` start' or even a [st] sequence in the training data.
Edit intent: fluency
Original:  We also argue that setting latent featural codes to values well beyond training range results in almost categorical generation of prototypical lexical items and reveals underlying values of each latent code . Probing deep neural networks trained on well understood dependencies in speech bears implications for latent space interpretability, understanding how deep neural networks learn meaningful representations, as well as a potential for unsupervised text-to-speech generation in the GAN framework .
Revised:  We also argue that setting latent featural codes to values well beyond training range results in almost categorical generation of prototypical lexical items and reveals underlying values of each latent code .
Edit intent: coherence
Original:  We propose that algorithmic flagging systems deployed to improve  efficiency of moderation work can also make moderation actions more fair to these users by reducing reliance on social signals and making norm violations by everyone else more visible.
Revised:  We propose that algorithmic flagging systems deployed to improve the efficiency of moderation work can also make moderation actions more fair to these users by reducing reliance on social signals and making norm violations by everyone else more visible.
Edit intent: fluency
Original: Learned Bloom filters enhance standard Bloom filters by using a learned model for the represented data set.
Revised: Bloom filters are space-efficient probabilistic data structures that are used to test whether an element is a member of a set, and may return false positives. Recently, variations referred to as learned Bloom filters were developed that can provide improved performance in terms of the rate of false positives, by using a learned model for the represented data set.
Edit intent: coherence
Original:  However, a learned Bloom filter may under-utilize the model by not taking full advantage of the output. The learned Bloom filter uses the output score by simply applying a threshold, with elements above the threshold being interpreted as positives, and elements below the threshold subject to further analysis independent of the output score (using a smaller backup Bloom filter to prevent false negatives). While recent work has suggested additional heuristic approaches to take better advantage of the score, the results are only heuristic. Here , we instead frame the problem of optimal model utilization as an optimization problem .
Revised:  However, a learned Bloom filter may under-utilize the model by not taking full advantage of the learned model. Here we show how to frame the problem of optimal model utilization as an optimization problem .
Edit intent: coherence
Original:  Here , we instead frame the problem of optimal model utilization as an optimization problem . We show that the optimization problem can be effectively solved efficiently, yielding an improved partitioned learned Bloom filter%DIFDELCMD < }%%% , which partitions the score space and utilizes separate backup Bloom filters for each region . Experimental results from both simulated and real-world datasets show significant performance improvements from our optimization approach over both the original learned Bloom filter constructions and previously proposed heuristic improvements.
Revised:  Here , we instead frame the problem of optimal model utilization as an optimization problem partitioned learned Bloom filter%DIFDELCMD < }%%% , which partitions the score space and utilizes separate backup Bloom filters for each region . Experimental results from both simulated and real-world datasets show significant performance improvements from our optimization approach over both the original learned Bloom filter constructions and previously proposed heuristic improvements.
Edit intent: coherence
Original:  We show that the optimization problem can be effectively solved efficiently, yielding an improved partitioned learned Bloom filter%DIFDELCMD < }%%% , which partitions the score space and utilizes separate backup Bloom filters for each region . Experimental results from both simulated and real-world datasets show significant performance improvements from our optimization approach over both the original learned Bloom filter constructions and previously proposed heuristic improvements.
Revised:  We show that the optimization problem can be effectively solved efficiently, yielding an improved %DIFDELCMD < }%%% , which partitions the score space and utilizes separate backup Bloom filters for each region . Experimental results from both simulated and real-world datasets show significant performance improvements from our optimization approach over both the original learned Bloom filter constructions and previously proposed heuristic improvements.
Edit intent: clarity
Original:  We show that the optimization problem can be effectively solved efficiently, yielding an improved partitioned learned Bloom filter%DIFDELCMD < }%%% , which partitions the score space and utilizes separate backup Bloom filters for each region . Experimental results from both simulated and real-world datasets show significant performance improvements from our optimization approach over both the original learned Bloom filter constructions and previously proposed heuristic improvements.
Revised:  We show that the optimization problem can be effectively solved efficiently, yielding an improved partitioned learned Bloom filter%DIFDELCMD < }%%% , and using our framework derive algorithms that can achieve near-optimal performance in many cases . Experimental results from both simulated and real-world datasets show significant performance improvements from our optimization approach over both the original learned Bloom filter constructions and previously proposed heuristic improvements.
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Original:  Under the noise model, we estimate conditional expected marginal profit of a node by reverse influence sampling technique and propose an efficient algorithm , which could return a (1-exp({\epsilon} ' -1)) -approximation solution with high probability .
Revised:  Under the noise model, we estimate conditional expected marginal profit of a node by modifying the EPIC algorithm and propose an efficient algorithm , which could return a (1-exp({\epsilon} ' -1)) -approximation solution with high probability .
Edit intent: meaning-changed
Original:  Under the noise model, we estimate conditional expected marginal profit of a node by reverse influence sampling technique and propose an efficient algorithm , which could return a (1-exp({\epsilon} ' -1)) -approximation solution with high probability .
Revised:  Under the noise model, we estimate conditional expected marginal profit of a node by reverse influence sampling technique and propose an efficient policy , which could return a (1-exp({\epsilon} ' -1)) -approximation solution with high probability .
Edit intent: clarity
Original:  Under the noise model, we estimate conditional expected marginal profit of a node by reverse influence sampling technique and propose an efficient algorithm , which could return a (1-exp({\epsilon} ' -1)) -approximation solution with high probability .
Revised:  Under the noise model, we estimate conditional expected marginal profit of a node by reverse influence sampling technique and propose an efficient algorithm , which could return a (1-exp({\epsilon} -1)) -approximation solution with high probability .
Edit intent: fluency
Original:  Under the noise model, we estimate conditional expected marginal profit of a node by reverse influence sampling technique and propose an efficient algorithm , which could return a (1-exp({\epsilon} ' -1)) -approximation solution with high probability .
Revised:  Under the noise model, we estimate conditional expected marginal profit of a node by reverse influence sampling technique and propose an efficient algorithm , which could return a (1-exp({\epsilon} ' -1)) expected approximation ratio. Several experiments are conducted on six realistic datasets to compare our proposed policies with their corresponding non-adaptive algorithms and some heuristic adaptive policies .
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Original:  Integrating them into one network may provide better learning abilities for general tasks. On the other hand, spiking neural network embodies "computation" in spatiotemporal domain with unique features of rich coding scheme and threshold switching, which is very suitable for low power and high parallel neuromorphic computing .
Revised:  Integrating them into one network may provide complementary learning capabilities for versatile learning scenarios. At the same time, neuromorphic computing holds great promise, but still needs plenty of useful algorithms and algorithm-hardware co-designs for exploiting the advantages .
Edit intent: clarity
Original:  Here, we report a spike-based general learning model that integrates two learning routes by introducing a brain-inspired meta-local module and a two-phase parametric modelling. The hybrid model can meta-learn general local plasticity , and receive top-down supervision information for multi-scale learning.
Revised:  Here, we report a spike-based general learning model that integrates two learning routes by introducing a brain-inspired meta-learning paradigm and a differentiable spiking model incorporating neuronal dynamics and synaptic plasticity. It can meta-learn general local plasticity , and receive top-down supervision information for multi-scale learning.
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Original:  We demonstrate that this hybrid model facilitates learning of many general tasks, including fault-tolerance learning, few-shot learningand multiple-task learning . Furthermore, the implementation of the hybrid model on the Tianjic neuromorphic platform proves that it can fully utilize the advantages of neuromorphic hardware architecture and promote energy-efficient on-chip applications .
Revised:  We demonstrate that this hybrid model facilitates learning of many general tasks, including few-shot learning, continual learning, and fault-tolerance learning in neuromorphic vision sensors. It achieves significantly higher performance than single-learning methods, and shows promise in empowering neuromorphic applications revolution. We further implemented the hybrid model in the implementation of the hybrid model on the Tianjic neuromorphic platform proves that it can fully utilize the advantages of neuromorphic hardware architecture and promote energy-efficient on-chip applications .
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Original:  Furthermore, the implementation of the hybrid model on the Tianjic neuromorphic platform proves that it can fully utilize the advantages of neuromorphic hardware architecture and promote energy-efficient on-chip applications .
Revised:  Furthermore, the implementation of the hybrid model on the Tianjic neuromorphic platform by exploiting algorithm-hardware co-designs and proved that the model can fully utilize the advantages of neuromorphic hardware architecture and promote energy-efficient on-chip applications .
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Original: The explosion of disinformation related to the COVID-19 pandemic has overloaded fact-checkers and media worldwide .
Revised: The explosion of disinformation related to the COVID-19 pandemic has overloaded fact-checkers and media worldwide , and brought a new major challenge to government responses worldwide. Not only is disinformation creating confusion about medical science amongst citizens, but it is also amplifying distrust in policy makers and governments.
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Original:  To help tackle this, we developed computational methods to support COVID-19 disinformation debunking and social impacts research .
Revised:  To help tackle this, we developed computational methods to support COVID-19 disinformation . The COVID-19 disinformation categories could be used for a) focusing fact-checking efforts on the most damaging kinds of COVID-19 disinformation; b) guiding policy makers who are trying to deliver effective public health messages and counter effectively COVID-19 disinformation .
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Original:  This paper presents: 1) the currently largest available manually annotated COVID-19 disinformation category dataset; and 2) a classification-aware neural topic model (CANTM) that combines classification and topic modelling under a variational autoencoder framework.
Revised:  This paper presents: 1) the currently largest available manually annotated COVID-19 disinformation categories; 2) a classification-aware neural topic model (CANTM) that combines classification and topic modelling under a variational autoencoder framework.
Edit intent: clarity
Original:  and 2) a classification-aware neural topic model (CANTM) that combines classification and topic modelling under a variational autoencoder framework. We demonstrate that CANTM efficiently improves classification performance with low resources, and is scalable. In addition, the classification-aware topics help researchers and end-users to better understand the classification results .
Revised:  and 2) a classification-aware neural topic model (CANTM) that combines classification and topic discovery; 3) an extensive analysis of COVID-19 disinformation categories with respect to time, volume, false type, media type and origin source .
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Original:  Markovian tasks can be characterised by a state space and a transition matrix . In mammals, the firing of populations of place or grid cells in the hippocampal formation are thought to represent the probability distribution over state space. Grid firing patterns are suggested to be eigenvectors of a transition matrix reflecting diffusion across states, allowing simple prediction of future state distributions , by replacing matrix multiplication with elementwise multiplication by eigenvalues .
Revised:  Markovian tasks can be characterised by a state space and a transition matrix and recent work has proposed that neural grid codes provide an efficient representation of the state space, as eigenvectors of a transition matrix reflecting diffusion across states, allowing simple prediction of future state distributions , by replacing matrix multiplication with elementwise multiplication by eigenvalues .
Edit intent: clarity
Original:  Here we extend this analysisto any translation-invariant directed transition structure ( displacement and diffusion), showing that a single set of eigenvectors supports prediction via displacement-specific eigenvalues.
Revised:  Here we extend the eigenbasis prediction model, utilising tools from Fourier analysis, to prediction over arbitrary translation-invariant directed transition structure ( displacement and diffusion), showing that a single set of eigenvectors supports prediction via displacement-specific eigenvalues.
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Original:  Here we extend this analysisto any translation-invariant directed transition structure ( displacement and diffusion), showing that a single set of eigenvectors supports prediction via displacement-specific eigenvalues. This unifies the prediction framework with traditional models of grid cells firing driven by self-motion to perform path integration .
Revised:  Here we extend this analysisto any translation-invariant directed transition structure ( displacement and diffusion), showing that a single set of eigenvectors can support predictions over arbitrary directed actions via action-specific eigenvalues. We show how to define a "sense of direction" to combine actions to reach a target state (ignoring task-specific deviations from translation-invariance), and demonstrate that adding the Fourier representations to a deep Q network aids policy learning in continuous control tasks. We show the equivalence between the generalised prediction framework and traditional models of grid cells firing driven by self-motion to perform path integration .
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Original:  This unifies the prediction framework with traditional models of grid cells firing driven by self-motion to perform path integration . We show that the complex eigenstructure of directed transitions corresponds to the Discrete Fourier Transform, the eigenvalues encode displacement via the Fourier Shift Theorem, and the Fourier components are analogous to " velocity-controlled oscillators " in oscillatory interference models.
Revised:  This unifies the prediction framework with traditional models of grid cells firing driven by self-motion to perform path integration , either using oscillatory interference (via Fourier components as velocity-controlled oscillators " in oscillatory interference models.
Edit intent: coherence