wanyu/IteraTeR_v2 huggingface dataset:
Original:  The architectural genetics of ancestor deep neural networks is encapsulated using the concept of synaptic probability density models, which can be viewed as the ' DNA' of these ancestor networks.
Revised:  The architectural genetics of ancestor deep neural networks are encoded using synaptic probability models, which can be viewed as the ' DNA' of these ancestor networks.
Edit intent: clarity
Original:  The architectural genetics of ancestor deep neural networks is encapsulated using the concept of synaptic probability density models, which can be viewed as the ' DNA' of these ancestor networks.
Revised:  The architectural genetics of ancestor deep neural networks is encapsulated using the concept of synaptic probability density models, which can be viewed as the ` DNA' of these ancestor networks.
Edit intent: fluency
Original:  These synaptic probability density models from the ancestor networks are then leveraged, along with computational environmental factor models to synthesize new descendant deep neural networks with different network architectures in a random manner to mimic natural selection and random mutations.
Revised:  These synaptic probability density models from the ancestor networks and computational environmental factor models to synthesize new descendant deep neural networks with different network architectures in a random manner to mimic natural selection and random mutations.
Edit intent: coherence
Original:  These synaptic probability density models from the ancestor networks are then leveraged, along with computational environmental factor models to synthesize new descendant deep neural networks with different network architectures in a random manner to mimic natural selection and random mutations.
Revised:  These synaptic probability density models from the ancestor networks are then leveraged, along with computational environmental factor models , in a random manner to mimic natural selection and random mutations.
Edit intent: coherence
Original:  These 'evolved' deep neural networks (which we will term EvoNets) are then trained into fully functional networks, like one would train a newborn, and have more efficient, more varied synapse architectures than their ancestor networks, while achieving powerful modeling capabilities.
Revised:  These 'evolved' deep neural networks (which we will term EvoNets) are then trained into fully functional networks, like one would train a newborn, and have more efficient, more diverse network architectures than their ancestor networks, while achieving powerful modeling capabilities.
Edit intent: clarity
Original:  Experimental results using the MSRA-B dataset for the purpose of image segmentation was performed, and it was demonstrated that the synthesized EvoNets can achieve state-of-the-art F_beta score (0.872 at second generation, 0.859 at third generation, and 0.839 at fourth generation) while having synapse architectures that are significantly more efficient ( ~19X fewer\sim48 synapses by the fourth generation) compared to the original ancestor deep neural network.
Revised:  Experimental results for the task of visual saliency demonstrated that the synthesized EvoNets can achieve state-of-the-art F_beta score (0.872 at second generation, 0.859 at third generation, and 0.839 at fourth generation) while having synapse architectures that are significantly more efficient ( ~19X fewer\sim48 synapses by the fourth generation) compared to the original ancestor deep neural network.
Edit intent: clarity
Original:  Experimental results using the MSRA-B dataset for the purpose of image segmentation was performed, and it was demonstrated that the synthesized EvoNets can achieve state-of-the-art F_beta score (0.872 at second generation, 0.859 at third generation, and 0.839 at fourth generation) while having synapse architectures that are significantly more efficient ( ~19X fewer\sim48 synapses by the fourth generation) compared to the original ancestor deep neural network.
Revised:  Experimental results using the MSRA-B dataset for the purpose of image segmentation was performed, and it was demonstrated that the synthesized `evolved' offspring networks can achieve state-of-the-art F_beta score (0.872 at second generation, 0.859 at third generation, and 0.839 at fourth generation) while having synapse architectures that are significantly more efficient ( ~19X fewer\sim48 synapses by the fourth generation) compared to the original ancestor deep neural network.
Edit intent: clarity
Original:  Experimental results using the MSRA-B dataset for the purpose of image segmentation was performed, and it was demonstrated that the synthesized EvoNets can achieve state-of-the-art F_beta score (0.872 at second generation, 0.859 at third generation, and 0.839 at fourth generation) while having synapse architectures that are significantly more efficient ( ~19X fewer\sim48 synapses by the fourth generation) compared to the original ancestor deep neural network.
Revised:  Experimental results using the MSRA-B dataset for the purpose of image segmentation was performed, and it was demonstrated that the synthesized EvoNets can achieve state-of-the-art performance while having network architectures that are significantly more efficient ( ~19X fewer\sim48 synapses by the fourth generation) compared to the original ancestor deep neural network.
Edit intent: clarity
Original:  Experimental results using the MSRA-B dataset for the purpose of image segmentation was performed, and it was demonstrated that the synthesized EvoNets can achieve state-of-the-art F_beta score (0.872 at second generation, 0.859 at third generation, and 0.839 at fourth generation) while having synapse architectures that are significantly more efficient ( ~19X fewer\sim48 synapses by the fourth generation) compared to the original ancestor deep neural network.
Revised:  Experimental results using the MSRA-B dataset for the purpose of image segmentation was performed, and it was demonstrated that the synthesized EvoNets can achieve state-of-the-art F_beta score (0.872 at second generation, 0.859 at third generation, and 0.839 at fourth generation) while having synapse architectures that are significantly more efficient ( with a staggering\sim48 synapses by the fourth generation) compared to the original ancestor deep neural network.
Edit intent: clarity
Original:  Experimental results using the MSRA-B dataset for the purpose of image segmentation was performed, and it was demonstrated that the synthesized EvoNets can achieve state-of-the-art F_beta score (0.872 at second generation, 0.859 at third generation, and 0.839 at fourth generation) while having synapse architectures that are significantly more efficient ( ~19X fewer\sim48 synapses by the fourth generation) compared to the original ancestor deep neural network.
Revised:  Experimental results using the MSRA-B dataset for the purpose of image segmentation was performed, and it was demonstrated that the synthesized EvoNets can achieve state-of-the-art F_beta score (0.872 at second generation, 0.859 at third generation, and 0.839 at fourth generation) while having synapse architectures that are significantly more efficient ( ~19X fewer\sim48-fold decrease in synapses by the fourth generation) compared to the original ancestor deep neural network.
Edit intent: clarity
Original:  Experimental results using the MSRA-B dataset for the purpose of image segmentation was performed, and it was demonstrated that the synthesized EvoNets can achieve state-of-the-art F_beta score (0.872 at second generation, 0.859 at third generation, and 0.839 at fourth generation) while having synapse architectures that are significantly more efficient ( ~19X fewer\sim48 synapses by the fourth generation) compared to the original ancestor deep neural network.
Revised:  Experimental results using the MSRA-B dataset for the purpose of image segmentation was performed, and it was demonstrated that the synthesized EvoNets can achieve state-of-the-art F_beta score (0.872 at second generation, 0.859 at third generation, and 0.839 at fourth generation) while having synapse architectures that are significantly more efficient ( ~19X fewer\sim48 synapses by the fourth generation) compared to the original ancestor network.
Edit intent: clarity
Original:  Real logic is defined on a full first-order language where formulas have truth-value in the interval [0,1] and semantics defined concretely on the domain of real numbers.
Revised:  Real logic is defined on a first-order language where formulas have truth-value in the interval [0,1] and semantics defined concretely on the domain of real numbers.
Edit intent: clarity
Original:  Real logic is defined on a full first-order language where formulas have truth-value in the interval [0,1] and semantics defined concretely on the domain of real numbers.
Revised:  Real logic is defined on a full first-order language whereby formulas have truth-value in the interval [0,1] and semantics defined concretely on the domain of real numbers.
Edit intent: fluency
Original:  Logical constants are interpreted as (feature ) vectors of real numbers.
Revised:  Logical constants are interpreted as feature vectors of real numbers.
Edit intent: fluency
Original:  Real logic promotes a well-founded integration of deductive reasoning on knowledge-bases with efficient , data-driven relational machine learning.
Revised:  Real Logic promotes a well-founded integration of deductive reasoning on knowledge-bases with efficient , data-driven relational machine learning.
Edit intent: fluency
Original:  Real logic promotes a well-founded integration of deductive reasoning on knowledge-bases with efficient , data-driven relational machine learning.
Revised:  Real logic promotes a well-founded integration of deductive reasoning on a knowledge-base and efficient data-driven relational machine learning.
Edit intent: fluency
Original:  We show how Real Logic can be implemented in deep Tensor Neural Networks with the use of Google's TensorFlow primitives.
Revised:  We show how Real Logic can be implemented in deep Tensor Neural Networks with the use of Google's tensorflow primitives.
Edit intent: fluency
Original:  These inputs are derived from regular inputs by minor yet carefully selected perturbations that deceive the neural network into desired misclassifications.
Revised:  These inputs are derived from regular inputs by minor yet carefully selected perturbations that deceive machine learning models into desired misclassifications.
Edit intent: clarity
Original:  Existing work in this emerging field was largely specific to the domain of image classification, since images with their high-entropy  can be conveniently manipulated without changing the images' overall visual appearance.
Revised:  Existing work in this emerging field was largely specific to the domain of image classification, since the high-entropy can be conveniently manipulated without changing the images' overall visual appearance.
Edit intent: clarity
Original:  Existing work in this emerging field was largely specific to the domain of image classification, since images with their high-entropy  can be conveniently manipulated without changing the images' overall visual appearance.
Revised:  Existing work in this emerging field was largely specific to the domain of image classification, since images with their high-entropy of images can be conveniently manipulated without changing the images' overall visual appearance.
Edit intent: clarity
Original:  Here, we face severely constrained limits on crafting suitable samples when the problem of image classification is replaced by malware classification : (i) continuous, differentiable input domains are replaced by discrete, often binary inputs;
Revised:  The application domain of malware classification introduces additional constraints in the adversarial sample crafting problem when compared to the computer vision domain : (i) continuous, differentiable input domains are replaced by discrete, often binary inputs;
Edit intent: clarity
Original: A substantial  postreproductive lifespan (PRLS) is considered to have evolved adaptively in females of few mammalian species.
Revised: A substantial period of life after reproduction ends, known as postreproductive lifespan (PRLS) is considered to have evolved adaptively in females of few mammalian species.
Edit intent: clarity
Original:  PRLS has so far only been confirmed in  two non-human species , both odontocete cetaceans.
Revised:  PRLS has so far only been confirmed in only two non-human species , both odontocete cetaceans.
Edit intent: fluency
Original:  We investigate the evidence for PRLS in a third such species, the false killer whale, Pseudorca crassidens, using morphological evidence from reproductive tissue and a quantitative measure of PRLS  . We examined specimens from false killer whales from combined strandings (South Africa, 1981) and harvest (Japan 1979-80) .
Revised:  We investigate the evidence for PRLS in a third species, the false killer whale, Pseudorca crassidens, using morphological evidence from reproductive tissue and a quantitative measure of PRLS . We examined specimens from false killer whales from combined strandings (South Africa, 1981) and harvest (Japan 1979-80) .
Edit intent: clarity
Original:  We investigate the evidence for PRLS in a third such species, the false killer whale, Pseudorca crassidens, using morphological evidence from reproductive tissue and a quantitative measure of PRLS  . We examined specimens from false killer whales from combined strandings (South Africa, 1981) and harvest (Japan 1979-80) .
Revised:  We investigate the evidence for PRLS in a third such species, the false killer whale, Pseudorca crassidens, using a quantitative measure of PRLS . We examined specimens from false killer whales from combined strandings (South Africa, 1981) and harvest (Japan 1979-80) .
Edit intent: clarity
Original:  Ovulation had ceased in 50\% of whales over 45 years, and all whales over 55 years  had ovaries classified as postreproductive.
Revised:  Ovulation had ceased in 50\% of whales over 45 years, and all whales over 55 years old had ovaries classified as postreproductive.
Edit intent: clarity
Original:  We also calculated a quantitative measure of PRLS, known as postreproductive representation (PrR)  . PrR for the combined sample was 0.25 , whereas the mean of the simulated distribution for PrR under the null hypothesis of no PRLS was 0.03.
Revised:  We also calculated a measure of PRLS, known as postreproductive representation (PrR) . PrR for the combined sample was 0.25 , whereas the mean of the simulated distribution for PrR under the null hypothesis of no PRLS was 0.03.
Edit intent: clarity
Original:  We found compelling morphological and statistical evidence for PRLS in South African and Japanese pods of false killer whales, establishing this species as the third non-human mammal in which this phenomenon has been convincingly demonstrated in wild populations.
Revised:  We found morphological and statistical evidence for PRLS in South African and Japanese pods of false killer whales, establishing this species as the third non-human mammal in which this phenomenon has been convincingly demonstrated in wild populations.
Edit intent: clarity
Original:  We found compelling morphological and statistical evidence for PRLS in South African and Japanese pods of false killer whales, establishing this species as the third non-human mammal in which this phenomenon has been convincingly demonstrated in wild populations.
Revised:  We found compelling morphological and statistical evidence for PRLS in South African and Japanese pods of false killer whales, suggesting that this species is the third non-human mammal in which this phenomenon has been convincingly demonstrated in wild populations.
Edit intent: clarity
Original:  We found compelling morphological and statistical evidence for PRLS in South African and Japanese pods of false killer whales, establishing this species as the third non-human mammal in which this phenomenon has been convincingly demonstrated in wild populations.
Revised:  We found compelling morphological and statistical evidence for PRLS in South African and Japanese pods of false killer whales, establishing this species as the third non-human mammal in which this phenomenon has been demonstrated in wild populations.
Edit intent: clarity
Original:  Our estimates for PrR in false killer whales ( 0.22-0.44 ) spanned the single value available for the short-finned pilot whale (0.28  ) and are comparable to estimates for historical or hunter-gather human populations (0.3-0.47).
Revised:  Our estimates for PrR in false killer whales ( 0.22-0.44 ) spanned the single values available for the short-finned pilot whale (0.28 ) and are comparable to estimates for historical or hunter-gather human populations (0.3-0.47).
Edit intent: fluency
Original:  In our experiments we show that QRN outperforms the state of the art in end-to-end bAbI QA tasks.
Revised:  In our experiments we show that QRN obtains the state-of-the-art result in end-to-end bAbI QA tasks.
Edit intent: clarity
Original:  In our experiments we show that QRN obtains the state-of-the-art result in end-to-end bAbI QA tasks .
Revised:  In our experiments we show that QRN produces the state-of-the-art result in end-to-end bAbI QA tasks .
Edit intent: clarity
Original:  QRN considers the context sentences as a sequence of state-changing triggers, and reduces the original query to an easier-to-answer query as it observes each trigger (context sentence) through time.
Revised:  QRN considers the context sentences as a sequence of state-changing triggers, and reduces the original query to a more informed query as it observes each trigger (context sentence) through time.
Edit intent: clarity
Original: Recognizing the actions of others from complex visual scenes is an essential task for humans .
Revised: The ability to recognize the actions of others from complex visual scenes is an essential task for humans .
Edit intent: clarity
Original: Recognizing the actions of others from complex visual scenes is an essential task for humans .
Revised: Recognizing the actions of others is a core component of human visual intelligence .
Edit intent: clarity
Original:  Here we investigate the computational mechanisms that support action recognition in the human visual system  .
Revised:  Here we investigate the computational mechanisms that allow the human visual system .
Edit intent: clarity
Original:  We use a novel dataset of well-controlled naturalistic videos of five actions performed by five actors at five viewpoint and extend a class of biologically inspired hierarchical computational models of object recognition to recognize actions from videos.
Revised:  We use a novel dataset of well-controlled naturalistic videos of five actions performed by five actors at five viewpoints and extend a class of biologically inspired hierarchical computational models of object recognition to recognize actions from videos.
Edit intent: fluency
Original:  We explore a number of variations within the class of convolutional neural networks and assess classification accuracy on a viewpoint invariant action recognition task as well as a correlation measure with magnetoencephalography (MEG) human brain recordings acquired using the same stimuli .
Revised:  We explore a number of variations within the class of convolutional neural networks and assess the performance of each model on a viewpoint invariant action recognition task as well as a correlation measure with magnetoencephalography (MEG) human brain recordings acquired using the same stimuli .
Edit intent: clarity
Original:  We explore a number of variations within the class of convolutional neural networks and assess classification accuracy on a viewpoint invariant action recognition task as well as a correlation measure with magnetoencephalography (MEG) human brain recordings acquired using the same stimuli .
Revised:  We explore a number of variations within the class of convolutional neural networks and assess classification accuracy on a viewpoint invariant action recognition task as well as how each one matches human neural activity measured with magnetoencephalography (MEG) human brain recordings acquired using the same stimuli .
Edit intent: clarity
Original:  We explore a number of variations within the class of convolutional neural networks and assess classification accuracy on a viewpoint invariant action recognition task as well as a correlation measure with magnetoencephalography (MEG) human brain recordings acquired using the same stimuli .
Revised:  We explore a number of variations within the class of convolutional neural networks and assess classification accuracy on a viewpoint invariant action recognition task as well as a correlation measure with magnetoencephalography (MEG) .
Edit intent: clarity
Original:  We show that feed-forward spatio-temporal convolutional neural networks solve the task of invariant action recognition  and account for the majority of the explainable variance in the neural data.
Revised:  We show that feed-forward spatio-temporal convolutional neural networks perform well on invariant action recognition and account for the majority of the explainable variance in the neural data.
Edit intent: clarity
Original:  We show that feed-forward spatio-temporal convolutional neural networks solve the task of invariant action recognition  and account for the majority of the explainable variance in the neural data.
Revised:  We show that feed-forward spatio-temporal convolutional neural networks solve the task of invariant action recognition tasks and account for the majority of the explainable variance in the neural data.
Edit intent: clarity
Original:  Furthermore, we show that model features that improve performance on viewpoint invariant action recognition lead to a model representation that better matches the representation encoded by neural data.
Revised:  Furthermore, we show that model features that improve performance on viewpoint invariant action recognition lead to model representations that better match human neural data.
Edit intent: clarity
Original:  These results advance the idea that robustness to complex transformations, such as 3D viewpoint invariance, is a specific goal of visual processing in the human brain.
Revised:  These results advance the idea that robustness to complex transformations, such as 3D viewpoint invariance, is a specific computational goal driving the organization of visual processing in the human brain.
Edit intent: clarity
Original: The ability to recognize the actions of others is a core component of human visual intelligence.
Revised: Recognizing the actions of others is a core component of human visual intelligence.
Edit intent: clarity
Original: The ability to recognize the actions of others is a core component of human visual intelligence.
Revised: The ability to recognize the actions of others from visual stimuli is a crucial aspect of human visual intelligence.
Edit intent: clarity
Original:  We show that model features that improve performance on viewpoint invariant action recognition lead to model representations that better match human neural data .
Revised:  We show that model features that improve performance on an invariant action recognition lead to model representations that better match human neural data .
Edit intent: fluency
Original:  We show that model features that improve performance on viewpoint invariant action recognition lead to model representations that better match human neural data .
Revised:  We show that model features that improve performance on viewpoint invariant action recognition task lead to data representations that better match human neural data .
Edit intent: clarity
Original:  We show that model features that improve performance on viewpoint invariant action recognition lead to model representations that better match human neural data .
Revised:  We show that model features that improve performance on viewpoint invariant action recognition lead to model representations that better match human neural recordings .
Edit intent: clarity