wanyu/IteraTeR_v2 huggingface dataset:
Original:  \Re^{M} }\to  , we show that equivariance of \phi_W is linked to the symmetry group of network parameters W. We then propose a sparse parameter-sharing scheme to induce the desirable symmetry on W. Under some conditions on the action of G, our procedure for tying the parameters achieves G-equivariance and guarantee sensitivity to all other permutation groups outside G. We demonstrate the relation of our approach to recently-proposed "structured"
Revised:  \Re^{M} }\to , we show that equivariance of \phi_W is linked to the network parameters W. We then propose a sparse parameter-sharing scheme to induce the desirable symmetry on W. Under some conditions on the action of G, our procedure for tying the parameters achieves G-equivariance and guarantee sensitivity to all other permutation groups outside G. We demonstrate the relation of our approach to recently-proposed "structured"
Edit intent: clarity
Original:  \Re^{M} }\to  , we show that equivariance of \phi_W is linked to the symmetry group of network parameters W. We then propose a sparse parameter-sharing scheme to induce the desirable symmetry on W. Under some conditions on the action of G, our procedure for tying the parameters achieves G-equivariance and guarantee sensitivity to all other permutation groups outside G. We demonstrate the relation of our approach to recently-proposed "structured"
Revised:  \Re^{M} }\to , we show that equivariance of \phi_W is linked to the symmetry group of network parameters W. Inspired by this observation, we then propose two parameter-sharing scheme to induce the desirable symmetry on W. Under some conditions on the action of G, our procedure for tying the parameters achieves G-equivariance and guarantee sensitivity to all other permutation groups outside G. We demonstrate the relation of our approach to recently-proposed "structured"
Edit intent: clarity
Original:  \Re^{M} }\to  , we show that equivariance of \phi_W is linked to the symmetry group of network parameters W. We then propose a sparse parameter-sharing scheme to induce the desirable symmetry on W. Under some conditions on the action of G, our procedure for tying the parameters achieves G-equivariance and guarantee sensitivity to all other permutation groups outside G. We demonstrate the relation of our approach to recently-proposed "structured"
Revised:  \Re^{M} }\to , we show that equivariance of \phi_W is linked to the symmetry group of network parameters W. We then propose a sparse parameter-sharing schemes to induce the desirable symmetry on W. Under some conditions on the action of G, our procedure for tying the parameters achieves G-equivariance and guarantee sensitivity to all other permutation groups outside G. We demonstrate the relation of our approach to recently-proposed "structured"
Edit intent: fluency
Original:  \Re^{M} }\to  , we show that equivariance of \phi_W is linked to the symmetry group of network parameters W. We then propose a sparse parameter-sharing scheme to induce the desirable symmetry on W. Under some conditions on the action of G, our procedure for tying the parameters achieves G-equivariance and guarantee sensitivity to all other permutation groups outside G. We demonstrate the relation of our approach to recently-proposed "structured"
Revised:  \Re^{M} }\to , we show that equivariance of \phi_W is linked to the symmetry group of network parameters W. We then propose a sparse parameter-sharing scheme to induce the desirable symmetry on W. Our procedures for tying the parameters achieve G-equivariance and, under some conditions on the action of G, our procedure for tying the parameters achieves G-equivariance and guarantee sensitivity to all other permutation groups outside G. We demonstrate the relation of our approach to recently-proposed "structured"
Edit intent: coherence
Original:  Then we demonstrate the Baldwin effect to happen in the in-silico scenario by estimating the probability and waiting times for the learned behavior to become innate.
Revised:  Then we demonstrate the Baldwin effect to happen in the in silico scenario by estimating the probability and waiting times for the learned behavior to become innate.
Edit intent: fluency
Original:  We find that learning increases the chance of fixation of the learned behavior by at least 7 orders of magnitude compared with the non-learning situation.
Revised:  We find that learning can increase the chance of fixation of the learned behavior by at least 7 orders of magnitude compared with the non-learning situation.
Edit intent: clarity
Original:  Complex Syst. 1, 495--502 ] claimed that this effect can be observed in silico, but here we argue that what was actually shown is that the learning ability is easily selected for.
Revised:  Complex Syst. 1, 495-502 ] claimed that this effect can be observed in silico, but here we argue that what was actually shown is that the learning ability is easily selected for.
Edit intent: fluency
Original:  However, going back to the work of Abrams and Lloyd, changing one aspect of quantum theory can result in yet more dramatic increases in computational power , as well as violations of fundamental physical principles.
Revised:  However, going back to the work of Abrams and Lloyd, changing one aspect of quantum theory yield computational power that is dramatically greater still. However, these modifications also violate fundamental physical principles.
Edit intent: clarity
Original:  In this work we show that this bound is tight : there exists a theory satisfying both the principles of tomographic localityand causality which can efficiently decide everything in AWPP , and in particular can simulate any efficient quantum computation .
Revised:  In this work we show that this bound is tight , in the sense that there exists a theory satisfying both the principles of tomographic localityand causality which can efficiently decide everything in AWPP , and in particular can simulate any efficient quantum computation .
Edit intent: clarity
Original:  In this work we show that this bound is tight : there exists a theory satisfying both the principles of tomographic localityand causality which can efficiently decide everything in AWPP , and in particular can simulate any efficient quantum computation .
Revised:  In this work we show that this bound is tight : there exists a theory , satisfying tomographic locality, as well as a basic principle of causality, which can efficiently decide everything in AWPP , and in particular can simulate any efficient quantum computation .
Edit intent: clarity
Original:  One approach has been to test whether neutral predictions are consistent with observed progeny distributions, recording the number of variants that have produced a given number of new instances within a specified time interval: the classic example is the distribution of baby names registered within a given year.
Revised:  One approach has been to test whether neutral predictions are consistent with observed progeny distributions, recording the number of variants that have produced a given number of new instances within a specified time interval: a classic example is the distribution of baby names registered within a given year.
Edit intent: fluency
Original:  Using an overlapping generations model we show that the progeny distribution consists of two phases: a power law phase with an universally-applicable exponent of -3/2, followed by an exponential cut-off for variants with very large numbers of progeny.
Revised:  Using an overlapping generations model we show that these distributions consist of two phases: a power law phase with an universally-applicable exponent of -3/2, followed by an exponential cut-off for variants with very large numbers of progeny.
Edit intent: clarity
Original:  Using an overlapping generations model we show that the progeny distribution consists of two phases: a power law phase with an universally-applicable exponent of -3/2, followed by an exponential cut-off for variants with very large numbers of progeny.
Revised:  Using an overlapping generations model we show that the progeny distribution consists of two phases: a power law phase with a constant exponent of -3/2, followed by an exponential cut-off for variants with very large numbers of progeny.
Edit intent: clarity
Original:  Maximum likelihood estimations of the model parameters then provide a direct way to parameterize the neutral model .
Revised:  Maximum likelihood estimations of the model parameters provide a direct way to parameterize the neutral model .
Edit intent: coherence
Original:  Maximum likelihood estimations of the model parameters then provide a direct way to parameterize the neutral model .
Revised:  Maximum likelihood estimations of the model parameters then provide a direct way to establish whether observed empirical patterns are consistent with neutral evolution .
Edit intent: clarity
Original:  We apply our approach to a  data set of baby names from Australia.
Revised:  We apply our approach to a complete data set of baby names from Australia.
Edit intent: clarity
Original:   While neutrality provides a plausible description of the progeny distribution of abundant variants, rare variants deviate from neutrality.
Revised:  While neutrality provides a plausible description of progeny distributions of abundant variants, rare variants deviate from neutrality.
Edit intent: fluency
Original:  We show that a kind of anti-novelty bias , where new variants are at a disadvantage by virtue of their novelty, is able to replicate more closely the complete progeny distribution of the Australian data set.
Revised:  We show that anti-novelty bias , where new variants are at a disadvantage by virtue of their novelty, is able to replicate more closely the complete progeny distribution of the Australian data set.
Edit intent: clarity
Original:  We show that a kind of anti-novelty bias , where new variants are at a disadvantage by virtue of their novelty, is able to replicate more closely the complete progeny distribution of the Australian data set.
Revised:  We show that a kind of anti-novelty bias is able to replicate more closely the complete progeny distribution of the Australian data set.
Edit intent: clarity
Original:  We show that a kind of anti-novelty bias , where new variants are at a disadvantage by virtue of their novelty, is able to replicate more closely the complete progeny distribution of the Australian data set.
Revised:  We show that a kind of anti-novelty bias , where new variants are at a disadvantage by virtue of their novelty, is able to replicate the complete progeny distribution of the Australian data set.
Edit intent: clarity
Original:  Moreover, we establish a cognitive model to reflect the essential characteristics of the recognition of this type of patterns.  ]
Revised:  Moreover, we establish a cognitive model to reflect the essential characteristics of the recognition of this type of patterns. In ]
Edit intent: fluency
Original:  Our theoretical result generalizes previous results with fewer assumptions  .
Revised:  Our theoretical results generalize previous results with fewer assumptions .
Edit intent: fluency
Original:  The problem has largely been overcome through the introduction of carefully constructed initializations and batch normalization .
Revised:  Although, the problem has largely been overcome through the introduction of carefully constructed initializations and batch normalization .
Edit intent: coherence
Original:  The problem has largely been overcome through the introduction of carefully constructed initializations and batch normalization .
Revised:  The problem has largely been overcome via carefully constructed initializations and batch normalization .
Edit intent: clarity
Original:  In contrast, the gradients in architectures with skip-connections are far more resistant to shattering  decaying sublinearly.
Revised:  In contrast, the gradients in architectures with skip-connections are far more resistant to shattering , decaying sublinearly.
Edit intent: fluency
Original:  Finally, we present a new "looks linear" (LL) initialization that prevents shattering . Preliminary experiments show the new initialization allows to train very deep networks without the addition of skip-connections.
Revised:  Finally, we present a new "looks linear" (LL) initialization that prevents shattering , with preliminary experiments showing the new initialization allows to train very deep networks without the addition of skip-connections.
Edit intent: clarity
Original:  For each country, two statistics , gross domestic product per capita and average life expectancy  , were used to quantify the development.
Revised:  For each country, four indicators , gross domestic product per capita and average life expectancy , were used to quantify the development.
Edit intent: clarity
Original:  For each country, two statistics , gross domestic product per capita and average life expectancy  , were used to quantify the development.
Revised:  For each country, two statistics , gross domestic product per capita ; average life expectancy , were used to quantify the development.
Edit intent: coherence
Original:  The first considers clusters of the countries based on these two statistics , and the second uncovers cycles in the data when combined with geographic network structure.
Revised:  The first considers clusters of the countries based on these indicators , and the second uncovers cycles in the data when combined with geographic network structure.
Edit intent: clarity
Original:  The first considers clusters of the countries based on these two statistics , and the second uncovers cycles in the data when combined with geographic network structure.
Revised:  The first considers clusters of the countries based on these two statistics , and the second uncovers cycles in the data when combined with geographic border structure.
Edit intent: clarity
Original:  Our analyses reveal that there is not a clear distinction of "first" and "third" world countries , and we discovered localized development patterns that are invisible in standard representations .
Revised:  Our analyses reveal that there is not a clear distinction of "first" and "third" world countries , and we discovered localized development patterns that are invisible in standard statistical methods .
Edit intent: clarity
Original:  The algorithm is essentially an averaged beat subtraction algorithm, where the ventricular activity template is estimated by combining a newly designed metric, the  diffusion distance,  and the non-local Euclidean median based on the non-linear manifold setup.
Revised:  The algorithm is essentially an averaged beat subtraction algorithm, where the ventricular activity template is estimated by combining a newly designed metric, the " diffusion distance, and the non-local Euclidean median based on the non-linear manifold setup.
Edit intent: fluency
Original:  The algorithm is essentially an averaged beat subtraction algorithm, where the ventricular activity template is estimated by combining a newly designed metric, the  diffusion distance,  and the non-local Euclidean median based on the non-linear manifold setup.
Revised:  The algorithm is essentially an averaged beat subtraction algorithm, where the ventricular activity template is estimated by combining a newly designed metric, the diffusion distance, " and the non-local Euclidean median based on the non-linear manifold setup.
Edit intent: fluency
Original: In the General Position Subset Selection (GPSS) problem, the goal is to find the largest possible subset of a set of points , such that no three of its members are collinear.
Revised: In the General Position Subset Selection (GPSS) problem, the goal is to find the largest possible subset of a set of points such that no three of its members are collinear.
Edit intent: fluency
Original:  If s_{ GPSS } is the size  the optimal solution, \sqrt{s_{\textrm{GPSS is the current best guarantee for the size of the solution obtained using a polynomial time algorithm.
Revised:  If s_{ GPSS } is the size of the optimal solution, \sqrt{s_{\textrm{GPSS is the current best guarantee for the size of the solution obtained using a polynomial time algorithm.
Edit intent: fluency
Original:  If s_{ GPSS } is the size  the optimal solution, \sqrt{s_{\textrm{GPSS is the current best guarantee for the size of the solution obtained using a polynomial time algorithm.
Revised:  If s_{ GPSS } is the size the optimal solution, \sqrt{s_{\mathrm{GPSS is the current best guarantee for the size of the solution obtained using a polynomial time algorithm.
Edit intent: fluency
Original:  We introduce a technique of general applicability to use hard nonlinearities with saturation cost .
Revised:  We introduce a new technique - hard nonlinearities with saturation cost .
Edit intent: clarity
Original:  Let \mathsf{stab (G) be the convex hull of the stable sets of G. Since G is perfect, it is easy to see that n \leqslant xc ( \mathsf{stab (G)) \leqslant n+m.
Revised:  Let \mathsf{STAB (G) be the convex hull of the stable sets of G. Since G is perfect, it is easy to see that n \leqslant xc ( \mathsf{stab (G)) \leqslant n+m.
Edit intent: clarity
Original:  Let \mathsf{stab (G) be the convex hull of the stable sets of G. Since G is perfect, it is easy to see that n \leqslant xc ( \mathsf{stab (G)) \leqslant n+m.
Revised:  Let \mathsf{stab (G) be the convex hull of the stable sets of G. It is easy to see that n \leqslant xc ( \mathsf{stab (G)) \leqslant n+m.
Edit intent: coherence
Original:  Let \mathsf{stab (G) be the convex hull of the stable sets of G. Since G is perfect, it is easy to see that n \leqslant xc ( \mathsf{stab (G)) \leqslant n+m.
Revised:  Let \mathsf{stab (G) be the convex hull of the stable sets of G. Since G is perfect, it is easy to see that n \leqslant xc ( \mathsf{STAB (G)) \leqslant n+m.
Edit intent: clarity
Original:  For the upper bound, we show that xc ( \mathsf{stab (G)) is O(n^2{\log n}), which is an improvement when G has quadratically many edges.
Revised:  For the upper bound, we show that xc ( \mathsf{STAB (G)) is O(n^2{\log n}), which is an improvement when G has quadratically many edges.
Edit intent: fluency
Original:  For the lower bound, we prove that xc ( \mathsf{stab (G)) is \Omega(n \log n) when G is the incidence graph of a finite projective plane.
Revised:  For the lower bound, we prove that xc ( \mathsf{STAB (G)) is \Omega(n \log n) when G is the incidence graph of a finite projective plane.
Edit intent: fluency
Original:  But , in applications, the parameters of the problem are rarely known exactly, and using wrong parameters can lead to undesirable outcomes.
Revised:  But in applications, the parameters of the problem are rarely known exactly, and using wrong parameters can lead to undesirable outcomes.
Edit intent: fluency
Original:  Our construction yields , in particular, a new characterisation of the profinite monad of the free S-semimodule monad for finite  commutative semirings S, which generalises our earlier insight that the Vietoris monad on Boolean spaces is the codensity monad of the finite powerset functor.
Revised:  Our construction hinges , in particular, a new characterisation of the profinite monad of the free S-semimodule monad for finite commutative semirings S, which generalises our earlier insight that the Vietoris monad on Boolean spaces is the codensity monad of the finite powerset functor.
Edit intent: clarity
Original:  Our construction yields , in particular, a new characterisation of the profinite monad of the free S-semimodule monad for finite  commutative semirings S, which generalises our earlier insight that the Vietoris monad on Boolean spaces is the codensity monad of the finite powerset functor.
Revised:  Our construction yields , in particular, a new characterisation of the profinite monad of the free S-semimodule monad for finite and commutative semirings S, which generalises our earlier insight that the Vietoris monad on Boolean spaces is the codensity monad of the finite powerset functor.
Edit intent: clarity
Original:  In particular, we provide a general construction on recognisers corresponding to adding one layer of various kinds of quantifiers and prove a related Reutenauer-type theorem.
Revised:  In particular, we provide a general construction on recognisers corresponding to adding one layer of various kinds of quantifiers and prove a corresponding Reutenauer-type theorem.
Edit intent: clarity
Original:  Our construction hinges , in particular, on a measure-theoretic characterisation of the profinite monad of the free S-semimodule monad for finite and commutative semirings S, which generalises our earlier insight that the Vietoris monad on Boolean spaces is the codensity monad of the finite powerset functor.
Revised:  Our construction hinges on a measure-theoretic characterisation of the profinite monad of the free S-semimodule monad for finite and commutative semirings S, which generalises our earlier insight that the Vietoris monad on Boolean spaces is the codensity monad of the finite powerset functor.
Edit intent: coherence
Original:  A key stumbling block is that independent Q-learning, the most popular multi-agent RL method, introduces nonstationarity that makes it incompatible with the experience replay memory on which deep RL relies.
Revised:  A major stumbling block is that independent Q-learning, the most popular multi-agent RL method, introduces nonstationarity that makes it incompatible with the experience replay memory on which deep RL relies.
Edit intent: clarity
Original:  A key stumbling block is that independent Q-learning, the most popular multi-agent RL method, introduces nonstationarity that makes it incompatible with the experience replay memory on which deep RL relies.
Revised:  A key stumbling block is that independent Q-learning, the most popular multi-agent RL method, introduces nonstationarity that makes it incompatible with the experience replay memory on which deep Q-learning relies.
Edit intent: fluency
Original:  This paper proposes two methods that address this problem: 1)  conditioning each agent's value function on a footprint that disambiguates the age of the data sampled from the replay memory and 2) using a multi-agent variant of importance sampling to naturally decay obsolete data .
Revised:  This paper proposes two methods that address this problem: 1) using a multi-agent variant of importance sampling to naturally decay obsolete data and 2) conditioning each agent's value function on a footprint that disambiguates the age of the data sampled from the replay memory and 2) using a multi-agent variant of importance sampling to naturally decay obsolete data .
Edit intent: coherence
Original:  This paper proposes two methods that address this problem: 1)  conditioning each agent's value function on a footprint that disambiguates the age of the data sampled from the replay memory and 2) using a multi-agent variant of importance sampling to naturally decay obsolete data .
Revised:  This paper proposes two methods that address this problem: 1) conditioning each agent's value function on a fingerprint that disambiguates the age of the data sampled from the replay memory and 2) using a multi-agent variant of importance sampling to naturally decay obsolete data .
Edit intent: clarity
Original:  This paper proposes two methods that address this problem: 1)  conditioning each agent's value function on a footprint that disambiguates the age of the data sampled from the replay memory and 2) using a multi-agent variant of importance sampling to naturally decay obsolete data .
Revised:  This paper proposes two methods that address this problem: 1) conditioning each agent's value function on a footprint that disambiguates the age of the data sampled from the replay memory .
Edit intent: coherence