wanyu/IteraTeR_v2 huggingface dataset:
Original:  The current paper attempts to rectify this by making these connectionsexplicit, reviewing (and making use of) some of the important existing work on discrete splines, and contributing several new perspectives and new results on discrete splines along the way.
Revised:  The current paper attempts to rectify this by making these connectionsexplicit, reviewing (and making use of) some of the important existing work on discrete splines, and contributing several new perspectives and new results along the way.
Edit intent: fluency
Original: Since integration by parts is an important tool when deriving energy or entropy estimates for differential equations, one may conjecture that some form of summation by parts (SBP) property is necessarily involved in provably stable numerical methods.
Revised: Since integration by parts is an important tool when deriving energy or entropy estimates for differential equations, one may conjecture that some form of summation by parts (SBP) property is involved in provably stable numerical methods.
Edit intent: clarity
Original:  This is an unsupervised way to learn general-purpose graph representations, it can be used for both graph sketching and graph comparison.
Revised:  This gives an unsupervised way to learn general-purpose graph representations, it can be used for both graph sketching and graph comparison.
Edit intent: clarity
Original:  This is an unsupervised way to learn general-purpose graph representations, it can be used for both graph sketching and graph comparison.
Revised:  This is an unsupervised way to learn general-purpose graph representation, applicable to both graph sketching and graph comparison.
Edit intent: clarity
Original: A purely inter-model version of a machine intelligencebenchmark would allow us to measure intelligence directly as information without projecting that information onto labeled datasets .
Revised: As with other commodities, markets could help us efficiently produce machine intelligence .
Edit intent: clarity
Original:  We propose a framework in which other learners measure the informational significance of their peers across a network and use a digital ledger to negotiate the scores .
Revised:  We propose a market where intelligence is priced by other intelligence systems peer-to-peer across the internet. Peers rank each other by training neural networks which learn the value of their neighbors. Scores accumulate on a digital ledger where high ranking peers are monetarily rewarded with additional weight in the network .
Edit intent: clarity
Original:  The result is a fair mechanism for training an internet-wide, decentralized and incentivized machine learning system, one which produces a continually hardening and expanding benchmark at the generalized intersection of the participants .
Revised:  The result is a collectively run intelligence market which continual produces newly trained models and pays contributors who create information theoretic value .
Edit intent: clarity
Original: Cross-modal food retrieval is an important task to perform analysis of food-related information, such as food images and cooking recipes.
Revised: Food retrieval is an important task to perform analysis of food-related information, such as food images and cooking recipes.
Edit intent: clarity
Original:  Compared with existing cross-modal retrieval approaches, two major challenges in this specific problem are : 1) the large intra-class variance across cross-modal food data;
Revised:  Compared with existing cross-modal retrieval approaches, two major challenges in addressing this problem are 1) the large intra-class variance across cross-modal food data;
Edit intent: clarity
Original:  and 2) the difficulties in obtaining discriminative recipe representations.
Revised:  and 2) difficulties in obtaining discriminative recipe representations.
Edit intent: fluency
Original:  To address these  problems, we propose Semantic-Consistent and Attention-based Networks (SCAN), which regularize the embeddings of the two modalities by aligning output semantic probabilities.
Revised:  To address these two problems, we propose Semantic-Consistent and Attention-based Networks (SCAN), which regularize the embeddings of the two modalities by aligning output semantic probabilities.
Edit intent: fluency
Original:  To address these  problems, we propose Semantic-Consistent and Attention-based Networks (SCAN), which regularize the embeddings of the two modalities by aligning output semantic probabilities.
Revised:  To address these problems, we propose Semantic-Consistent and Attention-based Networks (SCAN), which regularize the embeddings of the two modalities through aligning output semantic probabilities.
Edit intent: fluency
Original:  In addition , we exploit  self-attention mechanism to improve the embedding of recipes.
Revised:  Besides , we exploit self-attention mechanism to improve the embedding of recipes.
Edit intent: coherence
Original:  In addition , we exploit  self-attention mechanism to improve the embedding of recipes.
Revised:  In addition , we exploit a self-attention mechanism to improve the embedding of recipes.
Edit intent: fluency
Original:  We evaluate the performance of the proposed method on the large-scale Recipe1M dataset, and the result shows that it outperforms the state-of-the-art  .
Revised:  We evaluate the performance of the proposed method on the large-scale Recipe1M dataset, and show that we can outperform several state-of-the-art .
Edit intent: clarity
Original: In this paper , a block inertial Bregman proximal algorithm , namely%DIFDELCMD < \refBIBPA[]%%% , for solving structured nonsmooth nonconvex optimization problems is introduced.
Revised: In this paper , a block inertial Bregman proximal algorithm %DIFDELCMD < \refBIBPA[]%%% , for solving structured nonsmooth nonconvex optimization problems is introduced.
Edit intent: coherence
Original:  More specifically, the objective function is the sum of a block relatively smooth function ( i.e. , relatively smooth with respect to each block) and block separable (nonsmooth ) nonconvex functions.
Revised:  More specifically, the objective function is the sum of a block relatively smooth function ( that is , relatively smooth with respect to each block) and block separable (nonsmooth ) nonconvex functions.
Edit intent: clarity
Original:  More specifically, the objective function is the sum of a block relatively smooth function ( i.e. , relatively smooth with respect to each block) and block separable (nonsmooth ) nonconvex functions.
Revised:  More specifically, the objective function is the sum of a block relatively smooth function ( i.e. , relatively smooth concerning each block) and block separable (nonsmooth ) nonconvex functions.
Edit intent: clarity
Original:  More specifically, the objective function is the sum of a block relatively smooth function ( i.e. , relatively smooth with respect to each block) and block separable (nonsmooth ) nonconvex functions.
Revised:  More specifically, the objective function is the sum of a block relatively smooth function ( i.e. , relatively smooth with respect to each block) and block separable nonsmooth nonconvex functions.
Edit intent: fluency
Original:  It is shown that the sequence generated by %DIFDELCMD < \refBIBPA[] %%% converges subsequentially to critical points , while it is globally convergent for Kurdyka-{\L}ojasiewicz (K{ \L} ) functions.
Revised:  We prove that the sequence generated by %DIFDELCMD < \refBIBPA[] %%% converges subsequentially to critical points , while it is globally convergent for Kurdyka-{\L}ojasiewicz (K{ \L} ) functions.
Edit intent: clarity
Original:  Furthermore, the rate of convergence of this sequence is studied for the {\L} ojasiewicz-type K \L%DIFDELCMD < }} %%% functions .
Revised:  Furthermore, the rate of convergence of this sequence is studied for the {\L} \L%DIFDELCMD < }} %%% functions .
Edit intent: coherence
Original:  Furthermore, the rate of convergence of this sequence is studied for the {\L} ojasiewicz-type K \L%DIFDELCMD < }} %%% functions .
Revised:  Furthermore, the rate of convergence of this sequence is studied for the {\L} ojasiewicz-type K %DIFDELCMD < }} %%% functions .
Edit intent: coherence
Original: A potential solution to the weight transport problem, which questions the biological plausibility of the backpropagation of error algorithm, is proposed .
Revised: We propose a solution to the weight transport problem, which questions the biological plausibility of the backpropagation of error algorithm, is proposed .
Edit intent: clarity
Original: A potential solution to the weight transport problem, which questions the biological plausibility of the backpropagation of error algorithm, is proposed .
Revised: A potential solution to the weight transport problem, which questions the biological plausibility of the backpropagation algorithm .
Edit intent: clarity
Original:  We derive our method based upon an (approximate) analysis of the  dynamics of leaky integrate-and-fire neurons.
Revised:  We derive our method based upon an (approximate) analysis of the (approximate) dynamics of leaky integrate-and-fire neurons.
Edit intent: fluency
Original:  We thereafter validate our method and show that the use of spike timing alone out-competes existing biologically plausible methods for synaptic weight inference in spiking neural network models.
Revised:  We show that the use of spike timing alone out-competes existing biologically plausible methods for synaptic weight inference in spiking neural network models.
Edit intent: coherence
Original:  We thereafter validate our method and show that the use of spike timing alone out-competes existing biologically plausible methods for synaptic weight inference in spiking neural network models.
Revised:  We thereafter validate our method and show that the use of spike timing alone outcompetes existing biologically plausible methods for synaptic weight inference in spiking neural network models.
Edit intent: fluency
Original:  These features, together with its biological plausibility, make it an attractive candidate technique for weight inference at single synapses.
Revised:  These features, together with its biological plausibility, make it an attractive mechanism underlying weight inference at single synapses.
Edit intent: clarity
Original:  However, CS is a combination of complex combinatorial optimization problems (with NP-hard computational complexity), namelycrew pairing andcrew assignment , which are solved sequentially.
Revised:  However, CS is a combination of complex combinatorial optimization problems (with NP-hard crew pairing andcrew assignment , which are solved sequentially.
Edit intent: clarity
Original:  However, CS is a combination of complex combinatorial optimization problems (with NP-hard computational complexity), namelycrew pairing andcrew assignment , which are solved sequentially.
Revised:  However, CS is a combination of complex combinatorial optimization problems (with NP-hard computational complexity), namely andcrew assignment , which are solved sequentially.
Edit intent: coherence
Original:  However, CS is a combination of complex combinatorial optimization problems (with NP-hard computational complexity), namelycrew pairing andcrew assignment , which are solved sequentially.
Revised:  However, CS is a combination of complex combinatorial optimization problems (with NP-hard computational complexity), namelycrew pairing crew assignment , which are solved sequentially.
Edit intent: fluency
Original:  However, CS is a combination of complex combinatorial optimization problems (with NP-hard computational complexity), namelycrew pairing andcrew assignment , which are solved sequentially.
Revised:  However, CS is a combination of complex combinatorial optimization problems (with NP-hard computational complexity), namelycrew pairing and , which are solved sequentially.
Edit intent: clarity
Original:  Here,crew pairing optimization aims at generating a set of flight sequences (each called a crew pairing)to cover a finite set of flight legs from an airline's timetable at minimum cost, while satisfying several legality constraints linked to the federations' safety rules, airline-specific regulations, labor laws, etc.
Revised:  Here, aims at generating a set of flight sequences (each called a crew pairing)to cover a finite set of flight legs from an airline's timetable at minimum cost, while satisfying several legality constraints linked to the federations' safety rules, airline-specific regulations, labor laws, etc.
Edit intent: clarity
Original:  Subsequently,\textit{crew assignment aims at assigning crew members to these optimal crew pairings.
Revised:  Subsequently,\textit{ aims at assigning crew members to these optimal crew pairings.
Edit intent: clarity
Original: We show that a deep neural network can learn the semantics of  linear-time temporal logic (LTL)  . As a challenging task that requires deep understanding of the LTL semantics, we show that our network can solve the trace generation problem for LTL: given a satisfiable LTL formula, find a trace that satisfies the formula.
Revised: We show that neural networks can learn the semantics of linear-time temporal logic (LTL) . As a challenging task that requires deep understanding of the LTL semantics, we show that our network can solve the trace generation problem for LTL: given a satisfiable LTL formula, find a trace that satisfies the formula.
Edit intent: clarity
Original: We show that neural networks can learn the semantics of propositional and linear-time temporal logic (LTL) from imperfect training data.
Revised: We show that neural networks can learn the semantics of logics. In this work we focus on linear-time temporal logic (LTL) from imperfect training data.
Edit intent: clarity
Original:  Following up on the study of Brauer et . al. (IEEE ICASSP 2016), we significantly extend the range of the evaluation scenarios: we introduce the analysis (cosparse) model, we use more effective algorithms, we experiment with another time-frequency transform.
Revised:  Following up on the study of Brauer et al. (IEEE ICASSP 2016), we significantly extend the range of the evaluation scenarios: we introduce the analysis (cosparse) model, we use more effective algorithms, we experiment with another time-frequency transform.
Edit intent: fluency
Original:  In this case, in each time period, reporting one's type is a dominant strategy of the players .
Revised:  In this case, the mechanism leads to incentive compatible actions of agent for each time slot .
Edit intent: clarity
Original:  We develop a connection to learning functions which are " locally stable ", and propose new regularization terms for training deep neural networks that are stable against a class of local perturbations.
Revised:  We develop a connection to learning functions which are " locally stable deep neural networks that are stable against a class of local perturbations.
Edit intent: coherence
Original:  These regularizers enable us to train a network to state-of-the-art robust accuracy of 70\% on CIFAR-10 against a PGD adversary using \ell_\infty perturbations of size \epsilon = 8/ 255.
Revised:  These regularizers enable us to train a network to state-of-the-art robust accuracy of 70\% on CIFAR-10 against an adaptive PGD attack using \ell_\infty perturbations of size \epsilon = 8/ 255.
Edit intent: clarity
Original: Research in human-centered AI has shown the benefits of machine-learning systems that can explain their predictions.
Revised: Research in human-centered AI has shown the benefits of systems that can explain their predictions.
Edit intent: clarity
Original:  Methods that allow users to tune a model in response to the explanations are similarly useful.
Revised:  Methods that allow humans to tune a model in response to the explanations are similarly useful.
Edit intent: clarity
Original:  While both capabilities are well-developed for transparent learning models (e.g., linear models and GA2Ms), and recent techniques (e.g., LIME and SHAP) can generate explanations for opaque models, no method currently exists for tuning of opaque models in response to explanations  .
Revised:  While both capabilities are well-developed for transparent learning models (e.g., linear models and GA2Ms), and recent techniques (e.g., LIME and SHAP) can generate explanations for opaque models, no method for tuning opaque models in response to explanations .
Edit intent: clarity
Original:  We  apply our framework to Semantic Sanity, a neural recommender system for scientific papers , and report on a detailed user study , showing that our framework leads to significantly higher perceived user control, trust, and satisfaction  .
Revised:  We apply our framework to Semantic Sanity, a neural recommender system for scientific papers , and report on a user study showing that our framework leads to significantly higher perceived user control, trust, and satisfaction .
Edit intent: clarity
Original: An objective understanding of media depictions, such as about inclusive portrayals of how much someone is heard and seen on screen  in film and television, requires the machines to discern automatically who, when, how  and where someone is talking .
Revised: An objective understanding of media depictions, such as inclusive portrayals of how much someone is heard and seen on screen in film and television, requires the machines to discern automatically who, when, how and where someone is talking .
Edit intent: clarity
Original: An objective understanding of media depictions, such as about inclusive portrayals of how much someone is heard and seen on screen  in film and television, requires the machines to discern automatically who, when, how  and where someone is talking .
Revised: An objective understanding of media depictions, such as about inclusive portrayals of how much someone is heard and seen on screen such as in film and television, requires the machines to discern automatically who, when, how and where someone is talking .
Edit intent: clarity
Original: An objective understanding of media depictions, such as about inclusive portrayals of how much someone is heard and seen on screen  in film and television, requires the machines to discern automatically who, when, how  and where someone is talking .
Revised: An objective understanding of media depictions, such as about inclusive portrayals of how much someone is heard and seen on screen in film and television, requires the machines to discern automatically who, when, how , and where someone is talking .
Edit intent: fluency
Original:  Interestingly , they can solve multiple tasks at once.
Revised:  In some specific situations , they can solve multiple tasks at once.
Edit intent: clarity
Original:  We evaluate it on a simulated parametrized planar arm (10-dimensional search space;
Revised:  We evaluate it on a simulated parameterized planar arm (10-dimensional search space;
Edit intent: fluency
Original: An augmented metric space (X, d_X, f_X) is a metric space (X, d_X) equipped with a function f_X: X \to R.
Revised: An augmented metric space is a metric space (X, d_X) equipped with a function f_X: X \to R.
Edit intent: coherence
Original:  It arises commonly in practice, e.g, a point cloud X in R^d where each point x\in X has a density function value f_X(x) associated to it.
Revised:  This type of data arises commonly in practice, e.g, a point cloud X in R^d where each point x\in X has a density function value f_X(x) associated to it.
Edit intent: clarity
Original:  Such an augmented metric space  naturally gives rise to a 2-parameter filtration  .
Revised:  An augmented metric space naturally gives rise to a 2-parameter filtration .
Edit intent: coherence
Original:  We experimentally compare the two algorithms to a classical data reduction algorithm of Weiheand evaluate their combinations .
Revised:  Additionally, we experimentally compare the two algorithms to a classical data reduction algorithm of Weiheand evaluate their combinations .
Edit intent: coherence
Original:  We experimentally compare the two algorithms to a classical data reduction algorithm of Weiheand evaluate their combinations .
Revised:  We experimentally compare the two algorithms to a classical data reduction algorithm due to Weihe .
Edit intent: clarity
Original:  In contrast to classical PBS, TailorNet is easy to use and fully differentiable, which is crucial for computer vision algorithms.
Revised:  In contrast to PBS, TailorNet is easy to use and fully differentiable, which is crucial for computer vision algorithms.
Edit intent: clarity
Original:  We consider an arbitrage-free bond market under volatility uncertainty.
Revised:  The starting point is an arbitrage-free bond market under volatility uncertainty.
Edit intent: clarity
Original:  In such a setting we obtain a sublinear pricing measure for additional contracts  . Similar to the forward measure approach, we define a forward sublinear expectation to simplify the valuation of cashflows.
Revised:  Such a setting leads to a sublinear pricing measure for additional contracts . Similar to the forward measure approach, we define a forward sublinear expectation to simplify the valuation of cashflows.
Edit intent: clarity
Original:  In such a setting we obtain a sublinear pricing measure for additional contracts  . Similar to the forward measure approach, we define a forward sublinear expectation to simplify the valuation of cashflows.
Revised:  In such a setting we obtain a sublinear pricing measure for additional contracts . Similar to the forward measure approach, we define the forward sublinear expectation to simplify the valuation of cashflows.
Edit intent: fluency
Original:  In such a setting we obtain a sublinear pricing measure for additional contracts  . Similar to the forward measure approach, we define a forward sublinear expectation to simplify the valuation of cashflows.
Revised:  In such a setting we obtain a sublinear pricing measure for additional contracts . Similar to the forward measure approach, we define a forward sublinear expectation to simplify the pricing of cashflows.
Edit intent: clarity
Original:  Under the forward sublinear expectation, we obtain a robust version of the expectations hypothesis and a valuation method for bond options.
Revised:  Under the forward sublinear expectation, we obtain a robust version of the expectations hypothesis and we show how to price bond options.
Edit intent: clarity
Original:   With these tools, we derive robust pricing rules for the most common interest rate derivatives : fixed coupon bonds, floating rate notes, interest rate swaps, swaptions, caps, and floors.
Revised:  With these tools, we derive robust pricing formulas for all major interest rate derivatives : fixed coupon bonds, floating rate notes, interest rate swaps, swaptions, caps, and floors.
Edit intent: clarity
Original: We study the pricing of contracts in fixed income markets in the presence of volatility uncertainty .
Revised: In this paper, we study the pricing of contracts in fixed income markets in the presence of volatility uncertainty .
Edit intent: clarity
Original:  Under the forward sublinear expectation, we obtain a robust version of the expectations hypothesis  and we show how to price bond options .
Revised:  Under the forward sublinear expectation, we obtain a robust version of the expectations hypothesis , and we show how to price bond options .
Edit intent: fluency
Original:  Finally , we will  discuss the challenges that we face towards rigorous reversible programming , mainly from the instruction and hardware perspective.
Revised:  Finally , we will also discuss the challenges that we face towards rigorous reversible programming , mainly from the instruction and hardware perspective.
Edit intent: coherence
Original: Traditional machine instruction level reverse mode automatic differentiation (AD) faces the problem of having a space overhead that linear to time in order to trace back the computational state, which is also the source of bad time performance.
Revised: Reverse-mode automatic differentiation (AD) faces the problem of having a space overhead that linear to time in order to trace back the computational state, which is also the source of bad time performance.
Edit intent: coherence