sadat2307/MSciNLI huggingface dataset:
Domain: NeurIPS
Sentence 1: In this typical evaluation setting, if we train a set of classifiers that differ only in the extent to which their training objective functions enforce fairness, and then record their respective fairness and accuracy scores on a test set with such label bias, we see that increased fairness is achieved at the expense of accuracy (Figure 3a).
Sentence 2: because the labels are biased, we must immediately assume that the corresponding accuracy measurements are also biased.
Relationship: contrasting
Domain: NeurIPS
Sentence 1: We summarize our results in Table 5 and find that SSE-SE helps improving accuracy and BLEU scores on both dev and test sets in 10 out of 11 years from 2008 to 2018.
Sentence 2: on the last 5 years' test sets from 2014 to 2018, the transformer model with SSE-SE improves BLEU scores by 0.92 on average when compared to the baseline model without SSE-SE.
Relationship: entailment
Domain: NeurIPS
Sentence 1: We summarize our results in Table 5 and find that SSE-SE helps improving accuracy and BLEU scores on both dev and test sets in 10 out of 11 years from 2008 to 2018.
Sentence 2: in [3], the authors masked 15% of words and 10% of the time replaced the [mask] token with a random token.
Relationship: neutral
Domain: NeurIPS
Sentence 1: Our analysis then consists of two main parts where each part bounds one of the terms in the RHS above.
Sentence 2: we first prove that given the partial comparisons seen so far, we can obtain a relatively good estimation to the rank of the arrived element, and then in the second part, we show that we can typically find an unassigned position in the close proximity of this estimated rank to assign to it.
Relationship: entailment
Domain: NeurIPS
Sentence 1: Two important applications of this problem are search engines for document retrieval [L + 09, RJ05, LXQ + 07, CXL + 06, XL07] and collaborative filtering approaches to recommender systems [SLH10, SKB + 12, LY08, WRdVR08].
Sentence 2: our main result is a matching upper and lower bound for the secretary ranking problem.
Relationship: neutral
Domain: NeurIPS
Sentence 1: The total number of inversions can be approximated within a factor of 2 by the Spearman's footrule.
Sentence 2: we can write the cost of Algorithm 1 (up to a factor 2) as follows: This basically breaks the cost of the algorithm in two parts: one is the cost incurred by the estimation step and the other one is the cost of the assignment step.
Relationship: reasoning
Domain: NeurIPS
Sentence 1: However, existing matrix estimation results provide a bound on the estimation error of the whole matrix, which would be a crude upper bound for the estimation error of each single row.
Sentence 2: rEAL-Bandit includes a subroutine (called Row Enhancement) that refines the estimates in order to establish stronger row-wise guarantees that may be of independent interest (see Â§3 for details).
Relationship: reasoning
Domain: NeurIPS
Sentence 1: Notice that the F1 score is relatively stable with respect to the hyperparameter change.
Sentence 2: changing λ from 0.001 to 0.1 the performance over the dataset changes by at most 1.2% in F1 score, while changing ρinter and ρintra from 0.01 to 10, the performance changes by at most 0.6% and 2.1%, respectively.
Relationship: entailment
Domain: NeurIPS
Sentence 1: This has the following effect: when a cluster has a small number of points, it could be considered as under-sampled, hence, to generalize better to test data, we need to have a better separation from other clusters, i.e., larger margin.
Sentence 2: for a cluster with a large number of points, the margin could be smaller as the chance of changing the distances between and within clusters by adding more samples to it would be low.
Relationship: contrasting
Domain: NeurIPS
Sentence 1: To address the problem, [50,52] try to learn a combination of different criteria, i.e., weights of a mixture of submodular functions.
Sentence 2: chengguang Xu would like to thank Dat Huynh and Zwe Naing for their help and advice with some of the implementations during his research assistantship at MCADS lab, which resulted in this work.
Relationship: neutral
Domain: NeurIPS
Sentence 1: We draw data from two distribution families, the Normal family with mean a and variance b 2 , and the log-Normal family, with log-mean a log and log-variance b 2 log , under multiple parameter settings.
Sentence 2: we consider the impact of shifting the distribution location over [âˆ’40.0, 40.0], with small and large variance settings.
Relationship: entailment
Domain: NeurIPS
Sentence 1: While this approach lets us deal with unbounded losses, naturally the statistical error guarantees are only as good as the confidence intervals available for the empirical mean deviations.
Sentence 2: strong assumptions on all of the moments of the loss are essentially unavoidable using the traditional tools espoused by Bégin et al. [3], which means the “heavy-tailed” regime cannot be handled, where all we assume is that a few higher-order moments are finite (say finite variance and/or finite kurtosis). 
Relationship: entailment
Domain: NeurIPS
Sentence 1: For most gradient-based NAS methods, an over-parameterized super-network is constructed firstly with all candidates paths included and one superior path is selected on each edge with the other candidates removed.
Sentence 2: signals in network often contain numerous channels during forward propagation, which means that a path is not the minimum separable structure unit in network and path-level search methods [3,24,22] limit the granularity of architecture search.
Relationship: contrasting
Domain: NeurIPS
Sentence 1: Qualitatively, we found that the estimates of feature importance provided by CXPlain were more focused on the subjectively more important semantic regions of the sample images from both MNIST and ImageNet (Figures 4 and 5; more in Appendix D).
Sentence 2: providing fast and accurate estimates of feature importance for high-dimensional data, and quantifying the uncertainty of such estimates remain open challenges.
Relationship: neutral
Domain: NeurIPS
Sentence 1: Absent any prior knowledge about the structure of the input data, multilayer perceptrons (MLPs) are likely a sensible default choice.
Sentence 2: since architectures that exploit the spatial or temporal structure of input data have been shown to be efficacious, we reason that, depending on the data modality of the input features of the model to be explained, special-purpose architectures, such as convolutional neural networks [42] for images and attentive neural networks for texts [43], could perform better than MLPs.
Relationship: contrasting
Domain: NeurIPS
Sentence 1: To address this apparent dichotomy between performance and interpretability [6], researchers have developed a number of attribution methods that provide estimates of the importance of input features towards a model's output for specific types of models [4,[7][8][9][10][11][12][13][14][15], and for any machine-learning model [6,16].
Sentence 2: providing fast and accurate feature importance estimates for any machine-learning model is challenging because there exists a wide variety of intricate machine-learning models with different underlying model structures, algorithms, and decision functions, which makes it difficult to develop an optimised and unified approach to importance attribution.
Relationship: contrasting
Domain: NeurIPS
Sentence 1: The real-time implementation of the closed-loop system can be tackled by considering a sample-and-hold implementation of (1) of the form with p(0) = p, where p is a sampled version of the state p. The most common approach consists of periodically sampling the state, selecting a stepsize small enough to ensure that the function V remains monotonically decreasing for the resulting system.
Sentence 2: the result in Theorem 3.3 links the convergence rate of the discrete-time algorithm to the Lyapunov decay and the stepsize of the state-triggered implementation of the continuous-time dynamics.
Relationship: neutral
Domain: NeurIPS
Sentence 1: Note that for any fixed query x, the probability (with respect to the random draw of v i ) that x is in C i is polynomially small in d. We now define the wall W as follows: it is equal to the function h outside of the correlation cones and the ball of radius , and it is extended by convexity to the rest of the unit ball.
Sentence 2: near-optimal convergence for such methods under Lipschitz bounds on the k-th derivatives were recently given by [Gasnikov et al., 2018 (and follow from our framework).
Relationship: neutral
Domain: NeurIPS
Sentence 1: Regression with interaction terms has been studied in the statistics community.
Sentence 2: many existing results consider under the assumption of strong/weak hierarchical (SH/WH) structure: the coefficient of the interaction term x j1 x j2 is non-zero only when both coefficients of x j1 and x j2 are (or at least one of them is) non-zero.
Relationship: contrasting
Domain: NeurIPS
Sentence 1: More recently, dynamic dual embedding (DDE) explored a primal-dual view of MLE [15,16], while Stein implicit learning (SIL) [46,41] and kernel score estimation [60] match the landscape of the potential with that of kernel-smoothed empirical observations.
Sentence 2: these approaches are susceptible to poor scalability (SM, MCMC-MLE), biased estimation (CD), and computational (DDE, SIL) and statistical (NCE) efficiency issues.
Relationship: contrasting
Domain: NeurIPS
Sentence 1: Our approach generalizes many existing algorithms (see Table 2), and makes possible new algorithms with more flexibility and tighter handling of non-linear rate constraints.
Sentence 2: we give a new method (Algorithm 2) that can handle a wider range of performance metrics than previous surrogate methods (such as e.g. KL-divergence based metrics that only take inputs from a restricted range), and can be applied to constrained training problems without the risk of over-constraining the model because it needs to use surrogates less. 
Relationship: entailment
Domain: NeurIPS
Sentence 1: [33] handle constraints that are sums-of-ratios, but do so by solving a large number of linearly constrained sub-problems, with the number of sub-problems growing exponentially with the number of rates.
Sentence 2: each player can do Best Response (BR), Online Gradient Descent (OGD) or Follow-The-Leader (FTL), and the game is zero-sum (ZS) or not.
Relationship: neutral
Domain: NeurIPS
Sentence 1: The good empirical performance of distributional reinforcement learning (Bellemare et al., 2017) has also been attributed to representation learning effects, with recent visualizations supporting this claim (Such et al., 2019).
Sentence 2: while there is now conclusive empirical evidence of the usefulness of auxiliary tasks, their design and justification remain on the whole ad-hoc.
Relationship: contrasting
Domain: NeurIPS
Sentence 1: A recent line of work [15,5,26,4] has developed variants of differential privacy which permit tighter analyses of privacy loss over multiple releases of statistics as compared to both pure and approximate differential privacy.
Sentence 2: the notion of concentrated differential privacy (CDP) [15,5] has a simple and tight composition theorem for analyzing how privacy degrades over many releases while accommodating most of the key algorithms in the differential privacy literature, including addition of Gaussian noise calibrated to global sensitivity.
Relationship: entailment
Domain: NeurIPS
Sentence 1: We distinguish between two types of average potentials: the expected potential or true potential: and the average potential or empirical potential: The ultimate goal of the boosting algorithm is to minimize the expected potential, which determines the true error rate.
Sentence 2: most boosting algorithms, including XGBoost and LightGBM, focus on minimizing the empirical potential b (S T ), and rely on the limited capacity of the weak rules to guarantee that the true potential is also small.
Relationship: contrasting
Domain: NeurIPS
Sentence 1: By Lemma 4, since noise is additive, it is sufficient to construct confidence sets on Y a = Î¸ a. Lemma 5.
Sentence 2: if (1) is satisfied with reasonable values for Î“ and , a large expected regret on the left-hand side would imply that the right-hand side must be large as well, meaning that the agent should gain a lot of information about the environment.
Relationship: neutral
Domain: NeurIPS
Sentence 1: As mentioned in [36], there are two disadvantages for median-of-means approach: (a) it involves an additional tuning parameter; (b) it is numerically unstable for small sample size.
Sentence 2: we identify a class of heavy-tailed bandits (sub-Weibull bandit) where mean estimators can still achieve regret bounds of the same order as those under sub-Gaussian reward distributions.
Relationship: contrasting
Domain: NeurIPS
Sentence 1: Another way to address data scarcity is to synthesize additional training examples, for example by using off-the-shelf Generative Adversarial Networks (GANs) [3,13].
Sentence 2: classifiers trained from GAN-generated images are typically inferior to those trained with real images, possibly because the distribution of generated images may be biased towards frequent patterns (modes) of the original image distribution [26].
Relationship: contrasting
Domain: NeurIPS
Sentence 1: This is different from the neural network literature [45,25], where adversarial training usually provides better LRTE and significantly better test error than methods providing provable robustness guarantees.
Sentence 2: our upper bound on the robust loss is tight and tractable and thus adversarial training should not be used as it provides only a lower bound and minimization of an upper bound makes more sense than minimization of a lower bound.
Relationship: contrasting
Domain: NeurIPS
Sentence 1: This cannot be guaranteed with robust splits without pruning since the tree construction process is greedy, and some training examples are also influenced by splits at different branches.
Sentence 2: in order to control the upper bound on the robust loss globally over the whole tree as in (8), and not just for the current subtree as in ( 9), we need a post-hoc approach that takes into account the structure of the whole tree.
Relationship: reasoning
Domain: NeurIPS
Sentence 1: Various algorithms to solve the IRL problem have been proposed including linear programming Ng & Russel (2000), Hybrid IRL Neu & SzepesvÃ¡ri (2007), Maximum Margin Planning Ratliff et al.  (2006), Multiplicative Weights for Apprenticeship Learning Syed et al. (2008) and Bayesian estimation Ramachandran & Amir (2007).
Sentence 2: the problem of inverse reinforcement learning can be viewed as a one class support vector machine (or as a two class support vector machine with the origin as the negative class) problem in this regime.
Relationship: neutral
Domain: NeurIPS
Sentence 1: As output, weights for an observer function with a descriptive latent space are learned.
Sentence 2: recent breakthroughs have demonstrated capable computational methods for both controlling (Heess et al.  [2017], Schulman et al. [2017], Lillicrap et al. [2015]) and designing (Ha et al. [2017], Spielberg et al., Wampler and Popovic [2009]) rigid robots.
Relationship: neutral
Domain: Networks
Sentence 1: From the traces of the mesh STAs, it has been observed that, in case of inter­ference between 20 MHz and 40 MHz channels (we call this as asymmetric channel interference ) at STA-2, the packets transmitted through the 40 MHz channel gets su.ered.
Sentence 2: sTA-2 can decode the over­heard control packets from STA-3, however fails to de­code the packets from STA-1 in case of an asymmetric channel interference.
Relationship: reasoning
Domain: Networks
Sentence 1: At time 2, when the new subscription f3 occurs, RMCEP-Online is invoked again, which in turn calls RMCEP-Aprx with the subscriptions f2 (since e is not yet placed to satisfy f2) and f3.
Sentence 2: in each round of the algorithm, the chunk with the minimum AOC is selected, and added to the .nal solu­tion.
Relationship: neutral
Domain: Networks
Sentence 1: On the other hand, the number of RSUs do not have a significant direct impact on the service provider s cost, and hence the provider can install or rent a much smaller number of RSUs placed suitably around the city to achieve the same cost.
Sentence 2: note that sufficient number of RSUs are still needed to be suitably placed as all subscriptions and events are also reported to the service provider through the RSUs.
Relationship: contrasting
Domain: Networks
Sentence 1: NetRadar [2] collects traces from mobile devices to acquire localized link throughput statistics; OpenCellID [5] collects cell tower information and correlates them with user locations to establish network density and coverage maps; and finally, OpenSignal [12] measures the signal strength of cell towers to estimate achievable throughputs.
Sentence 2: none of the current approaches yet takes a holistic view along all operators and protocol levels to enable advanced modelling, e.g. through spatial statistics and machine learning based filtering methods, to build highly relevant coverage and performance estimates.
Relationship: contrasting
Domain: Networks
Sentence 1: As mentioned in section 4.2, when the number of ter­minals is one, the parallel transmission case decreases be­cause the number of collision among WLANs decreases by coexisting EIFS and DIFS.
Sentence 2: if the number of ter­minals is larger than two, the number of collision in same WLAN decreases and the one among WLANs increases.
Relationship: contrasting
Domain: Networks
Sentence 1: In [6][7], they provide simple closed-form expressions for coverage probability, which is the probability that a randomly deployed UE can achieve a target SINR in heterogeneous network with SBSs.
Sentence 2: this result implies that the number of hidden nodes is proportional to intensity .of SBSs Figure 6: CDF of the number of hidden nodes (without consideration of UE's location) Figure 7: CDF of the number of hidden nodes (with consideration of UE s location) 5.
Relationship: neutral
Domain: Networks
Sentence 1: In the 0-1 knapsack problem, we are replacing the variables v with p and w with b to avoid confusion with our defined variables.
Sentence 2: our instance of 0-1 knapsack is defined as following: there are n items xl such that l ∈ [1, n] and xl = 1 if the item is chosen and 0 otherwise.
Relationship: reasoning
Domain: Networks
Sentence 1: During our implementation, we faced a problem related to the fact that the LTE model in OPNET (which follows the 3GPP spec­ifications for single frequency networks) considers these settings to be static during the entire running time of a simulation.
Sentence 2: the LTE configuration node cannot be adjusted dynamically, and its initial settings (including the MCS assigned for each MBMS bearer) cannot be changed after the deployment phase.
Relationship: entailment
Domain: Networks
Sentence 1: The priorities are considered in regards to both the sites corresponding to each distributed active participant, and also the cameras within each site.
Sentence 2: hosseini et al [4, 3] adopted priority-based approaches towards the study of e.ciently transmitting, rendering, and displaying bulky 3D gaming information to power-limited devices given the importance of di.erent 3D objects in the gaming context.
Relationship: neutral
Domain: Networks
Sentence 1: As can be seen, the larger value of Rmax results in more bandwidth reduction, and thus quality sacrifices, for streams in C22 (corresponding to the highest priority class), while preserving more of the full-bandwidth streams in C11 (corresponding to the least priority class).
Sentence 2: our framework does provide quality degradation in general, but considering the bandwidth savings achieved using our adaptations, it is reasonable to believe that teleimmersive users would make this sacrifice in quality in ex­change for respecting their view and the network bandwidth constraints.
Relationship: neutral
Domain: Networks
Sentence 1: This strategy has the downsides that, when a video streaming is aborted (for instance to start streaming a new video), it is often the case that the hottest HTTP/1.1 connection is aborted to stop receiving the next video segment, in which case the next video streaming will start with a cold TCP connection and will not benefit of any prior warm up. 
Sentence 2: with HTTP/2, this case no longer happens and the streaming of a first video will benefit to the next one.
Relationship: contrasting
Domain: Networks
Sentence 1: In some sense, Halma actually augments amplitude modulation on legacy ZigBee by lever­aging the symbol amplitude variation naturally provided by negligible compared with the switching period in Halma (8 samples or 4 Âµs for ZigBee).
Sentence 2: halma's fine­grained, sub-symbol-level antenna switching mechanism is feasible in practice.
Relationship: reasoning
Domain: Networks
Sentence 1: We observe that the modulated waveform of any chip se­quence is made from 4 elementary patterns, corresponding to 32 complex samples (Figure 6 shows two of such patterns).
Sentence 2: halma employs an adap­tive antenna hopping (AAH) protocol that efficiently selects the subset of antennas to optimize this tradeo., based on a model-driven framework instrumented by channel pro.le measurement.
Relationship: neutral
Domain: Networks
Sentence 1: We placed 4 DEE R loggers, complete with modified collar, on free­ranging horses in a fenced area of maximum length 150 m. This allowed us to test the effect of the animal body on contact detec­tion.
Sentence 2: since animals were free to range, we could test the effect of neither distance from ground nor relative horse body po­sitions on contact detection.
Relationship: contrasting
Domain: Networks
Sentence 1: Figure 2 depicts how HTTPS requests are processed by Akamai: each PoP has N custom certificates installed for N virtual hosts, and each certificate is served on a dedicated IP address. 
Sentence 2: if a client connects to IP 1, it will be given the certificate for a.com, whereas if it connects to IP 2, it will be given the certificate for *.z.com.
Relationship: reasoning
Domain: Networks
Sentence 1: As we observe from the plot, when apps react to firewall-timer, and when firewall-timer is set to a high value, it substantially reduces signaling per minute for most of the apps.
Sentence 2: for apps having long-lived background connections such as Skype and Facebook, a high value of firewall timeout reduces the signaling overhead by more than a factor of 4.
Relationship: entailment
Domain: Networks
Sentence 1: Real-time video surveillance systems are essential in modern 2D environments has been studied in [1, 2].
Sentence 2: in this prototype, the model of PTZ cameras is Compro IP540/IP570[6], which supports the maximum resolution of 1280 by 1024 and provides flexible configurations (e.g., 340-degree pan, 100-degree tilt, and 12x optical zoom).
Relationship: neutral
Domain: Networks
Sentence 1: The ADMP401 offers a mostly flat bandwidth, i.e., -3 dB roll o., between 100 Hz and 15 kHz, omnidirectional sensitivity pattern, and requires a supply voltage between 1.5 V and 3.3 V DC.
Sentence 2: as a proof of concept, we design two prototypes that im­plement the U-Wear framework and operate in the near­ultrasonic frequency range, i.e., 17 - 22 kHz, using COTS speakers and microphones.
Relationship: neutral
Domain: Networks
Sentence 1: To address the challenge, we design strategies to filter out color intensity change associated with interfering factors, and adapt our strategy to the current scene type.
Sentence 2: for the static scene, we leverage an audio beamforming algorithm [33, 35] to minimize the impact of interfering factors and extract the desired color inten­sity change encoded with data; for the dynamic scene, we identify patterns caused by the encoded change.
Relationship: entailment