sadat2307/MSciNLI huggingface dataset:
Domain: NeurIPS
Sentence 1: Taking the union bound over the B bundles, and noting that 2 Ã— 0.073 2 > 0.01, we find that we can classify all of the bundles correctly with probability approaching one when t mul = (100 log B)(1 + o(1)), so that the total number of tests used is t mul B = (100B log B)(1 + o(1)).
Sentence 2: this leads to the SSS algorithm described in Algorithm 3.
Relationship: neutral
Domain: NeurIPS
Sentence 1: The utility of using data-dependent priors to control disintegrated mutual information depends on the balance of two effects: On the one hand, I(W;S) ≤ I(W;S|F), and so conditioning never improves a theoretical bound and may make it looser.
Sentence 2: i(W ; S) depends on the unknown data distribution and so distribution-independent bounds will often be very loose.
Relationship: contrasting
Domain: NeurIPS
Sentence 1: It is a 10 frequency signal, where the frequencies at each sampling time-instant are generated uniformly at random in the audible frequency range 20 Hz to 20 kHz, and where the amplitudes Ar(t) are generated uniformly in the range [−180, 180];
Sentence 2: in each trial among a total of N , at every sampling instant an independent realization of the sound-level signal is used.
Relationship: reasoning
Domain: NeurIPS
Sentence 1: In this case, an integration model will be selected so that multiple cues can be weighed and combined together to form a unified estimate of head direction of self-motion [7,8].
Sentence 2: when we wear a goggle to navigate in a virtual reality world while sitting on a spinning chair, the visual and the vestibular signals would be quite discordant and it would be wrong to integrate them during inference [9].
Relationship: contrasting
Domain: NeurIPS
Sentence 1: As such, we construct an error predictor (EP) and an iterative error correction machine (EC machine) as shown in Figure 2.
Sentence 2: we freeze the autoencoder-decoder from the previous stage and reuse them to generate the states of the input nodes h k , k = 0, ..., K and output nodes h t , t = 0, ..., T .
Relationship: entailment
Domain: NeurIPS
Sentence 1: The rest four methods (SCML, DRML, SGDINC, and our M-FLRML) claim that there is no need for SVD preprocessing, which are compared using the original data matrices as input.
Sentence 2: since the SVD calculation for datasets M10-100 and M40-100 has exceeded the memory limit of common PCs, only these four methods are tested on these two datasets.
Relationship: entailment
Domain: NeurIPS
Sentence 1: In addition to these generalizations, there is good reason to suspect the results to extend to much broader class of models and optimization procedures.
Sentence 2: a wealth of recent literature suggests that the mean field theory governing the wide network limit of fully-connected models [32,33] extends naturally to residual networks [35], CNNs [34], RNNs [39], batch normalization [40], and to broad architectures [37].
Relationship: entailment
Domain: NeurIPS
Sentence 1: VAEs with non Euclidean latent space have been recently introduced, such as Davidson et al. (2018) making use of hyperspherical geometry and Falorsi et al. (2018) endowing the latent space with a SO(3) group structure. 
Sentence 2: gradients rµz can straightforwardly be computed thanks to the exponential map reparametrisation (Eq 3), and gradients w.r.t. the dispersion rz are readily available for the wrapped normal.
Relationship: neutral
Domain: NeurIPS
Sentence 1: Also we noticed that tokens which are generated first are often uninformative (e.g., punctuation, determiners, etc.).
Sentence 2: wu et al. [7] suggest that left-to-right NMT models fit better for right-branching languages (e.g., English) and right-to-left NMT models fit better for left-branching languages (e.g., Japanese).
Relationship: neutral
Domain: NeurIPS
Sentence 1: While in our model positional encodings of most of the tokens change after each insertion operation and, therefore, decoder self-attention is re-applied at each generation step, the model by Gu et al. [22] does not need this and has better theoretical time complexity of O(len(Y )2) in contrast to our O(len(Y )3).
Sentence 2: in practice our decoding is on average only 50% times slower than the baseline; for the details, see Section 5.2.
Relationship: contrasting
Domain: NeurIPS
Sentence 1: When the feature of a person, who does not wear glasses, is translated along this direction, the new feature may correspond to the same person but with glasses (The new image can be explicitly reconstructed using proper algorithms as shown in [9]).
Sentence 2: by searching for many such semantic directions, we can effectively augment the training set in a way complementary to traditional data augmenting techniques.
Relationship: reasoning
Domain: NeurIPS
Sentence 1: In this paper, we take a first step towards resolving the above difficulty by developing a first-order algorithmic framework for tackling a class of Wasserstein distance-based distributionally robust logistic regression (DRLR) problem.
Sentence 2: we propose a novel linearized proximal ADMM to solve the DRLR problem, whose objective is convex but consists of a smooth term plus two non-separable non-smooth terms.
Relationship: entailment
Domain: NeurIPS
Sentence 1: On the numerical side, we demonstrate via extensive experiments that our proposed method can be sped up substantially by adopting a geometrically increasing step size strategy.
Sentence 2: our method can achieve a hundred-fold speedup over the standard solver (which is the only other method that has been used so far to solve (1.1)) on both synthetic and real-world datasets without the need to tune an optimal penalty parameter in every iteration.
Relationship: entailment
Domain: NeurIPS
Sentence 1: We find that the complexity of our model can be reduced by using the same CNN with shared weights for both image and part patches, but the zero-shot learning performance is slightly degraded, e.g., the score for CUB-PS and AWA-PS decreases by 3.6%, 2.7%, respectively.
Sentence 2: the heavy involvement of human labor for part annotations makes tasks costly in the real large-scale problems.
Relationship: neutral
Domain: NeurIPS
Sentence 1: However, when neural network function approximation is used, the effect of optimistic initialisation can disappear quickly with optimisation (Osband et al., 2018).
Sentence 2: with non-orthogonal state-action embeddings, Q value estimates may decrease for yet unseen state-action pairs, and estimates for different state-action states can move in tandem.
Relationship: entailment
Domain: NeurIPS
Sentence 1: However, when neural network function approximation is used, the effect of optimistic initialisation can disappear quickly with optimisation (Osband et al., 2018).
Sentence 2: to gain a better insight into the value prediction step, we examine its idealised implementation: Suppose we have access to a belief over MDPs, P T (as in PSRL), and want to compute the implied distribution P QÏ€ for a single policy Ï€.
Relationship: neutral
Domain: NeurIPS
Sentence 1: For UBE, this is a consequence of proposition 1, and the similarly poor behaviour of BDQN suggests it may suffer from an analogous issue.
Sentence 2: a family of methods called Randomised Value Functions (RVF; Osband et al., 2016b) attempt to overcome these issues by directly modelling a distribution over Q functions, P Q, instead of over MDPs, P T .
Relationship: neutral
Domain: NeurIPS
Sentence 1: Examples of correlations are even found in stateless decision-making problems, such as multi-armed bandits, where prominent patterns in the reward mechanisms of different arms can translate into correlated action choices of the operating agent [7,9].
Sentence 2: these statistical relationships become more pronounced in the case of contextual bandits, where effective decision-making strategies not only exhibit temporal correlation but also take into account the state context at each time point, introducing a second source of correlation [12].
Relationship: contrasting
Domain: NeurIPS
Sentence 1: Indeed, when deploying interventions in the open world (especially in sensitive domains impacting life and death like the ones that motivate this work), care must be taken to ensure that algorithms do not discriminate among people with respect to protected characteristics such as race, ethnicity, disability, etc.
Sentence 2: we need to ensure that independently of their group, individuals have a high chance of being covered, a notion we refer to as group fairness.
Relationship: entailment
Domain: NeurIPS
Sentence 1: We use a bounding volume hierarchy for collision detection [27], and non-rigid impact zones [12] to compute the collision response.
Sentence 2: we solve a cubic equation to detect the collision time t of each vertex-face or edge-edge pair that is sufficiently close to contact: where x k and v k (k = 1, 2, 3) are the relative position and velocity to the first vertex.
Relationship: entailment
Domain: NeurIPS
Sentence 1: Somewhat analogously, in the sequential machine learning setting, modern machine learning methods, such as artificial neural networks, can be drastically disrupted when presented with new information from different domains, which leads to catastrophic interference and forgetting [19,14].
Sentence 2: to retain long-term memory (for both human and machine learners), it is crucial to devise teaching strategies that adapt to the underlying forgetting mechanisms of the learner.
Relationship: reasoning
Domain: NeurIPS
Sentence 1: With a lower memory footprint, our method allows training with larger batches in each iteration and better utilization of available GPU resources, making it more practical and economical to deploy and train very deep architectures in production environments.
Sentence 2: training with fewer features per layer leads to much lower accuracy, while the naive approximation baseline simply fails, highlighting the importance of preventing accumulation of errors across layers using our approach.
Relationship: neutral
Domain: NeurIPS
Sentence 1: To examine the reason behind the robustness of our method, Fig. 4 visualizes the error in the final parameter gradients used to update the model.
Sentence 2: we take two models for CIFAR-100-at the start and end of training-and then compute gradients for a 100 batches with respect to the convolution kernels of all layers exactly, and using our approximate strategy.
Relationship: entailment
Domain: NeurIPS
Sentence 1: In our experiments, we use quantization to K-bit fixed point representations as a simple and computationally-inexpensive approximation strategy to validate our method.
Sentence 2: experiments on CIFAR-10, CIFAR-100, and ImageNet show that our method yields performance close to exact training, while storing activations compactly with as low as 4-bit precision.
Relationship: neutral
Domain: NeurIPS
Sentence 1: A number of works focus on reducing the memory footprint of a model during inference, e.g., by compression [7] and quantization [11], to ensure that it can be deployed on resource-limited mobile devices, while still delivering reasonable accuracy.
Sentence 2: these methods do not address the significant amount of memory required to train the networks themselves (note [7,11] require storing full-precision activations during training), which is significantly larger than what is needed for inference.
Relationship: contrasting
Domain: NeurIPS
Sentence 1: A major obstacle to using approximate activations for training is that, with the standard implementation of back-propagation (backprop), approximation errors compound across the forward and then backward pass through all layers.
Sentence 2: we need to store all intermediate activations during training because they are needed to compute gradients during back-propagation: ( 5)-( 8) involve not just the values of the incoming gradient, but also the values of the activations themselves.
Relationship: neutral
Domain: NeurIPS
Sentence 1: For max-pooling, exact back-propagation through the pooling operation would require storing the arg-max indices (the number of bits required to store these would depend on the max-pool receptive field size).
Sentence 2: since max-pool layers are used less often in recent architectures in favor of learned downsampling (ResNet architectures for image classification use max-pooling only in one layer), we instead choose not to approximate layers with max-pooling for simplicity.
Relationship: contrasting
Domain: NeurIPS
Sentence 1: When the goal is to just compute the final output of the network, the activations of an intermediate layer can be discarded during the forward pass as soon as we finish processing the subsequent layer or layers that use it as input.
Sentence 2: we need to store all intermediate activations during training because they are needed to compute gradients during back-propagation: ( 5)-( 8) involve not just the values of the incoming gradient, but also the values of the activations themselves.
Relationship: contrasting
Domain: NeurIPS
Sentence 1: This in-turn can lead to the computation being inefficient and "memory-bound": it forces the use of smaller training batches for each GPU leading to under-utilization of available GPU cores (smaller batches also complicate the use of batch-normalization [12] with batch statistics computed over fewer samples).
Sentence 2: practitioners are forced to either use a larger number of GPUs for parallelism, or contend with slower training.
Relationship: reasoning
Domain: NeurIPS
Sentence 1: Domain adaptation (DA) solves this problem by building a model utilizing the knowledge of label-rich dataset, i.e., source domain, which generalizes well on the label-scarce dataset, i.e., target domain.
Sentence 2: it means that inference of pseudo labels is easily influenced by imbalance distribution of samples in different classes where certain classes would dominate the process of self-training and cause errors accumulation.
Relationship: neutral
Domain: NeurIPS
Sentence 1: For our setup with d = 100, while both Decoupled DEEP-C and Sparse DEEP-C perform similar in average regret, we find that Sparse DEEP-C significantly outperforms Decoupled DEEP-C in standard deviation and in 95th-percentile.
Sentence 2: 95th-percentile of Sparse DEEP-C is 33% lower than that under Decoupled DEEP-C.
Relationship: entailment
Domain: NeurIPS
Sentence 1: However, there are some crucial differences.
Sentence 2: these works assume (a) a discrete set of arms, (b) the existence of a sequence of graphs indexed by time (possibly fixed) with the arms Contextual Non-parametric residuals Binary feedback Kleinberg and Leighton (2003) X X as its nodes, (c) the action involves pulling an arm, and at each time the reward at each neighbor of the pulled arm is revealed.
Relationship: entailment
Domain: NeurIPS
Sentence 1: Motivated by the application of real-time pricing in e-commerce platforms, we consider the problem of revenue-maximization in a setting where the seller can leverage contextual information describing the customer's history and the product's type to predict her valuation of the product.
Sentence 2: her true valuation is unobservable to the seller, only binary outcome in the form of success-failure of a transaction is observed.
Relationship: contrasting
Domain: NeurIPS
Sentence 1: The MNL contextual bandit is a multinomial generalization of generalized linear contextual bandits [23,30], particularly logistic bandits, that reduces to generalized linear bandits when the assortment contains a single item.
Sentence 2: this extension is non-trivial since the MNL model cannot be expressed in the form of a generalized linear model [15]; hence, the results of generalized linear bandits do not directly apply.
Relationship: contrasting
Domain: NeurIPS
Sentence 1: Aside from extending the analysis of this approach to population loss, we show that it can lead to algorithms for private SCO that use only near-linear number of gradient evaluations (whenever these assumptions hold).
Sentence 2: we give a variant of objective perturbation in conjunction with the stochastic variance reduced gradient descent (SVRG) with only O(n log n) gradient evaluations.
Relationship: entailment
Domain: NeurIPS
Sentence 1: Several of these methods are based on the observation that if all the candidate subsequences in the Viterbi algorithm converge at some point, then all subsequent states will share a common subsequence up to that point [32,33].
Sentence 2: these methods do not guarantee reduction in latency since, in the worst case, they still need to process all the rewards before producing any output.
Relationship: contrasting
Domain: NeurIPS
Sentence 1: In particular, OSA agreed with the optimal algorithm (Viterbi) on only 58.8% of predictions under both entropy and expected classification error measures suggested in [24].
Sentence 2: just with L = 1, Peek Search matched with Viterbi on 77.4% predictions thereby outperforming OSA by an overwhelming amount (over 30%).
Relationship: contrasting
Domain: NeurIPS
Sentence 1: This is the first model selection result for contextual bandits with non-vacuous regret for all values of d m , and to the best of our knowledge is the first positive result of this type for any online learning setting with partial information.
Sentence 2: our problem of estimating E m,i clearly falls into this general setup if we uniformly explore the actions for n rounds, then set {x s } n s=1 to be the features obtained through the feature map i and {y s } n s=1 to be the observed losses.
Relationship: neutral
Domain: Networks
Sentence 1: In any case this function is run only once on each node (line 33): all pending messages are canceled (lines 36-37) and the decision is published (line 40) with a unique message identifier that is produced using grpId as a seed (line 39).
Sentence 2: if several nodes make a decision in the same session, all messages carrying this decision will have the same identifier and will thus be considered as du­plicates of the same message by the communication layer.
Relationship: reasoning
Domain: Networks
Sentence 1: Once an interim topology is formed, the process with the lowest id becomes the leader, and only that process takes up the responsibility of rebuilding the power-law topology.
Sentence 2: in the domain of man-made systems, if the topology of a power-law netÂ­work gets altered due to failures or adversarial attacks, then remedial actions to restore the power-law property are very important.
Relationship: neutral
Domain: Networks
Sentence 1: It is important to note that our system is designed to be transparent to already-existing streaming infrastructure in order to ensure both vendor neutrality as well as simplified deploy­ment.
Sentence 2: we do not operate our own stream source, but instead we assume that there is an already-­published stream, for instance from a Content Distribution Network (CDN), that can be played directly from its source and our customers would transparently use our system to minimize the load on that source.
Relationship: reasoning
Domain: Networks
Sentence 1: In contrast, when the original SNR was low (12 dB with minimum Data PHY rate of 385 Mbps), no link connectivity is possible irrespective of the blockage position.
Sentence 2: under human blockage, beam dilation can help maintain link connectivity, but only if the original link SNR is high, and when blockage is sufficiently far away from the transmitter/receiver, such that only part of the beam is blocked.
Relationship: reasoning
Domain: Networks
Sentence 1: The theory of optimal experiment design [2] has established a set of such objective functions.
Sentence 2: maximizing the determi­nant of FIM (aka D-optimality )leads to a design that min­imizes (a bound on) the volume of the error ellipsoid, and minimizing the trace of the inverse FIM (aka A-optimality) leads to a design that minimizes (a bound on) the average mean squared error (MSE).
Relationship: entailment
Domain: Networks
Sentence 1: Although physical systems can often be modeled and described using first principles from physics and conservation laws [1], it is fundamental to ensure that the values of the parameters in the model describing the plant be inferred from input/output data.
Sentence 2:  it is required to ensure identifiability of the corresponding dynamical systems [2]; in particular, due to the unknown nature of the parameters that we aim to determine (i.e., to identify) it is common to consider structural identifiability – two models are the same if and only if the parameters describing this model are the same [3].
Relationship: entailment
Domain: Networks
Sentence 1: The internal jammer plays a twofold role: we assume ADV has previously compromised the secret keys of a set c = {0, 8, 16, 24} of nodes of the network (with N = 32) , while retaining further jamming capabili­ties.
Sentence 2: aDV jams the transmissions with proba­bility  pA ∈ [0.1, 0.9]: the adversary firstly jams the frequencies related to the keys it is in possession of, than if it has enough jamming power, it random chooses other frequencies.
Relationship: entailment
Domain: Networks
Sentence 1: Pairs in TDBS are pre-computed during the off-line phase. 
Sentence 2:  the protocol is “static” —with respect to the network pairs— and cannot deal with the join of a new node: a new node joining the network involves a new computation of all the 1-factorizations for all the network pairs, and therefore, all the pseudo-random frequency sequences must be invalidated and re-computed.
Relationship: reasoning
Domain: Networks
Sentence 1: Experimentation in real testbed using GeoNetworking implementation can provide very precise evaluation result in the deployment phase of the implementation, however it requires heavy cost (i.e. vehicles, equipment, drivers, time, etc.).
Sentence 2: the simulation can provide the evaluation result in flexible networks with various scenarios in a low cost, however the result is based on the preconfigured model in the simulation and often diverse from the experimental evaluation in the real testbed.
Relationship: contrasting
Domain: Networks
Sentence 1: When the Wi-Fi direct transmission rate is higher than the Wi-Fi hotspot rate, then the throughput to mobile A in Figure 1(a) is maximized if only the Wi-Fi direct link is used.
Sentence 2: if the Wi-Fi hotspot rate is higher, then the selection that maximizes the throughput depends on whether the hotspot backhaul is a bottleneck.
Relationship: contrasting
Domain: Networks
Sentence 1: Context discovery can also be achieved without perform­ing ambience fingerprinting, where crowdsourcing is used for the extraction of semantic location from sensor data (e.g., audio, im­age), without any need for fingerprinting [8].
Sentence 2: this type of approach relies on heavy computer vision techniques (e.g., scene classification, optical-character-recognition, object recognition), as well as heavy natural language processing (e.g., speech recognition, sound classification).
Relationship: contrasting
Domain: Networks
Sentence 1: This can also be achieved in the context of IoT, by associating access to beacon-advertised websites (e.g., by UriBeacons [14]) to the user's MAC address.
Sentence 2: whenever a user clicks on a link that was advertised by a specific beacon, it creates an opportunity for the server to associate the user's MAC address to the IP address from which the website was accessed.
Relationship: entailment
Domain: Networks
Sentence 1: The stricter insertion rule of OPC, which only inserts chunks in sequence, reduces the DRAM accesses for insertions/evictions by roughly 40%, leading to better memory performance.
Sentence 2: nevertheless, ICN caching has not yet met these expectations, receiving criticism for its effciency [8, 9], based on the debatable performance superiority of distributed in-network caching over independent caches at the network edge, as well as on the questionable support for packet-level caching by today's hardware.
Relationship: neutral