text stringlengths 27 153 | label stringlengths 27 153 | id int64 0 40k |
|---|---|---|
RC We results in about this show main two paper. | We show two main results about RC in this paper. | 5,500 |
solution with non-Markovian This for number examines (NMRDPs). methods processes a rewards of paper decision | This paper examines a number of solution methods for decision processes with non-Markovian rewards (NMRDPs). | 5,501 |
As different temporal translations. logics a result, different and adopt they | As a result, they adopt different temporal logics and different translations. | 5,502 |
step the first filling this paper This gap. is towards | This paper is the first step towards filling this gap. | 5,503 |
by developed Systems Solving the emerged from General Problem Klir. FIR G. | FIR emerged from the General Systems Problem Solving developed by G. Klir. | 5,504 |
is on a data rather driven knowledge. on structural It than methodology based systems behavior | It is a data driven methodology based on systems behavior rather than on structural knowledge. | 5,505 |
FIR available on pattern is based from reasoning rules synthesized the data. | FIR reasoning is based on pattern rules synthesized from the available data. | 5,506 |
rule rules as possible. Sugeno base pattern much The preserves as knowledge | The Sugeno rule base preserves pattern rules knowledge as much as possible. | 5,507 |
process increased. is this In information considerably is some lost robustness but | In this process some information is lost but robustness is considerably increased. | 5,508 |
present learning mean. for We framework novel counter-harmonic using operators a morphological | We present a novel framework for learning morphological operators using counter-harmonic mean. | 5,509 |
networks. and combines neural from morphology convolutional concepts It | It combines concepts from morphology and convolutional neural networks. | 5,510 |
settings. large well and to online scales datasets It | It scales well to large datasets and online settings. | 5,511 |
probabilistic relationships its probabilistic network signs. of qualitative between variables by models the means WA | WA qualitative probabilistic network models the probabilistic relationships between its variables by means of signs. | 5,512 |
Non-monotonic ambiguous an sign. have associated influences | Non-monotonic influences have associated an ambiguous sign. | 5,513 |
upon signs These results to lead uninformative inference. ambiguous typically | These ambiguous signs typically lead to uninformative results upon inference. | 5,514 |
situational this concept we capture effect, the of sign. introduce To | To capture this effect, we introduce the concept of situational sign. | 5,515 |
Pearl's precisely, approach More with we a independent of structural-model choice Poole's combination present logic. | More precisely, we present a combination of Pearl's structural-model approach with Poole's independent choice logic. | 5,516 |
to first-order capabilities mapping actions explicit approach. structural-model This the and modeling adds also | This mapping also adds first-order modeling capabilities and explicit actions to the structural-model approach. | 5,517 |
recent the microbial We when a re-analyzing data observe experiment. from patterns evolution same | We observe the same patterns when re-analyzing data from a recent microbial evolution experiment. | 5,518 |
is histogram words generated. each visual of For subwindow, a | For each subwindow, a histogram of visual words is generated. | 5,519 |
Markov we hidden field. Gaussian mixture to Then it model-based random generalize | Then we generalize it to Gaussian mixture model-based hidden Markov random field. | 5,520 |
supporting and in Scotland have applications many our We and theory. UK Sweden done | We have done many applications in UK and Scotland and Sweden supporting our theory. | 5,521 |
For both only estimators and data. from provides death methodology cases both population our | For both cases our methodology provides both estimators from only death and population data. | 5,522 |
advantages The of our method straightforward. are | The advantages of our method are straightforward. | 5,523 |
the data not do make need We survey to calculations. | We do not need survey data to make the calculations. | 5,524 |
survey and test based improve methodologies. should The the estimates resulting to used be existing | The resulting estimates should be used to test and improve the existing survey based methodologies. | 5,525 |
paper, shape current-day this of we identify In techniques. some matching of limitations the | In this paper, we identify some of the limitations of current-day shape matching techniques. | 5,526 |
any shape be that easily can method other algorithm. our augmented matching with We show | We show that our method can easily be augmented with any other shape matching algorithm. | 5,527 |
structural incorporation will Greater information of data into accuracy learning. prior on require | Greater accuracy will require incorporation of prior structural information on data into learning. | 5,528 |
of set the graph) indices. structure on | graph) structure on the set of indices. | 5,529 |
letter Breen a In al. et recent Nature, to | In a recent letter to Nature, Breen et al. | 5,530 |
et epistasis, ratio al. when absence calculating the dN/dS Breen However, in expected of the | However, when calculating the expected dN/dS ratio in the absence of epistasis, Breen et al. | 5,531 |
genes and all in nuclear the for Furthermore, et al. Breen chloroplast | Furthermore, for all nuclear and chloroplast genes in the Breen et al. | 5,532 |
a time-consuming Yet procedure. this may become | Yet this may become a time-consuming procedure. | 5,533 |
real results validates paper proposed demonstrates the The data. and framework simulated on data on | The paper validates the proposed framework on simulated data and demonstrates results on real data. | 5,534 |
Traditionally, this photographs is pattern of a aligned from contains elements. measured which distortion flat | Traditionally, this distortion is measured from photographs of a flat pattern which contains aligned elements. | 5,535 |
measurable attainable precisions. the limits This fact | This fact limits the attainable measurable precisions. | 5,536 |
is the natural once, at thing and, process investigate. complex we This a | This is a natural process and, at once, the complex thing we investigate. | 5,537 |
of article. of Developing this destination such the is a model | Developing of such a model is the destination of this article. | 5,538 |
network. a This of corresponds anatomical AL complexity with high their | This corresponds with a high anatomical complexity of their AL network. | 5,539 |
The features. all significantly clusters two five differed in | The two clusters differed significantly in all five features. | 5,540 |
in and role We problem symmetry concept investigate its the solving. of | We investigate the concept of symmetry and its role in problem solving. | 5,541 |
of the symmetry Finally problem improving in solving. concept this attempts exploit to paper | Finally this paper attempts to exploit the concept of symmetry in improving problem solving. | 5,542 |
real synthetic on validate Simulations the data provided approach. proposed and to are | Simulations on synthetic and real data are provided to validate the proposed approach. | 5,543 |
up scale reinforcement has large to learning networks. that complex tasks require yet to Neuroevolution | Neuroevolution has yet to scale up to complex reinforcement learning tasks that require large networks. | 5,544 |
video) imply directly. very search if a high space dimensional encoded raw | raw video) imply a very high dimensional search space if encoded directly. | 5,545 |
contain Because network matrices exist (i.e. there solutions whose regularity often weight | Because there often exist network solutions whose weight matrices contain regularity (i.e. | 5,546 |
sRBM and in RBM log- models Both likelihood. outperform models Ising | Both RBM and sRBM models outperform Ising models in log- likelihood. | 5,547 |
renaissance. of a We are the heart in digital | We are in the heart of a digital renaissance. | 5,548 |
we this computation. of of work, the In mathematical models automation, some review motivate use | In this work, we motivate the use of automation, review some mathematical models of computation. | 5,549 |
algorithm images. wide variety demonstrated using is recursive a The of this of statistical effectiveness | The effectiveness of this recursive statistical algorithm is demonstrated using a wide variety of images. | 5,550 |
always been Handwritten recognition has character a challenging task. | Handwritten character recognition has always been a challenging task. | 5,551 |
the rate. In implemented to fusion classifier method is paper, this a improve recognition | In this paper, a classifier fusion method is implemented to improve the recognition rate. | 5,552 |
Linear considered (LC). (KNN) the For have we fusion, classifier and neighbour classifier K-nearest | For the classifier fusion, we have considered K-nearest neighbour (KNN) and Linear classifier (LC). | 5,553 |
with streaming require An increasing applications of reasoning real-time number under uncertainty input. | An increasing number of applications require real-time reasoning under uncertainty with streaming input. | 5,554 |
framework formalism such provides a powerful applications. (dynamic) temporal The for Bayes representational net | The temporal (dynamic) Bayes net formalism provides a powerful representational framework for such applications. | 5,555 |
existing in algorithms mind. addition, not In developed real-time were processing with | In addition, existing algorithms were not developed with real-time processing in mind. | 5,556 |
not time do each expressions of that at these The pre-computed. change parts step are | The parts of these expressions that do not change at each time step are pre-computed. | 5,557 |
tractability are knowledge, the these approach. the for explicit structural-model results To first our | To our knowledge, these are the first explicit tractability results for the structural-model approach. | 5,558 |
the using state over the inference how be probabilistic We system show tracked can model. | We show how the system state can be tracked using probabilistic inference over the model. | 5,559 |
result well Theorem. the Hammersley-Clifford generalizes known This | This result generalizes the well known Hammersley-Clifford Theorem. | 5,560 |
Credal independence here as relations interpreted variables. encoding strong are networks among | Credal networks are here interpreted as encoding strong independence relations among variables. | 5,561 |
networks of based We sets separately first of theory present specified probabilities. credal a on | We first present a theory of credal networks based on separately specified sets of probabilities. | 5,562 |
inference also polytrees We that is setting. this with NP-hard show in | We also show that inference with polytrees is NP-hard in this setting. | 5,563 |
world real these to fail For hold. problems, assumptions many | For many real world problems, these assumptions fail to hold. | 5,564 |
a on relies propose two-step The system Answer-Set we and Programming approach. follows | The system we propose relies on Answer-Set Programming and follows a two-step approach. | 5,565 |
future express to temporal linear representation extends Our rewards. logic (FLTL) | Our representation extends future linear temporal logic (FLTL) to express rewards. | 5,566 |
translation Our the has method. the solution model-checking in of effect embedding | Our translation has the effect of embedding model-checking in the solution method. | 5,567 |
of scenarios lead theory is and scenario intended to to formal analysis. work This a | This work is intended to lead to a formal theory of scenarios and scenario analysis. | 5,568 |
decision A with is together functions step-strategy for actions. a strategy selection | A strategy is a step-strategy together with selection functions for decision actions. | 5,569 |
an for instantiation. any incorporating DAG a step-strategy introduce concept of the We GS-DAG: optimal | We introduce the concept of GS-DAG: a DAG incorporating an optimal step-strategy for any instantiation. | 5,570 |
address propagation of belief loopy in the question convergence We the algorithm. (LBP) | We address the question of convergence in the loopy belief propagation (LBP) algorithm. | 5,571 |
MAP. of complexity investigates the This paper | This paper investigates the complexity of MAP. | 5,572 |
show complete for We that is NP. MAP | We show that MAP is complete for NP. | 5,573 |
for also We provide based negative algorithms. elimination complexity results | We also provide negative complexity results for elimination based algorithms. | 5,574 |
MAP out when even turns Pr are It and hard MPE, that remains easy. | It turns out that MAP remains hard even when MPE, and Pr are easy. | 5,575 |
investigate best results, guaranteed with there effort algorithm approximations. approximation is no we Because | Because there is no approximation algorithm with guaranteed results, we investigate best effort approximations. | 5,576 |
MAP approximation framework. a generic introduce We | We introduce a generic MAP approximation framework. | 5,577 |
an dependence. propose efficient Bayesian in for We method functional network models inference with | We propose an efficient method for Bayesian network inference in models with functional dependence. | 5,578 |
IN TO example, FOR propagation it helps avoid large tree junction cliques. | FOR example, IN junction tree propagation it helps TO avoid large cliques. | 5,579 |
three an stratified fold iris SIFT efficient This matching for paper proposes recognition. | This paper proposes an efficient three fold stratified SIFT matching for iris recognition. | 5,580 |
conventional filter to SIFT wrongly is The objective paired matches. | The objective is to filter wrongly paired conventional SIFT matches. | 5,581 |
impairments. regions different at iris Due may to of high there some image be similarity | Due to high image similarity at different regions of iris there may be some impairments. | 5,582 |
keypoints II. detected of filtered gradient are These and Strata by finding in paired | These are detected and filtered by finding gradient of paired keypoints in Strata II. | 5,583 |
used paired III. impairments scaling the in remove to keypoints is factor of Strata Further, | Further, the scaling factor of paired keypoints is used to remove impairments in Strata III. | 5,584 |
after be pairs retained matches iris are potential Strata likely to recognition. The for III | The pairs retained after Strata III are likely to be potential matches for iris recognition. | 5,585 |
over and This iris. the for existing SIFT of improvement matching significant marks accuracy FAR | This marks significant improvement of accuracy and FAR over the existing SIFT matching for iris. | 5,586 |
set information, edge shadow and background, up The models of and are adaptively updated. | The models of background, edge information, and shadow are set up and adaptively updated. | 5,587 |
maximizing The field. is density solution posterior possibility the by the of obtained segmentation | The solution is obtained by maximizing the posterior possibility density of the segmentation field. | 5,588 |
fundamental uncertainty to combinatorial is the Robust with of in deal one optimization approaches optimization. | Robust optimization is one of the fundamental approaches to deal with uncertainty in combinatorial optimization. | 5,589 |
enlarge to and the . considerably problems to amenable effective solutions of class | and to enlarge considerably the class of problems amenable to effective solutions . | 5,590 |
approximate networks are Bayesian is algorithms so necessary. monitoring in dynamic Exact intractable, | Exact monitoring in dynamic Bayesian networks is intractable, so approximate algorithms are necessary. | 5,591 |
Bayesian scoring Global (BN) metric. a the metric network new We called (GU) introduce Uniform | We introduce a new Bayesian network (BN) scoring metric called the Global Uniform (GU) metric. | 5,592 |
based metric a particular of type This on is default prior. parameter | This metric is based on a particular type of default parameter prior. | 5,593 |
is BNs for GU form derived. classes computing closed A for of special formula | A closed form formula for computing GU for special classes of BNs is derived. | 5,594 |
computing GU an Efficiently for open remains arbitrary an problem. BN | Efficiently computing GU for an arbitrary BN remains an open problem. | 5,595 |
such definition search with space. deals the analysis This of and paper one | This paper deals with the definition and analysis of one such search space. | 5,596 |
called (IASC). then Spectral clustering derive We Approximate spectral a Incremental Clustering algorithm novel | We then derive a novel spectral clustering algorithm called Incremental Approximate Spectral Clustering (IASC). | 5,597 |
model by Penna. proposed used for biological A commonly aging was | A commonly used model for biological aging was proposed by Penna. | 5,598 |
I live have delayed find corresponding that longer. that to populations senescence younger models | I find that models corresponding to delayed senescence have younger populations that live longer. | 5,599 |
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