HHousen/ParaSCI · Datasets at Hugging Face

sentence1 stringlengths 16 446	sentence2 stringlengths 14 436
liu et al leveraged unlabeled data to enhance the feature representation capability of the network .	liu et al rely on a single network by leveraging abundantly available unlabeled crowd imagery in a learning-to-rank framework .
each of the layers is followed by a relu layer for non-linear mapping .	each convolutional layer can have an arbitrary number and size of filters , and is followed by relu .
moreover , non-commutative field theory can be seen as an effective regime of string theory .	on the other hand , open string theory on a d2-brane can be understood from the viewpoint of deformation quantization .
this random walk is assumed to be skip free in the direction to the boundary of the quadrant , but may have unbounded jumps in the opposite direction , which are referred to as upward jumps .	this random walk is assumed to be skip free toward the boundary of the quadrant but may have unbounded jumps in the opposite direction , which we call upward jumps .
infogan learns to disentangle latent representations by maximizing the mutual information between a small subset of the latent variables and the observation .	infogan uses an unsupervised approach to learn semantic features maximizing mutual information between the latent code and the generated observation .
however , gravity is a global force and poisson solvers operate on the global density field .	gravity , which is the simplest model in higher-curvature theories of gravity .
the overall architecture is similar to and input the attention-derived image features to the cell node of the lstm .	in showatttell , while the overall architecture is similar to and input the attention-derived image features to the cell node of the lstm .
if the discriminant of t is a square , then a is not dominated by the set of powers of itself .	since the discriminant is a polynomial in y , it has a finite number of roots .
the fact that the low-lying states do not have parity doublets implies that the vacuum is not invariant under the axial transformations .	that the vacuum is not invariant under the axial transformation is directly seen from the nonzero values of the quark condensates , which are order parameters for spontaneous chiral symmetry breaking .
we use the recent results of on the achievable rates of finite block-length codes to investigate the power-limited outage probability of the arq protocols .	we use the recent results of on the achievable rates of finite block-length codes to analyze the system performance .
intuitively , a foliation is a pattern of -dimensional stripes - ie , submanifolds - on m n , called the leaves of the foliation , which are locally well-behaved .	a foliation by c k leaves which is tranversely c k is called simply a c k foliation .
we use adam to adapt learning rates and improve numerical stability .	we adopt the adam optimizer for accelerated training and learning rate decay .
convolutional neural networks have made great progress in various fields , such as object classification , detection and character recognition .	deep neural networks have revolutionized many domains , eg , image recognition , speech recognition and knowledge discovery .
in this subsection , we briefly review the symplectic approach to the toric sasaki-einstein manifolds according to .	in this section , we recall known facts about toric sasaki manifolds , following .
the mitigation methods based on power-law analysis can provide rough mitigation strategies in the planning horizon , but lack accurate tactics for online operations .	the mitigation methods based on power-law analysis can only provide rough mitigation strategies in the planning horizon , lacking accurate tactics for operations .
deep convolutional neural networks have demonstrated significant improvements over traditional approaches in many pattern recognition tasks .	convolutional neural networks have been instrumental to the recent breakthroughs in computer vision .
the models are trained using the adam optimiser with the default hyperparameters in minibatches of 80 instances .	all models are trained using mle loss and optimized using adam optimizer with a batch size 256 .
the lowest-dimension unprotected multiplets in this ope correspond to uirs lying above the unitarity bound of the continuous series and are realized by quadrilinear operators .	there the operators at the unitarity bound of the continuous series of uirs are trilinear and can not appear in the ope .
hd 45677 hd 45677 is a well studied b2 star whose evolutionary status is still unclear .	hd 37903 hd 37903 is the only sightline for which we have hst data that meets our criterion as an outlier .
now we proceed onto define smarandache co-ring and smarandache iso-ring .	now we proceed on to define smarandache n-ideal rings .
we employ the proximal policy optimization for the search algorithm .	in all experiments we use the common proximal policy optimization .
domain walls can form at a spontaneous phase transition with discrete symmetry breaking in the early universe .	topological defects could be produced at a phase transition in the early universe .
the lstms are now successfully applied in several applications , such as speech-recognition .	lstms have become very successful in applications to language modeling , machine translation , and speech recognition .
deep neural networks have been widely applied and achieved state-of-art performance on a variety of tasks including image recognition .	recurrent neural networks have received renewed interest due to their recent success in various domains , including speech recognition .
now we proceed on to define polynomial birings .	now we proceed on to define smarandache zero divisors .
in the case of free fermions , a complete classification has been achieved in using such ideas as anderson localization and k-theory .	for quadratic fermionic models , a complete topological classification of the possible states has been achieved .
this is the interface of the classical world and the quantum world .	this contrasts with the world indicated by quantum mechanics .
clv of maximum contrast values measured images .	clv of median contrast values measured images .
this filtration is canonically associated to e and is called the harder-narasimhan filtration of e .	it is called the harder-narasimhan filtration of x .
to evaluate the performance of our geonet in monocular depth estimation , we take the split of eigen et al to compare with related works .	we implement the same split of eigen et al to evaluate the performance of our dfo framework with others in the single-view depth estimation task .
so it is natural to expect that this theory may be also used to describe multiple m5-branes .	on the other hand , like its counterpart , this theory may be also a light-cone description of multiple m5-branes .
specifically , works in demonstrate such a difficulty of characterising the error surface of an mlp .	specifically , works in demonstrate such a difficulty of characterising the error surface of an fnn .
the advances in convolutional neural networks have successfully pushed the limits and improved the stateof-the-art technologies of image and video understanding .	recent advances in convolutional neural nets dramatically improved the state-of-the-art in image classification .
all spectra were grouped to a minimum of 20 counts per bin and fitted using xspec .	the spectra are grouped to have a minimum of 20 net counts per bin and fitted using the xspec package .
koch a , mcwilliam a , grebel ek , zucker db , belokurov v .	shetrone md , siegel mh , cook do , bosler t .
convolutional neural networks have achieved superior performance in many visual tasks , such as object classification and detection .	deep convolutional neural networks have achieved great success in various computer vision tasks , including object classification .
deep learning has become very popular for many computer vision and image recognition tasks .	deep learning has made an enormous impact on many applications in computer vision such as generic object recognition .
convolutional neural networks have recently achieved great success on various visual recognition tasks .	convolutional neural networks have broken many records of computer vision tasks , such as image classification .
imaging and self-calibration were performed using the difmap software package .	data reduction was carried out with the miriad software package using standard procedures .
we construct the local conforming virtual element space by resorting to the so-called enhancement strategy introduced in .	we construct the local nonconforming virtual element space by resorting to the so-called enhancement strategy originally devised in .
following , we say that an induced copy of claw in g is 1-heavy if at least one of its endvertices is heavy .	following , an induced claw of g is called 2-heavy if at least two of its end vertices are heavy .
recent advances in deep neural networks have contributed to the state-of-the-art performance in various artificial intelligence -based applications such as image classification , and so forth .	designing deeper and wider convolutional neural networks has led to significant breakthroughs in many machine learning tasks , such as image classification .
as we all know , quantum correlations , such as quantum entanglement , have been proposed as the key resource present in certain quantum communication tasks and quantum computational models .	these quantum correlations are powerful resources for quantum engineering , quantum cryptography , quantum communication , and quantum information processing .
a pulsar is a rotating neutron star with a strong magnetic dipole not aligned with the rotation axis .	a pulsar is a rapidly rotating neutron star that emits highly directional radiation .
gong et al explored spatiotemporal characteristics of intra-city trips using metro scd of 5 million trips in shenzhen , china .	gong et al explored spatiotemporal characteristics of intra-city trips using metro scd on 5 million trips in shenzhen , china .
deep neural networks , together with large scale accurately annotated datasets , have achieved remarkable performance in a great many classification tasks in recent years .	convolutional neural networks have achieved tremendous progress on many pattern recognition tasks , especially large-scale images recognition problems .
controllable single photons are an important tool to study fundamental quantum mechanics and also for practical applications in quantum communication .	entangled states of photons are the basic resource in the successful implementation of quantum information processing applications , namely optical quantum computing .
thus , work in this area relies on the development of algorithms which are guided by various heuristics .	thus , work in this area relies on the development of various heurisics-based algorithms .
the theoretical foundations for cothorities already exist in the form of threshold signatures .	the theoretical foundations for cosi and witness cothorities already exist in the form of threshold signatures .
finally , as an application , we consider systems of quotient coherent sheaves .	this alternative formulation is necessary for the later application to quotient coherent sheaves .
we compare our algorithm with other 11 state-ofthe-art ones including 7 deep learning based algorithms and 4 conventional algorithms .	we compare our proposed algorithm with other 14 state-ofthe-art ones , including 10 deep learning based algorithms and 4 conventional algorithms .
the neutrinoless double beta decay is a convenient tool to test physics beyond the sm .	double beta decay is the slowest nuclear decay process observed until now in nature .
the parameters are continuously optimized by means of the expectation-maximization algorithm , seeded by k-means clustering .	the associated parameters can be calculated considering the expectation maximization algorithm .
mc-cdma is formed by ofdm and cdma combination which is multiple access and multi-carrier system .	mc-cdma is produced by combination of ofdm and cdma which is multiple access and multi-carrier systems .
moreover , scalar linear network coding over a sufficiently large finite field was shown to be sufficient to achieve the capacity of a multicast network .	randomized linear network coding schemes were shown to be sufficient in achieving the information theoretic max-flow , min-cut bound on network capacity .
the ordinate is the difference between the value obtained from linear grid interpolation of the given parameter and the true value , expressed in units of the grid step .	the ordinate is the time dependent conductance g is the time dependent current , and v the bias .
generative adversarial networks are a recently developed generative model to produce synthetic images or texts after being trained .	generative adversarial networks are a recent popular technique for learning generative models for high-dimensional unstructured data .
articles study makespan minimization assuming that an online algorithm knows the optimum makespan or the sum of the processing times of σ .	articles study makepan minimization assuming that an online algorithm knows the optimum makespan or the sum of the processing times of σ .
in the quantum theory this is the -algebra symmetry .	assume that quantum theory is a correct model of the real world then we can always find .
among them , the approaches focused on shape retrieval , and performed diffusion on image level .	among them , the approaches addressed the shape retrieval problem , and performed diffusion on image level .
recently successful methods in naturalistic environments learn representations from sequences of frames .	recently successful methods learn representations from sequences of frames .
the axion is a hypothetical elementary particle pos tulated to resolve the strong cp problem in quantum chromodynamics .	the axion is a particle that is theoretically motivated , since is the consequence of the peccei quinn solution to the strong cp problem .
to perform classification using va , we first learn the feature representations by va , and then build a linear svm classifier on these features using the pegasos stochastic subgradient algorithm .	to use va for classification , a subsequent classifier is built -we first learn feature representations by va and then learn a linear svm on these features using pegasos algorithm .
recent advances in deep learning have revolutionized the application of machine learning in areas such as computer vision , speech recognition and natural language processing .	in recent years , ai and machine learning technologies have been widely used in various areas such as speech recognition , image processing and autonomous driving .
an important enabling factor of the rapid development of deep learning is the availability of large scale datasets .	the success of deep learning is driven , in part , by large datasets such as imagenet .
deep neural networks have achieved great success in various tasks , including but not limited to image classification .	deep neural networks have shown quite impressive performances in several pattern recognition applications .
many different neural language models have been proposed .	various methods have been proposed that scale and speed up large neural models .
generative adversarial networks , first introduced by , have become an important technique for learning generative models from complicated real-life data .	since their introduction a few years ago , generative adversarial networks have gained prominence as one of the most widely used methods for training deep generative models .
isola et al proposed the conditional gan framework for various image-to-image translation tasks with paired images for supervision .	isola et al have proposed a conditional gan-based unified framework for image-to-image translation .
recently , transformer , implemented as deep multi-head self-attention networks , has become the state-of-the-art neural machine translation model in recent years .	recently , neural machine translation systems have achieved state of the art performance in large-scale machine translation tasks .
we introduce batch normalization operations in every convolution layer of the contracting path .	we apply batch normalization and rectified linear unit after every convolutional layer , followed by max-pooling operations .
we optimize the variational lower bound over these parameters using stochastic gradient descent using adamax , a variant of the adam optimization algorithm .	we extract the gradients of the lower bound using automatic differentiation and maximize it using stochastic gradient ascent via the adam algorithm .
and the european vlbi network , which is a joint facility of european , chinese , south african and other radio astronomy institutes funded by their national research councils .	the european vlbi network is a joint facility of european and chinese radio astronomy institutes funded by their national research councils .
for the past several years , advances in deep neural networks have shown to be a powerful tool for a variety of machine learning problems in multiple domains , including computer vision .	with the rapid development of deep neural networks , computers now can achieve remarkable performance in many fields such as image classification .
as the higgs is a component of a 4d gluon , its self-energy has to be finite .	the endomorphism φ is called a higgs field .
deep neural networks have demonstrated excellent performance on challenging research benchmarks , while pushing the frontiers of numerous impactful applications such as language translation .	deep convolutional neural networks have made significant breakthroughs in many visual understanding tasks including image classification .
convolutional neural networks have made great progress in various fields , such as object classification , detection and character recognition .	in the last decade , convolutional neural networks have shown state of the art accuracy on a variety of visual recognition tasks such as image classification .
the front-end model is a modified version of the vgg-16 network and is extended for dense prediction .	moreover , they used the vgg16 model which is a shallower network compared to recent network models .
stewart , power counting in the soft-collinear effective theory , phys .	stewart , on power suppressed operators and gauge invariance in scet , phys .
thus , in this paper , we use sketch-a-net to extract texture features of sketch .	in our method , we use sketch-a-net framework to extract texture features of sketches .
during the last decade , deep learning algorithms , especially convolutional neural networks have achieved remarkable progress on numerous practical vision tasks .	in the last five years , deep neural networks have enjoyed tremendous progress , achieving or surpassing human-level performance in many tasks such as speech recognition .
recently , convolutional neural networks achieve remarkable progresses in a variety of computer vision tasks , such as image classification .	recently , deep convolutional neural networks have achieved great successes in computer vision topics such as image classification .
we deploy the first 10 layers from vgg-16 as the front-end and dilated convolution layers as the back-end to enlarge receptive fields and extract deeper features without losing resolutions .	we use first four convolutional layers from vgg-16 with pre-trained weights as our feature extraction part to obtain the image features .
in algebra such mappings concordant with algebraic structures are called morphisms .	category theory and even its part , which is called topos theory are called morphisms from a to b in c .
in this work , we adopt the adam solver to learn the model parameters .	we use the adaptive moment algorithm for training the model .
simultaneously we have the dynamic spectrum on the pc screen .	simultaneously we have the dynamic spectrum .
the scalar potential can be determined by a general ward identity of extended supergravities , which shows that it follows from squaring the fermion shifts .	the scalar potential of the theory follows as usual from the square of the fermionic shifts by using a known ward identity of n-extended gauged supergravities .
the classical morrey spaces were introduced by morrey to study the local behavior of solutions to second-order elliptic partial differential equations .	on the other hand , the classical morrey space was originally introduced by morrey in to study the local behavior of solutions to second order elliptic partial differential equations .
but it is somewhat complicated , so it will possibly appear elsewhere .	it is however somewhat complicated , and will possibly appear elsewhere .
the main result in this case is the classification of minimal triangular pointed hopf algebras .	a general classification of triangular hopf algebras is not known yet .
to study the effect of network structure on fast sparsely synchronized oscillations , we consider the wattsstrogatz model for small-world networks which interpolates between regular lattice and random graph via rewiring .	to study the effect of network structure on noiseinduced burst and spike synchronizations , we consider the watts-strogatz model for small-world networks which interpolates between regular lattice and random graph via rewiring .
the black and gray lines correspond to pc and pa monomers , respectively .	the solid and dashed lines correspond to pc and pa monomers , respectively .
one of the most successful deep learning paradigms for object detection is the series of region-based convolutional neural networks .	great progress has been made in recent years on object detection due to convolutional neural networks .
adversarial training is based on generative adversarial networks .	this can be achieved using generative adversarial networks .
the exchangecorrelation effects were treated within generalized gradient approximation within the perdew-burke-ernzerhof functional .	the exchange-correlation interactions are treated by the generalized gradient approximation formulated by perdew , burke , and ernzerhof .
positivity constraints on quark and gluon distributions in qcd .	electromagnetic polarizability of the nucleon in chiral perturbation theory .
millimeter wave communication is one of the most promising technologies of the fifth generation communication systems , due to the large spectrum resources in the mmwave bands .	massive multiple input multiple output technology is one of the promising means for achieving the extremely high energy and spectrum efficiency requirements of the future 5g networks .
propagator that there is a lessening of the overall slope , suggesting that the non oscillating piece may not be consistent with a single exponential .	this propagator is the inverse of the wave operator .
performance within the task of supervised image classification has been vastly improved in the era of deep learning using modern convolutional neural network .	recent development of deep convolutional neural networks has led to great success in a variety of tasks including image classfication and others .
we evaluate our object detection performance on the ms coco dataset , which contains 118k training images , 5k validation images and 20k hold-out testing images .	for this purpose , we train an object detector on ms coco , a dataset which has approximately 80k training images and 40k validation images .

liu et al leveraged unlabeled data to enhance the feature representation capability of the network .

liu et al rely on a single network by leveraging abundantly available unlabeled crowd imagery in a learning-to-rank framework .

each of the layers is followed by a relu layer for non-linear mapping .

each convolutional layer can have an arbitrary number and size of filters , and is followed by relu .

moreover , non-commutative field theory can be seen as an effective regime of string theory .

on the other hand , open string theory on a d2-brane can be understood from the viewpoint of deformation quantization .

this random walk is assumed to be skip free in the direction to the boundary of the quadrant , but may have unbounded jumps in the opposite direction , which are referred to as upward jumps .

this random walk is assumed to be skip free toward the boundary of the quadrant but may have unbounded jumps in the opposite direction , which we call upward jumps .

infogan learns to disentangle latent representations by maximizing the mutual information between a small subset of the latent variables and the observation .

infogan uses an unsupervised approach to learn semantic features maximizing mutual information between the latent code and the generated observation .

however , gravity is a global force and poisson solvers operate on the global density field .

gravity , which is the simplest model in higher-curvature theories of gravity .

the overall architecture is similar to and input the attention-derived image features to the cell node of the lstm .

in showatttell , while the overall architecture is similar to and input the attention-derived image features to the cell node of the lstm .

if the discriminant of t is a square , then a is not dominated by the set of powers of itself .

since the discriminant is a polynomial in y , it has a finite number of roots .

the fact that the low-lying states do not have parity doublets implies that the vacuum is not invariant under the axial transformations .

that the vacuum is not invariant under the axial transformation is directly seen from the nonzero values of the quark condensates , which are order parameters for spontaneous chiral symmetry breaking .

we use the recent results of on the achievable rates of finite block-length codes to investigate the power-limited outage probability of the arq protocols .

we use the recent results of on the achievable rates of finite block-length codes to analyze the system performance .

intuitively , a foliation is a pattern of -dimensional stripes - ie , submanifolds - on m n , called the leaves of the foliation , which are locally well-behaved .

a foliation by c k leaves which is tranversely c k is called simply a c k foliation .

we use adam to adapt learning rates and improve numerical stability .

we adopt the adam optimizer for accelerated training and learning rate decay .

convolutional neural networks have made great progress in various fields , such as object classification , detection and character recognition .

deep neural networks have revolutionized many domains , eg , image recognition , speech recognition and knowledge discovery .

in this subsection , we briefly review the symplectic approach to the toric sasaki-einstein manifolds according to .

in this section , we recall known facts about toric sasaki manifolds , following .

the mitigation methods based on power-law analysis can provide rough mitigation strategies in the planning horizon , but lack accurate tactics for online operations .

the mitigation methods based on power-law analysis can only provide rough mitigation strategies in the planning horizon , lacking accurate tactics for operations .

deep convolutional neural networks have demonstrated significant improvements over traditional approaches in many pattern recognition tasks .

convolutional neural networks have been instrumental to the recent breakthroughs in computer vision .

the models are trained using the adam optimiser with the default hyperparameters in minibatches of 80 instances .

all models are trained using mle loss and optimized using adam optimizer with a batch size 256 .

the lowest-dimension unprotected multiplets in this ope correspond to uirs lying above the unitarity bound of the continuous series and are realized by quadrilinear operators .

there the operators at the unitarity bound of the continuous series of uirs are trilinear and can not appear in the ope .

hd 45677 hd 45677 is a well studied b2 star whose evolutionary status is still unclear .

hd 37903 hd 37903 is the only sightline for which we have hst data that meets our criterion as an outlier .

now we proceed onto define smarandache co-ring and smarandache iso-ring .

now we proceed on to define smarandache n-ideal rings .

we employ the proximal policy optimization for the search algorithm .

in all experiments we use the common proximal policy optimization .

domain walls can form at a spontaneous phase transition with discrete symmetry breaking in the early universe .

topological defects could be produced at a phase transition in the early universe .

the lstms are now successfully applied in several applications , such as speech-recognition .

lstms have become very successful in applications to language modeling , machine translation , and speech recognition .

deep neural networks have been widely applied and achieved state-of-art performance on a variety of tasks including image recognition .

recurrent neural networks have received renewed interest due to their recent success in various domains , including speech recognition .

now we proceed on to define polynomial birings .

now we proceed on to define smarandache zero divisors .

in the case of free fermions , a complete classification has been achieved in using such ideas as anderson localization and k-theory .

for quadratic fermionic models , a complete topological classification of the possible states has been achieved .

this is the interface of the classical world and the quantum world .

this contrasts with the world indicated by quantum mechanics .

clv of maximum contrast values measured images .

clv of median contrast values measured images .

this filtration is canonically associated to e and is called the harder-narasimhan filtration of e .

it is called the harder-narasimhan filtration of x .

to evaluate the performance of our geonet in monocular depth estimation , we take the split of eigen et al to compare with related works .

we implement the same split of eigen et al to evaluate the performance of our dfo framework with others in the single-view depth estimation task .

so it is natural to expect that this theory may be also used to describe multiple m5-branes .

on the other hand , like its counterpart , this theory may be also a light-cone description of multiple m5-branes .

specifically , works in demonstrate such a difficulty of characterising the error surface of an mlp .

specifically , works in demonstrate such a difficulty of characterising the error surface of an fnn .

the advances in convolutional neural networks have successfully pushed the limits and improved the stateof-the-art technologies of image and video understanding .

recent advances in convolutional neural nets dramatically improved the state-of-the-art in image classification .

all spectra were grouped to a minimum of 20 counts per bin and fitted using xspec .

the spectra are grouped to have a minimum of 20 net counts per bin and fitted using the xspec package .

koch a , mcwilliam a , grebel ek , zucker db , belokurov v .

shetrone md , siegel mh , cook do , bosler t .

convolutional neural networks have achieved superior performance in many visual tasks , such as object classification and detection .

deep convolutional neural networks have achieved great success in various computer vision tasks , including object classification .

deep learning has become very popular for many computer vision and image recognition tasks .

deep learning has made an enormous impact on many applications in computer vision such as generic object recognition .

convolutional neural networks have recently achieved great success on various visual recognition tasks .

convolutional neural networks have broken many records of computer vision tasks , such as image classification .

imaging and self-calibration were performed using the difmap software package .

data reduction was carried out with the miriad software package using standard procedures .

we construct the local conforming virtual element space by resorting to the so-called enhancement strategy introduced in .

we construct the local nonconforming virtual element space by resorting to the so-called enhancement strategy originally devised in .

following , we say that an induced copy of claw in g is 1-heavy if at least one of its endvertices is heavy .

following , an induced claw of g is called 2-heavy if at least two of its end vertices are heavy .

recent advances in deep neural networks have contributed to the state-of-the-art performance in various artificial intelligence -based applications such as image classification , and so forth .

designing deeper and wider convolutional neural networks has led to significant breakthroughs in many machine learning tasks , such as image classification .

as we all know , quantum correlations , such as quantum entanglement , have been proposed as the key resource present in certain quantum communication tasks and quantum computational models .

these quantum correlations are powerful resources for quantum engineering , quantum cryptography , quantum communication , and quantum information processing .

a pulsar is a rotating neutron star with a strong magnetic dipole not aligned with the rotation axis .

a pulsar is a rapidly rotating neutron star that emits highly directional radiation .

gong et al explored spatiotemporal characteristics of intra-city trips using metro scd of 5 million trips in shenzhen , china .

gong et al explored spatiotemporal characteristics of intra-city trips using metro scd on 5 million trips in shenzhen , china .

deep neural networks , together with large scale accurately annotated datasets , have achieved remarkable performance in a great many classification tasks in recent years .

convolutional neural networks have achieved tremendous progress on many pattern recognition tasks , especially large-scale images recognition problems .

controllable single photons are an important tool to study fundamental quantum mechanics and also for practical applications in quantum communication .

entangled states of photons are the basic resource in the successful implementation of quantum information processing applications , namely optical quantum computing .

thus , work in this area relies on the development of algorithms which are guided by various heuristics .

thus , work in this area relies on the development of various heurisics-based algorithms .

the theoretical foundations for cothorities already exist in the form of threshold signatures .

the theoretical foundations for cosi and witness cothorities already exist in the form of threshold signatures .

finally , as an application , we consider systems of quotient coherent sheaves .

this alternative formulation is necessary for the later application to quotient coherent sheaves .

we compare our algorithm with other 11 state-ofthe-art ones including 7 deep learning based algorithms and 4 conventional algorithms .

we compare our proposed algorithm with other 14 state-ofthe-art ones , including 10 deep learning based algorithms and 4 conventional algorithms .

the neutrinoless double beta decay is a convenient tool to test physics beyond the sm .

double beta decay is the slowest nuclear decay process observed until now in nature .

the parameters are continuously optimized by means of the expectation-maximization algorithm , seeded by k-means clustering .

the associated parameters can be calculated considering the expectation maximization algorithm .

mc-cdma is formed by ofdm and cdma combination which is multiple access and multi-carrier system .

mc-cdma is produced by combination of ofdm and cdma which is multiple access and multi-carrier systems .

moreover , scalar linear network coding over a sufficiently large finite field was shown to be sufficient to achieve the capacity of a multicast network .

randomized linear network coding schemes were shown to be sufficient in achieving the information theoretic max-flow , min-cut bound on network capacity .

the ordinate is the difference between the value obtained from linear grid interpolation of the given parameter and the true value , expressed in units of the grid step .

the ordinate is the time dependent conductance g is the time dependent current , and v the bias .

generative adversarial networks are a recently developed generative model to produce synthetic images or texts after being trained .

generative adversarial networks are a recent popular technique for learning generative models for high-dimensional unstructured data .

articles study makespan minimization assuming that an online algorithm knows the optimum makespan or the sum of the processing times of σ .

articles study makepan minimization assuming that an online algorithm knows the optimum makespan or the sum of the processing times of σ .

in the quantum theory this is the -algebra symmetry .

assume that quantum theory is a correct model of the real world then we can always find .

among them , the approaches focused on shape retrieval , and performed diffusion on image level .

among them , the approaches addressed the shape retrieval problem , and performed diffusion on image level .

recently successful methods in naturalistic environments learn representations from sequences of frames .

recently successful methods learn representations from sequences of frames .

the axion is a hypothetical elementary particle pos tulated to resolve the strong cp problem in quantum chromodynamics .

the axion is a particle that is theoretically motivated , since is the consequence of the peccei quinn solution to the strong cp problem .

to perform classification using va , we first learn the feature representations by va , and then build a linear svm classifier on these features using the pegasos stochastic subgradient algorithm .

to use va for classification , a subsequent classifier is built -we first learn feature representations by va and then learn a linear svm on these features using pegasos algorithm .

recent advances in deep learning have revolutionized the application of machine learning in areas such as computer vision , speech recognition and natural language processing .

in recent years , ai and machine learning technologies have been widely used in various areas such as speech recognition , image processing and autonomous driving .

an important enabling factor of the rapid development of deep learning is the availability of large scale datasets .

the success of deep learning is driven , in part , by large datasets such as imagenet .

deep neural networks have achieved great success in various tasks , including but not limited to image classification .

deep neural networks have shown quite impressive performances in several pattern recognition applications .

many different neural language models have been proposed .

various methods have been proposed that scale and speed up large neural models .

generative adversarial networks , first introduced by , have become an important technique for learning generative models from complicated real-life data .

since their introduction a few years ago , generative adversarial networks have gained prominence as one of the most widely used methods for training deep generative models .

isola et al proposed the conditional gan framework for various image-to-image translation tasks with paired images for supervision .

isola et al have proposed a conditional gan-based unified framework for image-to-image translation .

recently , transformer , implemented as deep multi-head self-attention networks , has become the state-of-the-art neural machine translation model in recent years .

recently , neural machine translation systems have achieved state of the art performance in large-scale machine translation tasks .

we introduce batch normalization operations in every convolution layer of the contracting path .

we apply batch normalization and rectified linear unit after every convolutional layer , followed by max-pooling operations .

we optimize the variational lower bound over these parameters using stochastic gradient descent using adamax , a variant of the adam optimization algorithm .

we extract the gradients of the lower bound using automatic differentiation and maximize it using stochastic gradient ascent via the adam algorithm .

and the european vlbi network , which is a joint facility of european , chinese , south african and other radio astronomy institutes funded by their national research councils .

the european vlbi network is a joint facility of european and chinese radio astronomy institutes funded by their national research councils .

for the past several years , advances in deep neural networks have shown to be a powerful tool for a variety of machine learning problems in multiple domains , including computer vision .

with the rapid development of deep neural networks , computers now can achieve remarkable performance in many fields such as image classification .

as the higgs is a component of a 4d gluon , its self-energy has to be finite .

the endomorphism φ is called a higgs field .

deep neural networks have demonstrated excellent performance on challenging research benchmarks , while pushing the frontiers of numerous impactful applications such as language translation .

deep convolutional neural networks have made significant breakthroughs in many visual understanding tasks including image classification .

convolutional neural networks have made great progress in various fields , such as object classification , detection and character recognition .

in the last decade , convolutional neural networks have shown state of the art accuracy on a variety of visual recognition tasks such as image classification .

the front-end model is a modified version of the vgg-16 network and is extended for dense prediction .

moreover , they used the vgg16 model which is a shallower network compared to recent network models .

stewart , power counting in the soft-collinear effective theory , phys .

stewart , on power suppressed operators and gauge invariance in scet , phys .

thus , in this paper , we use sketch-a-net to extract texture features of sketch .

in our method , we use sketch-a-net framework to extract texture features of sketches .

during the last decade , deep learning algorithms , especially convolutional neural networks have achieved remarkable progress on numerous practical vision tasks .

in the last five years , deep neural networks have enjoyed tremendous progress , achieving or surpassing human-level performance in many tasks such as speech recognition .

recently , convolutional neural networks achieve remarkable progresses in a variety of computer vision tasks , such as image classification .

recently , deep convolutional neural networks have achieved great successes in computer vision topics such as image classification .

we deploy the first 10 layers from vgg-16 as the front-end and dilated convolution layers as the back-end to enlarge receptive fields and extract deeper features without losing resolutions .

we use first four convolutional layers from vgg-16 with pre-trained weights as our feature extraction part to obtain the image features .

in algebra such mappings concordant with algebraic structures are called morphisms .

category theory and even its part , which is called topos theory are called morphisms from a to b in c .

in this work , we adopt the adam solver to learn the model parameters .

we use the adaptive moment algorithm for training the model .

simultaneously we have the dynamic spectrum on the pc screen .

simultaneously we have the dynamic spectrum .

the scalar potential can be determined by a general ward identity of extended supergravities , which shows that it follows from squaring the fermion shifts .

the scalar potential of the theory follows as usual from the square of the fermionic shifts by using a known ward identity of n-extended gauged supergravities .

the classical morrey spaces were introduced by morrey to study the local behavior of solutions to second-order elliptic partial differential equations .

on the other hand , the classical morrey space was originally introduced by morrey in to study the local behavior of solutions to second order elliptic partial differential equations .

but it is somewhat complicated , so it will possibly appear elsewhere .

it is however somewhat complicated , and will possibly appear elsewhere .

the main result in this case is the classification of minimal triangular pointed hopf algebras .

a general classification of triangular hopf algebras is not known yet .

to study the effect of network structure on fast sparsely synchronized oscillations , we consider the wattsstrogatz model for small-world networks which interpolates between regular lattice and random graph via rewiring .

to study the effect of network structure on noiseinduced burst and spike synchronizations , we consider the watts-strogatz model for small-world networks which interpolates between regular lattice and random graph via rewiring .

the black and gray lines correspond to pc and pa monomers , respectively .

the solid and dashed lines correspond to pc and pa monomers , respectively .

one of the most successful deep learning paradigms for object detection is the series of region-based convolutional neural networks .

great progress has been made in recent years on object detection due to convolutional neural networks .

adversarial training is based on generative adversarial networks .

this can be achieved using generative adversarial networks .

the exchangecorrelation effects were treated within generalized gradient approximation within the perdew-burke-ernzerhof functional .

the exchange-correlation interactions are treated by the generalized gradient approximation formulated by perdew , burke , and ernzerhof .

positivity constraints on quark and gluon distributions in qcd .

electromagnetic polarizability of the nucleon in chiral perturbation theory .

millimeter wave communication is one of the most promising technologies of the fifth generation communication systems , due to the large spectrum resources in the mmwave bands .

massive multiple input multiple output technology is one of the promising means for achieving the extremely high energy and spectrum efficiency requirements of the future 5g networks .

propagator that there is a lessening of the overall slope , suggesting that the non oscillating piece may not be consistent with a single exponential .

this propagator is the inverse of the wave operator .

performance within the task of supervised image classification has been vastly improved in the era of deep learning using modern convolutional neural network .

recent development of deep convolutional neural networks has led to great success in a variety of tasks including image classfication and others .

we evaluate our object detection performance on the ms coco dataset , which contains 118k training images , 5k validation images and 20k hold-out testing images .

for this purpose , we train an object detector on ms coco , a dataset which has approximately 80k training images and 40k validation images .