HHousen/ParaSCI · Datasets at Hugging Face

sentence1 stringlengths 16 446	sentence2 stringlengths 14 436
continuous-time quantum walks , first defined in , are the quantum analogue of continuoustime markov chains .	continuous-time quantum walks are analogues of continuous-time classical markov chains .
deep learning is a field of machine learning that aims to model data using complex architectures combining several nonlinear transformations with hundreds of parameters called deep neural networks .	deep learning is a subfield of machine learning that allows computational models composed of multiple processing layers to learn representations of data with multiple levels of abstraction .
the lieb-robinson bound was extended to systems with long-range interactions decaying by power law .	the first generalization of the lieb-robinson bound to power-law decaying interactions was given by hastings and koma .
the duration is the total period of the run with enabled magnetic fields .	duration is the length of the observation .
most prior work on the ap has focused on the domain of pattern mining such as biological motif search .	most prior work on the ap has focused on the domain of high speed pattern mining such as biological motif search .
tremendous progress has been made on object detection in static images thanks to the success of deep convolutional neural networks .	object detection has been greatly improved by convolutional neural networks on both accuracy and speed .
low-density parity-check codes have emerged as one of the top contenders for capacity approaching error correction over many important channels .	low-density parity-check codes together with message passing decoders are among the most powerful channel coding schemes known .
similarly , in quantum mechanics this is a legitimate energy eigenstate , and the dipole will remain anti-aligned with the field in the absence of perturbations .	in quantum mechanics , there is a lower limit to this process , set by the size of the planck cell , and this reduces the complexity of quantum motion as compared to classical motion .
deep learning has been widely adapted to many different problems , such as image classification , and has demonstrated state-of-the-art results for these problems .	deep learning models have yielded outstanding results in several applications , including speech recognition .
in this context , deep neural networks have set benchmarks in various fields of research , such as computer vision , speech recognition and image classification .	deep learning has had a tremendous impact in several fields , such as image processing and natural language processing .
reliability requirements in terms of packet delivery success rates may be as high as 5-nines to 9-nines , while also the acceptable latency may be at the sub-second level or even down to a few milliseconds .	reliability requirements in terms of packet loss rates may be as high as 5-nines to 9-nines , while also the acceptable latency may be down to few milliseconds .
this radiation we will denote the synchrotron radiation background .	therefore , radiation is a negligible effect in the post-reconnection process .
dhingra et al proposed an end-to-end rl dialogue agent for information access .	dhingra et al proposed an rl based model for information access that can learn online via user interactions .
we have shown how the smoothness of the gauge field as seen by the overlap operator depends on the value of the wilson mass .	we examine the extent to which the topological charge density , revealed by the overlap definition , depends on the value of the negative wilson mass .
we refer the reader to for a thorough exposition of symbolic dynamics .	we refer the reader to the excellent book of lind and marcus for background on shifts of finite type .
in the standard big bang cosmology inflation was proposed to solve simultaneously the flatness , isotropy , homogenity , horizon and relics problems .	in the standard big bang cosmology the cosmological inflation was proposed in to solve the flatness , isotropy , homogeneity , horizon and relic problems .
let us compute the terms of highest degree in these variables .	let us consider the isomonodromic coordinates .
in recent years , deep learning methods , such as convolutional neural networks and recurrent neural networks , and their variations , have demonstrated excellent performance in visual and multi-label classification .	in recent years , significant advances in speed and accuracy have been achieved by detection frameworks based on convolutional neural networks .
the most well-known qkd protocol is the first one called as bennett-brassard-84 created in 1984 .	the best-known protocol for qkd is the bennett-brassard protocol .
bolme et al proposed a fast correlation tracker with a minimum output sum of squared error filter , which runs in hundreds of frames per second .	bolme et al propose a cf tracker by learning a minimum output sum of squared error for target appearance , which is able to run in high speed .
graphene is a fascinating material with unusual electronic , magnetic , optical and thermal properties 1 .	as graphene is a zero band gap material , one of the routes to make use of graphene in electronics industry is to create a small band gap by means of functionalization with external chemical agents .
these all suggest that the ising model is a handy tool to study magnetic thin-films .	however , the ising model is a special case of general magnetic model , for example , the heisenberg model .
for example bayesian inference is often used to infer densities of random variables conditional on available observational densities .	for example , bayesian inference is often used to infer densities of random variables conditional on available observational data .
we reduced the data with the miriad package , using natural weighting .	we fit the disks using the imfit task in miriad .
let us denote by tdo the set of ox -ring isomorphism classes of tdo-rings , endowed with this abelian group law .	denote by br the set of equivalence classes of azumaya r-algebras endowed with this abelian group law , which is called brauer group of r .
independently , brundan and kleshchev have proved a similar result for cyclotomic quotient algebras .	in a landmark paper , brundan and kleshchev constructed an explicit zgrading on the cyclotomic hecke algebras of type a .
gao et al used this fine-tuning scheme to detect lung disease in ct images .	gao et al proposed a deep cnn for the classification of interstitial lung disease patterns on ct images .
deep convolutional neural networks have seen great success in a range of computer vision tasks , including image classification .	convolutional neural networks have achieved state-of-the-art accuracy in computer vision tasks such as image recognition .
artificial neural networks have become an indispensable asset for applied machine learning , rivaling human performance in a variety of domain-specific tasks .	deep neural networks are increasingly used in a variety of applications , many of them with substantial safety and security concerns .
bepposax is a joint italian-dutch program .	bepposax also consists of a high pressure gas scintillator proportional counter along-with a phoswich detection system .
our cnns architecture for semantic segmentation is based on densenets .	we use a fully-convolutional densenet architecture for semantic segmentation tasks .
we will now proceed onto define interval subgroupoids of .	we now proceed onto describe and define level three interval polynomial groupoid .
deep convolutional neural networks have enabled unparalleled breakthroughs in a variety of visual tasks , such as image classification .	deep neural networks have contributed to tremendous advances in computer vision during recent times .
this geometry is a trumpet with curvature singularity at the origin of the coordinate system , and it is dual to the semi-infinite cigar .	remember that our geometry consists of a double elliptic fibration over a complex plane parameterised by z .
the largest influence of horizontal rt has been found in the rms contrast of the line core intensity .	the largest effect of horizontal rt is found in the residual intensity contrast owing to the low absolute value of the intensity .
the interested reader may refer to for a comprehensive treatment of the moreau envelope in a hilbert setting .	for a more comprehensive coverage we refer the interested reader to in the finite dimensional case .
the specific heat in dependence on the temperature for different electron filling .	the specific heat in dependence on the temperature for the canonical ensemble and half-filling .
this will generalize the results obtained in in the context of symplectic field theory .	this is the setup in which one is able to study moduli spaces of holomorphic curves , in the framework of symplectic field theory .
therefore , the bit-flip and phase errors are , indeed , correlated .	this implies that there are non-trivial correlations between bit-flip and phase errors .
then the power conjugacy problem for hyperbolic elements is solvable in g .	thus the special parabolicity problem is unsolvable in g .
the wave front is the solution which depends on one space variable only , so it is not a soliton .	the wave front is the eikonal surface , ie the surface with a conservative quantity .
this immediately shows that bqp01 is np-hard and one can also establish some approximation hardness results with appropriate assumptions .	this immediately shows that the bbqp is np-hard and one can also establish some approximation hardness results with appropriate assumptions .
generative adversarial networks have been used to model and sample from natural image distributions .	generative adversarial networks have been extensively used in image generation .
for this we use both the point signal-to-noise ratio and the structural similarity index measure .	we use the average peak signal to noise ratio and structural similarity scores for which higher scores indicate better results .
analysis of the model can be done via an implementation in a term rewriting tool .	analysis of the model can be done via an implementation in maude .
generative adversarial networks have achieved great success at generating realistic and sharp looking images .	generative adversarial networks have become the benchmark in image synthesis .
this minimal bosonic vasiliev theorywas conjectured to be dual to 3d free or critical ovector model , depending on the bulk scalar boundary conditions .	the free ovector model in three dimensions is conjectured to be described holographically by a higher-spin vassiliev theory in ads 4 with certain specific boundary conditions .
recently , deep neural networks have achieved impressive results for many image classification tasks .	deep convolutional neural networks have achieved great success in image classification and many others .
our approach distributes that in by proposing the usage of an inverse approximated chain which can be computed in a distributed fashion .	hence , we parways with by proposing an inverse chain which can be computed in a distributed fashion .
in the sequel c will always denote a positive constant which may vary from line to line .	in the sequel c denotes generic , but not necessarily equal constants .
object recognition is one of the most challenging problems in computer vision , and is catalysed by the swift development of deep learning in recent years .	detecting people in images is among the most important components of computer vision and has attracted increasing attention in recent years .
deep convolutional neural networks have achieved great success in image classification and many others .	in recent years , deep neural networks have been applied to many areas and have achieved huge success in different domains such as image classification .
this machine consists of 64 300 mhz mips 10000 processors with an aggregate memory of 16 gbytes .	this machine consists of 100 nodes and has a peak speed of 1 2 tflops and 448 gb of main memory in total .
by receiving it bob gets to observe quantum cloning of alices original q-bit .	if bob has time to receive this message he gets to observe quantum cloning of alices original q-bit .
the metropolis-hastings algorithm is the standard tool for drawing samples from a target probability distribution π .	the metropolis-hastings algorithm gives a way to define a transition probability so that a random walk converges to any desired distribution π .
dirhc is one of the 21 np-complete problems originally introduced by karp .	the vc problem is one of the classical np-complete problems listed by karp .
the liquid phase was described using the subregular solution model , and solid phases-in the framework of the compound energy formalism .	the liquid phase was described using the sub regular solution model , and solid phases-in the framework of the compound energy formalism .
the virtual uplink channel corresponding to can be expressed as .	the transmitted signal vector at the bs can be expressed as .
in , an rnn with lstm units was used to encode a source sentence and starting from the last hidden state , to decode a target sentence .	in , an rnn was used to encode a source sentence and starting from the last hidden state , to decode a target sentence .
mpm is a derivative of the fluid-implicitparticle method , which is based on the particle-in-cell method .	first described in sulsky et al , mpm is a derivative of the fluid-implicit-particle method , which in turn is based on the particle-in-cell method .
in recent years , word embeddings have brought large improvements to a wide range of applications in natural language processing .	recently , word embeddings such as word2vec have yielded significant advances in this direction .
vaswani et al applied self-attention to neural machine translation and achieved the state-of-the-art results .	lately , vaswani et al presented the first fully attention-based model , which recorded the state-of-the-art result in machine translation .
the shear rate a is the relevant nonequilibrium parameter of the problem .	the shear rate is the same for three cases .
at the end of each feature embedding network , we use batch-normalization and l2-normalization respectively .	in particular , we apply batch normalization after each convolutional layer .
however , it would be of interest to get such an example in zfc .	of course , an example in zfc would be desirable .
there are mainly two approaches for the file caching , namely deterministic caching and random caching .	there are mainly two approaches for file caching , namely deterministic caching and random caching .
the last map is the suspension isomorphism .	the third maps are the homology isomorphism .
conditional random fields are a framework for building probabilistic models to segment and label sequence data .	conditional random fields are undirected graphical models that are generally used for segmenting and labeling a given sequence .
deep learning techniques have been extensively researched for the tasks of segmentation of two dimensional images .	deep convolutional neural networks have been proven very useful in various tasks in computer vision including classification .
recently , deep learning methods have made remarkable progress in computer vision and machine learning .	the recent advances in deep neural networks have shown promising results on a variety machine leaning tasks including image classification .
the post-newtonian approximation of general relativity stands out among the various and complementary approaches to model these systems , as it enables to an analytical treatment of the inspiral phase of their evolution .	the post-newtonian approximation of general relativity stands out among the various and complementary approaches to model these systems , as it enables to treat analytically the initial inspiral phase of their evolution .
the following proposition is an immediate consequence of the above definition .	the motivation for the above definition is the following observation .
specifically , the quantization of the orbital radius and the structure wavelength fixes their coupling , which in turn determines the relationship between µ and m .	the quantization of the structure radius and wavelength fixes their relationship , which in turn determines the relationship between µ and m .
recently proposed deep convolutional neural network models have made incredible progress on image classification and other vision problems .	recent years have witnessed astonishing progress in accuracy of convolutional neural networks on visual object recognition tasks .
in these scenarios the knee is a consequence of leakage of particles from the galaxy .	which of these scenarios is realized is a function of vortex lattice geometry and the anisotropy of the dirac dispersion in the homogenous superconductor .
deep neural networks have led to a revolution in the areas of machine learning , audio analysis , and computer vision , achieving state-of-the-art results in numerous applications .	deep neural networks have recently achieved great success in computer vision , speech recognition , and natural language processing .
jagannathan , quantum theory of electron lenses based on the dirac equation , phys .	jagannathan , on the quantum mechanics of charged particle beam transport through magnetic lenses , phys .
to compute the poisson cohomology of star log symplectic surfaces , we will use the method of rigged lie algebroids that we introduced in .	we compute poisson cohomology by constructing the rigged lie algebroid r that we introduced in .
generative adversarial networks and variational autoencoders are the most successful approaches applied to unsupervised learning of complicated empirical distributions .	the best known representatives for generative models are variational autoencoders and generative adversarial networks .
graphene is a semiconductor material with zero energy bandgap .	graphene is a semi-metal that is characterized by an unusual electronic structure .
the dirichlet process is the natural conjugate prior for non-parametric discrete distributions .	the dirichlet process is a distribution over random distributions .
moreover , we assume that all pieces of this system have the same schmidt basis .	we will assume that all states have the same basis for their schmidt decompositions .
the higgs boson is the scalar gauge boson to mediate the transformation between massive particle .	if the higgs boson is a fundamental scalar , its mass has to be protected .
characterization of skew-invariant curves .	characterization of translation related ritt polynomials .
embedding-based language models , such as word2vec , have helped to improve performance in many nlp tasks .	distributed word embeddings have proven to be a useful feature in many nlp tasks .
the two most significant types are the variational auto-encoders and generative adversarial networks .	two notable approaches in this area are variational auto-encoders , and generative adversarial networks .
molecules comprise an excellent model system for charge transport in one dimensional polymers .	with intrinsically 3d-chiral molecules , and it is the property of unequal absorption of right and .
on the universal space for group actions with compact isotropy .	on algebraic and geometric dimensions for groups with torsion .
spin model with only the ising interaction is called the ising model .	because the ising model is a free-field theory , the computations are particularly simple .
we use both plain stochastic gradient-ascent and the adam optimiser with default parameter values .	we use the adam optimizer , with gradients clipped with norm value 1 .
however , exploiting the low-rank nature of the high-dimensional pmu data matrix z , the authors in propose a new attack detection mechanism based on ld so as to separate the lowrank matrix z and column sparse matrix ch t in .	however , exploiting the low-rank nature of the high-dimensional pmu data matrix w , the authors in propose a new attack detection mechanism based on lrd so as to separate the low-rank matrix w and column sparse matrix ch t in .
with the surge of deep learning research and applications in recent years , lots of research works have appeared to apply the deep learning methods , like deep belief network , in various applications .	with the surge of deep learning research and applications in recent years , lots of research works have appeared to apply the deep learning methods , like deep belief network .
gong et al used a ranking-based learning strategy to train deep convolutional neural networks for multi-label image recognition and found that the weighted approximated-ranking loss performs best .	based on such deep features , gong et al evaluated various loss functions and found that weighted approximate ranking loss worked best with cnns .
the renormalization is a difficult task which we neglect .	the renormalization is a quadratic term , regardless of whether or not the original model contained such a term .
generalizing the same ansatz , formulas have been given in for the asymptotic velocity and gaussian fluctuations of parallel-chain models and divided-pathway models .	generalizing the same ansatz , formulas have been given in for the asymptotic velocity and gaussian fluctuations of parallel-chains models and divided-chains models .
deep convolutional neural networks have been successfully applied to a wide range of image classification tasks .	convolutional neural networks have improved the performance of image classification significantly .
more precisely , this low energy theory is a special case of noncommutative dipole theory we introduced and studied in previous sections .	the low energy theory is a u gauge theory with electrically charged matter , and flows to a free theory in the infrared limit .
action sets , automata and comparisons form what is known as a discrete cartesian bicategory .	one would like to say this makes form what is called a bicategory .
this may be also spelled out that vacuum is a most degenerate state of a system .	thus , this vacuum is a true minimum of the full theory .
since then , depth prediction has evolved from simple handcrafted feature representations .	since then , depth estimation has evolved from using simple handcrafted feature representations .

continuous-time quantum walks , first defined in , are the quantum analogue of continuoustime markov chains .

continuous-time quantum walks are analogues of continuous-time classical markov chains .

deep learning is a field of machine learning that aims to model data using complex architectures combining several nonlinear transformations with hundreds of parameters called deep neural networks .

deep learning is a subfield of machine learning that allows computational models composed of multiple processing layers to learn representations of data with multiple levels of abstraction .

the lieb-robinson bound was extended to systems with long-range interactions decaying by power law .

the first generalization of the lieb-robinson bound to power-law decaying interactions was given by hastings and koma .

the duration is the total period of the run with enabled magnetic fields .

duration is the length of the observation .

most prior work on the ap has focused on the domain of pattern mining such as biological motif search .

most prior work on the ap has focused on the domain of high speed pattern mining such as biological motif search .

tremendous progress has been made on object detection in static images thanks to the success of deep convolutional neural networks .

object detection has been greatly improved by convolutional neural networks on both accuracy and speed .

low-density parity-check codes have emerged as one of the top contenders for capacity approaching error correction over many important channels .

low-density parity-check codes together with message passing decoders are among the most powerful channel coding schemes known .

similarly , in quantum mechanics this is a legitimate energy eigenstate , and the dipole will remain anti-aligned with the field in the absence of perturbations .

in quantum mechanics , there is a lower limit to this process , set by the size of the planck cell , and this reduces the complexity of quantum motion as compared to classical motion .

deep learning has been widely adapted to many different problems , such as image classification , and has demonstrated state-of-the-art results for these problems .

deep learning models have yielded outstanding results in several applications , including speech recognition .

in this context , deep neural networks have set benchmarks in various fields of research , such as computer vision , speech recognition and image classification .

deep learning has had a tremendous impact in several fields , such as image processing and natural language processing .

reliability requirements in terms of packet delivery success rates may be as high as 5-nines to 9-nines , while also the acceptable latency may be at the sub-second level or even down to a few milliseconds .

reliability requirements in terms of packet loss rates may be as high as 5-nines to 9-nines , while also the acceptable latency may be down to few milliseconds .

this radiation we will denote the synchrotron radiation background .

therefore , radiation is a negligible effect in the post-reconnection process .

dhingra et al proposed an end-to-end rl dialogue agent for information access .

dhingra et al proposed an rl based model for information access that can learn online via user interactions .

we have shown how the smoothness of the gauge field as seen by the overlap operator depends on the value of the wilson mass .

we examine the extent to which the topological charge density , revealed by the overlap definition , depends on the value of the negative wilson mass .

we refer the reader to for a thorough exposition of symbolic dynamics .

we refer the reader to the excellent book of lind and marcus for background on shifts of finite type .

in the standard big bang cosmology inflation was proposed to solve simultaneously the flatness , isotropy , homogenity , horizon and relics problems .

in the standard big bang cosmology the cosmological inflation was proposed in to solve the flatness , isotropy , homogeneity , horizon and relic problems .

let us compute the terms of highest degree in these variables .

let us consider the isomonodromic coordinates .

in recent years , deep learning methods , such as convolutional neural networks and recurrent neural networks , and their variations , have demonstrated excellent performance in visual and multi-label classification .

in recent years , significant advances in speed and accuracy have been achieved by detection frameworks based on convolutional neural networks .

the most well-known qkd protocol is the first one called as bennett-brassard-84 created in 1984 .

the best-known protocol for qkd is the bennett-brassard protocol .

bolme et al proposed a fast correlation tracker with a minimum output sum of squared error filter , which runs in hundreds of frames per second .

bolme et al propose a cf tracker by learning a minimum output sum of squared error for target appearance , which is able to run in high speed .

graphene is a fascinating material with unusual electronic , magnetic , optical and thermal properties 1 .

as graphene is a zero band gap material , one of the routes to make use of graphene in electronics industry is to create a small band gap by means of functionalization with external chemical agents .

these all suggest that the ising model is a handy tool to study magnetic thin-films .

however , the ising model is a special case of general magnetic model , for example , the heisenberg model .

for example bayesian inference is often used to infer densities of random variables conditional on available observational densities .

for example , bayesian inference is often used to infer densities of random variables conditional on available observational data .

we reduced the data with the miriad package , using natural weighting .

we fit the disks using the imfit task in miriad .

let us denote by tdo the set of ox -ring isomorphism classes of tdo-rings , endowed with this abelian group law .

denote by br the set of equivalence classes of azumaya r-algebras endowed with this abelian group law , which is called brauer group of r .

independently , brundan and kleshchev have proved a similar result for cyclotomic quotient algebras .

in a landmark paper , brundan and kleshchev constructed an explicit zgrading on the cyclotomic hecke algebras of type a .

gao et al used this fine-tuning scheme to detect lung disease in ct images .

gao et al proposed a deep cnn for the classification of interstitial lung disease patterns on ct images .

deep convolutional neural networks have seen great success in a range of computer vision tasks , including image classification .

convolutional neural networks have achieved state-of-the-art accuracy in computer vision tasks such as image recognition .

artificial neural networks have become an indispensable asset for applied machine learning , rivaling human performance in a variety of domain-specific tasks .

deep neural networks are increasingly used in a variety of applications , many of them with substantial safety and security concerns .

bepposax is a joint italian-dutch program .

bepposax also consists of a high pressure gas scintillator proportional counter along-with a phoswich detection system .

our cnns architecture for semantic segmentation is based on densenets .

we use a fully-convolutional densenet architecture for semantic segmentation tasks .

we will now proceed onto define interval subgroupoids of .

we now proceed onto describe and define level three interval polynomial groupoid .

deep convolutional neural networks have enabled unparalleled breakthroughs in a variety of visual tasks , such as image classification .

deep neural networks have contributed to tremendous advances in computer vision during recent times .

this geometry is a trumpet with curvature singularity at the origin of the coordinate system , and it is dual to the semi-infinite cigar .

remember that our geometry consists of a double elliptic fibration over a complex plane parameterised by z .

the largest influence of horizontal rt has been found in the rms contrast of the line core intensity .

the largest effect of horizontal rt is found in the residual intensity contrast owing to the low absolute value of the intensity .

the interested reader may refer to for a comprehensive treatment of the moreau envelope in a hilbert setting .

for a more comprehensive coverage we refer the interested reader to in the finite dimensional case .

the specific heat in dependence on the temperature for different electron filling .

the specific heat in dependence on the temperature for the canonical ensemble and half-filling .

this will generalize the results obtained in in the context of symplectic field theory .

this is the setup in which one is able to study moduli spaces of holomorphic curves , in the framework of symplectic field theory .

therefore , the bit-flip and phase errors are , indeed , correlated .

this implies that there are non-trivial correlations between bit-flip and phase errors .

then the power conjugacy problem for hyperbolic elements is solvable in g .

thus the special parabolicity problem is unsolvable in g .

the wave front is the solution which depends on one space variable only , so it is not a soliton .

the wave front is the eikonal surface , ie the surface with a conservative quantity .

this immediately shows that bqp01 is np-hard and one can also establish some approximation hardness results with appropriate assumptions .

this immediately shows that the bbqp is np-hard and one can also establish some approximation hardness results with appropriate assumptions .

generative adversarial networks have been used to model and sample from natural image distributions .

generative adversarial networks have been extensively used in image generation .

for this we use both the point signal-to-noise ratio and the structural similarity index measure .

we use the average peak signal to noise ratio and structural similarity scores for which higher scores indicate better results .

analysis of the model can be done via an implementation in a term rewriting tool .

analysis of the model can be done via an implementation in maude .

generative adversarial networks have achieved great success at generating realistic and sharp looking images .

generative adversarial networks have become the benchmark in image synthesis .

this minimal bosonic vasiliev theorywas conjectured to be dual to 3d free or critical ovector model , depending on the bulk scalar boundary conditions .

the free ovector model in three dimensions is conjectured to be described holographically by a higher-spin vassiliev theory in ads 4 with certain specific boundary conditions .

recently , deep neural networks have achieved impressive results for many image classification tasks .

deep convolutional neural networks have achieved great success in image classification and many others .

our approach distributes that in by proposing the usage of an inverse approximated chain which can be computed in a distributed fashion .

hence , we parways with by proposing an inverse chain which can be computed in a distributed fashion .

in the sequel c will always denote a positive constant which may vary from line to line .

in the sequel c denotes generic , but not necessarily equal constants .

object recognition is one of the most challenging problems in computer vision , and is catalysed by the swift development of deep learning in recent years .

detecting people in images is among the most important components of computer vision and has attracted increasing attention in recent years .

deep convolutional neural networks have achieved great success in image classification and many others .

in recent years , deep neural networks have been applied to many areas and have achieved huge success in different domains such as image classification .

this machine consists of 64 300 mhz mips 10000 processors with an aggregate memory of 16 gbytes .

this machine consists of 100 nodes and has a peak speed of 1 2 tflops and 448 gb of main memory in total .

by receiving it bob gets to observe quantum cloning of alices original q-bit .

if bob has time to receive this message he gets to observe quantum cloning of alices original q-bit .

the metropolis-hastings algorithm is the standard tool for drawing samples from a target probability distribution π .

the metropolis-hastings algorithm gives a way to define a transition probability so that a random walk converges to any desired distribution π .

dirhc is one of the 21 np-complete problems originally introduced by karp .

the vc problem is one of the classical np-complete problems listed by karp .

the liquid phase was described using the subregular solution model , and solid phases-in the framework of the compound energy formalism .

the liquid phase was described using the sub regular solution model , and solid phases-in the framework of the compound energy formalism .

the virtual uplink channel corresponding to can be expressed as .

the transmitted signal vector at the bs can be expressed as .

in , an rnn with lstm units was used to encode a source sentence and starting from the last hidden state , to decode a target sentence .

in , an rnn was used to encode a source sentence and starting from the last hidden state , to decode a target sentence .

mpm is a derivative of the fluid-implicitparticle method , which is based on the particle-in-cell method .

first described in sulsky et al , mpm is a derivative of the fluid-implicit-particle method , which in turn is based on the particle-in-cell method .

in recent years , word embeddings have brought large improvements to a wide range of applications in natural language processing .

recently , word embeddings such as word2vec have yielded significant advances in this direction .

vaswani et al applied self-attention to neural machine translation and achieved the state-of-the-art results .

lately , vaswani et al presented the first fully attention-based model , which recorded the state-of-the-art result in machine translation .

the shear rate a is the relevant nonequilibrium parameter of the problem .

the shear rate is the same for three cases .

at the end of each feature embedding network , we use batch-normalization and l2-normalization respectively .

in particular , we apply batch normalization after each convolutional layer .

however , it would be of interest to get such an example in zfc .

of course , an example in zfc would be desirable .

there are mainly two approaches for the file caching , namely deterministic caching and random caching .

there are mainly two approaches for file caching , namely deterministic caching and random caching .

the last map is the suspension isomorphism .

the third maps are the homology isomorphism .

conditional random fields are a framework for building probabilistic models to segment and label sequence data .

conditional random fields are undirected graphical models that are generally used for segmenting and labeling a given sequence .

deep learning techniques have been extensively researched for the tasks of segmentation of two dimensional images .

deep convolutional neural networks have been proven very useful in various tasks in computer vision including classification .

recently , deep learning methods have made remarkable progress in computer vision and machine learning .

the recent advances in deep neural networks have shown promising results on a variety machine leaning tasks including image classification .

the post-newtonian approximation of general relativity stands out among the various and complementary approaches to model these systems , as it enables to an analytical treatment of the inspiral phase of their evolution .

the post-newtonian approximation of general relativity stands out among the various and complementary approaches to model these systems , as it enables to treat analytically the initial inspiral phase of their evolution .

the following proposition is an immediate consequence of the above definition .

the motivation for the above definition is the following observation .

specifically , the quantization of the orbital radius and the structure wavelength fixes their coupling , which in turn determines the relationship between µ and m .

the quantization of the structure radius and wavelength fixes their relationship , which in turn determines the relationship between µ and m .

recently proposed deep convolutional neural network models have made incredible progress on image classification and other vision problems .

recent years have witnessed astonishing progress in accuracy of convolutional neural networks on visual object recognition tasks .

in these scenarios the knee is a consequence of leakage of particles from the galaxy .

which of these scenarios is realized is a function of vortex lattice geometry and the anisotropy of the dirac dispersion in the homogenous superconductor .

deep neural networks have led to a revolution in the areas of machine learning , audio analysis , and computer vision , achieving state-of-the-art results in numerous applications .

deep neural networks have recently achieved great success in computer vision , speech recognition , and natural language processing .

jagannathan , quantum theory of electron lenses based on the dirac equation , phys .

jagannathan , on the quantum mechanics of charged particle beam transport through magnetic lenses , phys .

to compute the poisson cohomology of star log symplectic surfaces , we will use the method of rigged lie algebroids that we introduced in .

we compute poisson cohomology by constructing the rigged lie algebroid r that we introduced in .

generative adversarial networks and variational autoencoders are the most successful approaches applied to unsupervised learning of complicated empirical distributions .

the best known representatives for generative models are variational autoencoders and generative adversarial networks .

graphene is a semiconductor material with zero energy bandgap .

graphene is a semi-metal that is characterized by an unusual electronic structure .

the dirichlet process is the natural conjugate prior for non-parametric discrete distributions .

the dirichlet process is a distribution over random distributions .

moreover , we assume that all pieces of this system have the same schmidt basis .

we will assume that all states have the same basis for their schmidt decompositions .

the higgs boson is the scalar gauge boson to mediate the transformation between massive particle .

if the higgs boson is a fundamental scalar , its mass has to be protected .

characterization of skew-invariant curves .

characterization of translation related ritt polynomials .

embedding-based language models , such as word2vec , have helped to improve performance in many nlp tasks .

distributed word embeddings have proven to be a useful feature in many nlp tasks .

the two most significant types are the variational auto-encoders and generative adversarial networks .

two notable approaches in this area are variational auto-encoders , and generative adversarial networks .

molecules comprise an excellent model system for charge transport in one dimensional polymers .

with intrinsically 3d-chiral molecules , and it is the property of unequal absorption of right and .

on the universal space for group actions with compact isotropy .

on algebraic and geometric dimensions for groups with torsion .

spin model with only the ising interaction is called the ising model .

because the ising model is a free-field theory , the computations are particularly simple .

we use both plain stochastic gradient-ascent and the adam optimiser with default parameter values .

we use the adam optimizer , with gradients clipped with norm value 1 .

however , exploiting the low-rank nature of the high-dimensional pmu data matrix z , the authors in propose a new attack detection mechanism based on ld so as to separate the lowrank matrix z and column sparse matrix ch t in .

however , exploiting the low-rank nature of the high-dimensional pmu data matrix w , the authors in propose a new attack detection mechanism based on lrd so as to separate the low-rank matrix w and column sparse matrix ch t in .

with the surge of deep learning research and applications in recent years , lots of research works have appeared to apply the deep learning methods , like deep belief network , in various applications .

with the surge of deep learning research and applications in recent years , lots of research works have appeared to apply the deep learning methods , like deep belief network .

gong et al used a ranking-based learning strategy to train deep convolutional neural networks for multi-label image recognition and found that the weighted approximated-ranking loss performs best .

based on such deep features , gong et al evaluated various loss functions and found that weighted approximate ranking loss worked best with cnns .

the renormalization is a difficult task which we neglect .

the renormalization is a quadratic term , regardless of whether or not the original model contained such a term .

generalizing the same ansatz , formulas have been given in for the asymptotic velocity and gaussian fluctuations of parallel-chain models and divided-pathway models .

generalizing the same ansatz , formulas have been given in for the asymptotic velocity and gaussian fluctuations of parallel-chains models and divided-chains models .

deep convolutional neural networks have been successfully applied to a wide range of image classification tasks .

convolutional neural networks have improved the performance of image classification significantly .

more precisely , this low energy theory is a special case of noncommutative dipole theory we introduced and studied in previous sections .

the low energy theory is a u gauge theory with electrically charged matter , and flows to a free theory in the infrared limit .

action sets , automata and comparisons form what is known as a discrete cartesian bicategory .

one would like to say this makes form what is called a bicategory .

this may be also spelled out that vacuum is a most degenerate state of a system .

thus , this vacuum is a true minimum of the full theory .

since then , depth prediction has evolved from simple handcrafted feature representations .

since then , depth estimation has evolved from using simple handcrafted feature representations .