ライフサイエンスコーパス: generative

1 ining predictive ability as our technique is generative.

2 titudes that, I argue, provides a useful and generative account of human social behavior.

3 l validity rewards, we have introduced novel generative adversarial award, being generalized sliced W

4 al analysis proximal sensing method based on generative adversarial nets (GAN) is proposed, named as

5 amed as outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN).

6 ion-correction approach aided by conditional generative adversarial network (cGAN) methodology that a

7 This study suggests an attention-gated generative adversarial network (DoseGAN) to improve lear

8 nner, we propose a deep learning conditional generative adversarial network (GAN) capable of producin

9 present a deep learning framework based on a generative adversarial network (GAN) to perform super-re

10 -spread-function, and is based on training a generative adversarial network (GAN) to transform diffra

11 eved superior results compared with U-Net, a generative adversarial network (GAN), and a cycle-consis

12 The equalization method is trained as a generative adversarial network (GAN), using the StarGAN

13 se of Residual-in-Residual Dense Blocks in a Generative Adversarial Network (GAN).

14 linical practice, we also integrated a cycle generative adversarial network into our pipeline to tran

15 Then, we use a U-net structured Generative Adversarial Network to reduce the residual ar

16 as the inputs for the training data set of a generative adversarial network.

17 ing electron microscopy (SEM) images using a generative adversarial network.

18 We propose utilizing generative adversarial networks (GAN) to spawn ensembles

19 Here, we propose the generative adversarial networks (GANs) for scRNA-seq imp

20 Generative Adversarial Networks (GANs) provide one such

21 Generative adversarial networks (GANs) utilize adversari

22 An ensemble of generative adversarial networks (GANs) was trained on TD

23 ation problem and propose to use conditional generative adversarial networks for tackling it.

24 Generative adversarial networks offer a novel method for

25 tion of this framework, based on conditional generative adversarial networks, to automatically identi

26 al autoencoder-based framework, a variant of generative adversarial networks.

27 ng, we have developed a two-stage multiscale generative adversarial neural network to complete realis

28 convolutional neural network trained using a generative adversarial-network model can transform wide-

29 We propose a generative algorithm GENESCs for SCs modeling fundamenta

30 r determining fluorescence brightness with a generative algorithm, the probability of selecting DNA t

31 antly outperforms two other state-of-the-art generative algorithms ([Formula: see text] and [Formula:

32 inst a variety of alternative geometries and generative algorithms.

33 The best-performing models were a generative analysis-by-synthesis model (based on variati

34 This paper presents a novel approach for Generative Anatomy Modeling Language (GAML).

35 other implantable materials for better bone-generative and -regenerative potential.

36 ised machine learning methods, as well as of generative and discriminative modelling approaches.

37 ctor, Nanog is expressed within the striatal generative and mantle regions, and in Hdh-Q111 embryos t

38 velopment, a property that may have played a generative and regulatory role throughout the evolution

39 hift, suggesting that this is a particularly generative and revolutionary time to be studying decisio

40 he community that allow for the inference of generative and selection models from high-throughput seq

41 In wild-type plants, generative and sperm nuclei enter S phase soon after inc

42 i were produced with no differentiation into generative and vegetative cells.

43 al role in the photoperiodic control of both generative and vegetative growth in strawberry.

44 of Segal and Sharan as an alternative to the generative approach of Reiss and Schwikowski.

45 roviding these novel annotations, we built a generative approach to modeling of punctate distribution

46 (Simulation using Best-fit Algorithm) a non-generative approach, based on a combination of stochasti

47 This work focuses on supervised generative binary classification models, specifically li

48 repertoire, we show that the reduced B-cell generative capacity of "aged" long-term reconstituting h

49 mor incidence is determined by the life-long generative capacity of mutated cells.

50 r laboratory-cultured cells retain a similar generative capacity remains unknown.

51 h CB, adult BM shows a reduction of in vitro generative capacity that is progressively more profound

52 ion to their capacity for extensive in vitro generative capacity, the human CD45(+)/CD34(-) cells rec

53 0-fold reduction in IL-7-dependent B lineage generative capacity.

54 king system that rationally infers different generative causes of motion signals.

55 motion dynamics, effectively inferring their generative causes.

56 e nucleus but dynamically accumulates in the generative cell (the progenitor of sperm) to promote the

57 e, which led to failures in the birth of the generative cell and, subsequently, the sperm.

58 While structural features of the generative cell are well documented, genetic programs re

59 eover, conditional knockdown of ARID1 in the generative cell causes reduced heterochromatic silencing

60 ll documented, genetic programs required for generative cell cycle progression are unknown.

61 progression through different phases of the generative cell cycle, providing the first evidence to o

62 s I (PMI) but fail at distinct stages of the generative cell cycle.

63 len1 (duo1) and duo pollen2 (duo2), in which generative cell division is blocked, resulting in the fo

64 in the sperm cells and not in the progenitor generative cell or in the vegetative cell, but it is als

65 Loss of HAPLESS 2/GENERATIVE CELL SPECIFIC 1 (HAP2/GCS1) proteins results

66 lants is initiated with the formation of the generative cell that is the progenitor of the two sperm

67 involved in the accumulation of ARID1 in the generative cell, and found that AGO9, a germline-specifi

68 nly in the sperm cells and in the progenitor generative cell, but not in the vegetative cell or in ot

69 equired for the accumulation of ARID1 in the generative cell.

70 len, suggesting that SLF1 is specific to the generative cell.

71 uction of the genome before the formation of generative cells and promotes the exchange of genetic in

72 n regulating the transcriptional programs of generative cells and the zygote.

73 Mutant generative cells in duo1 pollen fail to enter mitosis at

74 r mitosis at G2-M transition, whereas mutant generative cells in duo2 enter PMII but arrest at promet

75 ty reveals that brain function is encoded in generative changes to cells that compete with destructiv

76 Prelife is a generative chemistry that proliferates information and p

77 In this issue, Ponce and colleagues use a generative closed-loop system to evolve synthetic images

78 uild the brain to be capable of flexible and generative cognition?

79 argue that inclusion of impairments in more generative cognitive processes is necessary for complete

80 For the generative component, we use a spike and slab prior over

81 nology, syntax, and semantics as independent generative components whose structures are linked by int

82 (e.g. genes) by a hybrid of conditional and generative components.

83 We here propose a generative copula-based method that can accurately estim

84 al cortex via the vast hypothesis space of a generative deep neural network, avoiding assumptions abo

85 Our approach is based on a generative deep-learning model: the long-short-term memo

86 hin and between states enables a compact and generative description of the conformational landscape a

87 o neurons in primary motor cortex encode the generative details of motor behavior, such as individual

88 at regulates yearly cycles of vegetative and generative development through separate genetic pathways

89 ral sclerosis (ALS) is a progressive neurode-generative disease characterized by motor neuron death.

90 ce encourages students' active, exploratory, generative engagement.

91 We present GERV (generative evaluation of regulatory variants), a novel c

92 The generative event statistics are consistent between indiv

93 infer the joint distribution of the various generative events that occur when a new T-cell receptor

94 We characterize the generative fields (GFs) inferred by MCA with respect to

95 e hypothesis that hybridization can act as a generative force in macroevolutionary diversification.

96 to teach modern psychiatry and can be a more generative forefather than the icon created by the neo-K

97 SCALE combines a deep generative framework and a probabilistic Gaussian Mixtur

98 y, our results have validated a powerful and generative framework for synthesizing human neuroimaging

99 et the results of previous research within a generative framework.

100 re of grammar that contrasts with mainstream generative grammar (MGG) in that (a) it treats phonology

101 que computer graphics platform that combines generative grammars with visual perception, we accessed

102 ility through the use of syntactic rules (or generative grammars) that describe the acceptable struct

103 In this paper, we propose a conditional deep generative graph neural network that learns an energy fu

104 To shift from vegetative to generative growth, they require a chilling phase known a

105 in Arabidopsis thaliana, specifically during generative growth.

106 To address these problems, we introduce a generative hierarchical model of synchronous activity to

107 Unlike the traditional generative HMM, discriminative HMM can use examples from

108 We complement this discussion by exploring generative implicit CP self-models, arguably emerging du

109 oders) for MNIST and a hybrid discriminative-generative joint energy model for CIFAR-10.

110 hod for poly(A) motif prediction by marrying generative learning (hidden Markov models) and discrimin

111 Generative learning provides a rich palette on which the

112 es loaded with piRNAs derived from annotated generative loci, which are normally restricted to either

113 nverse design problem in a probabilistically generative manner, enabling to elegantly investigate the

114 Surprisingly, there exists no simple generative mechanism explaining all the three properties

115 This generative mechanism for co-evolution of individual beha

116 tion, development and plasticity, provides a generative mechanism for functional diversity among cort

117 ngement signatures associated with an fCNA's generative mechanism, AR enables a more thorough underst

118 es with enhanced neurotoxicity, although the generative mechanisms and the implications for disease t

119 recognized for well over a century, but the generative mechanisms are still debated vigorously.

120 An improved understanding of the generative mechanisms behind the mutation rules and thei

121 Although it is not yet clear whether the generative mechanisms for human and animal behavior will

122 Generative mechanisms for this architecture have been di

123 and pathways, as a proxy for the underlying generative mechanisms inducing comorbidity.

124 hese findings impart novel insights into the generative mechanisms of metacognition.

125 ket fluctuations, suggesting system-specific generative mechanisms.

126 spects of HSC biology and applications in re-generative medicine.

127 HSC generation were involved in the AGM HSC-generative microenvironment.

128 arries both the semantics of a probabilistic generative model and of a neural network.

129 On a data-driven basis, the proposed deep generative model can serve as a comprehensive and effici

130 Moreover, we compared this model with a generative model combining spatial distance and topologi

131 We describe a generative model for documents, introduced by Blei, Ng,

132 We have developed the first deep generative model for drug combination design, by jointly

133 We propose a generative model for NGS data derived from multiple subs

134 combines a previously proposed hierarchical generative model for oxi-mC-seq data and a general linea

135 We introduce a probabilistic generative model for protein localization, and develop a

136 In this paper, we introduce a simple generative model for the collective behavior of millions

137 t can facilitate the inference of the proper generative model for the task at hand, unlike perceptual

138 s neuroscience, we introduce a probabilistic generative model for vision in which message-passing-bas

139 could be taken as indication that there is a generative model in the brain, specifically one that can

140 lend further credence to the hypothesis of a generative model in the brain.

141 is designed to be extensible by dividing the generative model into modules, each of which is expresse

142 ations of the axonal branching patterns, the generative model is ported into the physically accurate

143 We have developed a generative model of binding sites in ChIP-chip data and

144 We tested a detailed generative model of cortical microcircuits that accurate

145 A generative model of dendrite growth based on competitive

146 We describe a generative model of differentiation-associated changes i

147 We then build a generative model of discovery informed by qualitative re

148 d enrichment analysis application based on a generative model of gene activation.

149 ks using graph theoretical indices and use a generative model of network growth to explore mechanisms

150 ge, we illustrate to our knowledge the first generative model of noise correlations that is consisten

151 arized discriminative model outperformed the generative model of Reiss and Schwikowski in terms of th

152 model selection to identify the most likely generative model of subjects' behavior and found that at

153 movement timing, we incorporate both into a generative model of swimming.

154 In this paper, a stochastic generative model of the metabolic system is developed.

155 activity patterns as a hyper-parameter in a generative model of the neural data.

156 the posterior probability distribution of a generative model of the read data.

157 composed of these elementary features, and a generative model of this process reproduces individual b

158 GERV learns a k-mer-based generative model of transcription factor binding from Ch

159 This is achieved using a hierarchical generative model that aims to minimize prediction error

160 In other states, the theory is a generative model that constructs a sensory representatio

161 We then developed a generative model that explains real-time pursuit traject

162 ving movements are based upon a hierarchical generative model that infers the context in which moveme

163 transition probabilities over time yields a generative model that recapitulates the key features of

164 We then use a novel generative model to create a detailed semantic atlas.

165 TADA, uses available data and a statistical generative model to create new samples augmenting existi

166 erein, a deep neural network is trained as a generative model to direct the relationship between CO(2

167 ls, the VKF gives up some flexibility in the generative model to enable a more faithful approximation

168 We present a generative model to estimate the propensity of evolving

169 We demonstrate the use of a probabilistic generative model to explore the biomarker changes occurr

170 that auditory cortex continuously updates a generative model to predict its sensory inputs.

171 FAVITES creates a generative model to produce contact networks, transmissi

172 Here, we develop a generative model to produce undirected, simple, connecte

173 We used a simple generative model to synthesize novel sounds with natural

174 Further optimization focuses the generative model toward function in a specific genomic c

175 A generative model was trained on the unlabeled data set,

176 dels based on Bayesian inference in a linear generative model), for concreteness we demonstrate the a

177 tempt to encompass the wisdom of crowds in a generative model, but posit that a successful attempt at

178 ally, we classify artificial images from the generative model, interacting self-propelled particle mo

179 We present a generative model, Lux, to quantify DNA methylation modif

180 Within a generative model, we investigate growth rules for axonal

181 imple linear regression model and a Bayesian generative model, we show that most songs of the hermit

182 This behavior is captured by a generative model, whereby a reward-driven modulatory sig

183 tive sorting is modelled around hypothesized generative model, which addresses the natural phenomena

184 igurations are proposed and scored against a generative model, which assumes Gaussian clusters overla

185 hat the phenome can be further analyzed by a generative model, which can discover probabilistic assoc

186 gical features of the real connectome as the generative model, yet better explained the connectivity

187 Using fMRI in combination with a generative model-based analysis, we found that probabili

188 ortion of connections (56%) explained by the generative model.

189 A in the form of a two-layer latent variable generative model.

190 any system whose input can be captured by a generative model.

191 at the brain makes use of an internal (i.e., generative) model to make inferences about the causes of

192 on of collective behavior using methods from generative modeling and nonlinear manifold learning.

193 how model selection in such probabilistic or generative modeling can facilitate analysis of closely r

194 We use graph analysis and generative modeling to show that the transition between

195 We propose, following Clark, that generative models also play a central role in the percep

196 we use factor graphs to show the form of the generative models and of the messages they entail.

197 ayesian cognitive processes and hierarchical generative models as discussed by the target article.

198 ional methods and deep autoencoders to build generative models for cell shapes in terms of the accura

199 Here we consider generative models for the probability of a functional co

200 Results also show that the deep generative models generate drug combinations following t

201 of these methods in terms of the accuracy of generative models has been limited.

202 However, generative models need not be veridical representations

203 actical approach to designing more realistic generative models of information diffusion.

204 dels (DCM) of cortical network responses, as generative models of magnetoencephalographic observation

205 rm oscillatory codes for critical aspects of generative models of perception.

206 top-down influences and the plausibility of generative models of sensory brain function.

207 e with the mechanistic precision afforded by generative models of the brain.

208 cribe perceptual processes based on accurate generative models of the world.

209 in sequences without relying on family-based generative models such as hidden Markov models.

210 ple levels of the visual hierarchy, based on generative models that differentiate between signals tha

211 ative representations function as high-level generative models that direct our attention and structur

212 y between cortical sources using a set of 21 generative models that embedded alternate hypotheses of

213 function can be conceived as a hierarchy of generative models that optimizes predictions of sensory

214 We compared 20 generative models that represented alternative interacti

215 neuronal rather than vascular level, we used generative models that specified both the neural interac

216 suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of

217 ational drug-combination design has not seen generative models to meet its potential to accelerate re

218 he group structure and motion via a class of generative models to position each particle on a two-dim

219 functional contribution to the hierarchical generative models underlying speech perception.

220 forward models in motor control are not the generative models used in perceptual inference.

221 s and strategies in this regard, focusing on generative models which infer mechanistically interpreta

222 view mechanistic network models as implicit generative models whose parameters can be optimized to f

223 e learning provide ways for fitting implicit generative models without the need to evaluate the likel

224 These approaches-biophysical network models, generative models, and model-based fMRI analyses of neur

225 Hierarchical generative models, such as Bayesian networks, and belief

226 Generative models, such as predictive coding, posit that

227 form inference using Hidden Markov Models as generative models.

228 y inferences proceed without detailed causal generative models.

229 of interactions, resulting in less accurate generative models.

230 tificial networks created by several popular generative models.

231 e regression and corresponding probabilistic generative models.

232 ue is identifying discriminative rather than generative motifs.

233 The generative nature of recollection allows us to represent

234 eval method based on conditional variational generative network (CVGN) that can address both demands.

235 cterization of this connectome and propose a generative network model which utilizes two elemental or

236 Using a generative network model, we show that these biases depe

237 he human brain as feedback in a hierarchical generative network.

238 al imagery to the computational abilities of generative networks.

239 In this paper, we explore unsupervised generative neural methods, based on the variational auto

240 tion for activation maximization) combined a generative neural network and a genetic algorithm in a c

241 ional architecture of the brain but also its generative niches for resident progenitors - glial as we

242 dimensional discriminative model to encode a generative noise process-is generally applicable to othe

243 Mutant generative nuclei in duo1 complete DNA synthesis but byp

244 However, mutant generative nuclei in duo2 arrest in prometaphase of PMII

245 sis results in the accumulation of Bs in the generative nucleus and therefore ensures their transmiss

246 n and are continually replaced by input from generative organs.

247 aking the rTCA cycle special limits possible generative outcomes, there are many unrealized compounds

248 However, the Bayesian approach rejected the generative parameter values significantly less often tha

249 Although their generative pathway remains undefined, tolerance promotin

250 d in a longer growing season, but a constant generative period in wild plants and a shortened one in

251 ehavior of source and sink organs during the generative phase of the barley (Hordeum vulgare) plant.

252 cture and a potential early step towards the generative phonemic system of human language.

253 ids preferentially become located toward the generative pole.

254 response may reflect the enlargement of this generative pool by the transient repression of IL-2-medi

255 points of contention and that highlight the generative potential of our model.

256 's specific assertions, which may hinder the generative potential of their model.

257 infinite set of outputs ("grammars") vary in generative power.

258 y with positional priors in the context of a generative probabilistic model of ChIP data and genome s

259 ch to RNA multiple alignment which couples a generative probabilistic model of sequence and structure

260 To overcome these problems we developed a generative probabilistic model which identifies a (small

261 We present a generative, probabilistic model of RNA polymerase that f

262 Here, we develop a novel generative, probabilistic model that simultaneously capt

263 M) that merges a Potts emission process to a generative probability model of insertion and deletion.

264 y plausible, but incorrect beliefs about the generative process for outcomes and (ii) human behavior

265 based learning, the agent seeks to learn the generative process for outcomes from which the value of

266 nsity and societal interaction, we propose a generative process for the evolution of social structure

267 s of predictive coding frame perception as a generative process in which expectations constrain senso

268 ollectively, these results point to a common generative process that is conserved across species, sug

269 be understood as an exploratory space and a generative process that leads to different, and sometime

270 mathematical structure suggested a unifying generative process, as reflected in the title of the boo

271 have "grown" from a skeleton by a stochastic generative process.

272 The deep homology of generative processes and cell-type specification mechani

273 lour and pattern variants to investigate the generative processes of skin colour patterning shared am

274 y the presence of cues suggestive of various generative processes, despite statistically identical ou

275 f movement pattern data, linking patterns to generative processes.

276 pture the relevant aspects of (hypothetical) generative processing in the cortex.

277 ne a model-derived approximation of the true generative regressor.

278 However, scaling up generative replay to complicated problems with many task

279 s, such memory replay can be implemented as 'generative replay', which can successfully - and surpris

280 tions clarifies its important, exciting, and generative role in scientific progress.

281 The same generative rules have been found in vertebrate locomotor

282 We characterize and quantify the generative rules that shape Drosophila locomotor behavio

283 plication is centriole replication, which is generative, semiconservative, and independent of the nuc

284 Hence, liver and spleen are generative sites of B cell responses, and they include V

285 We present a generative statistical model and associated inference me

286 Here we describe and validate a generative statistical model that accurately quantifies

287 spond to the spectral characteristics of the generative stimuli.

288 Prelife is a generative system that can produce information.

289 coma, with the additional benefit of being a generative technique capable of predicting future patter

290 erms of Bayesian computations and provides a generative testing ground for future work.

291 currently missing, we argue, is a positive, generative thesis about associative learning mechanisms

292 e differentially expressed in vegetative and generative tissues undergoing PCD.

293 ted stochastic neighbor embedding (t-SNE) or generative topographic mapping (GTM) can be used to prov

294 ical correlations, possibility of non-normal generative variables, as well as ease of interpretation

295 Purpose To examine Generative Visual Rationales (GVRs) as a tool for visual

296 d by neural networks can be identified using Generative Visual Rationales, enabling detection of bias

297 ing empathy and IER provides a synthetic and generative way to ask new questions about how social emo

298 ability and occurrence frequency fitted by a generative whole-brain model, fine-tuned on the basis of

299 ers for the topologies of rachidial and barb generative zones (setting vane boundaries), respectively

300 of proliferating cells was found within the generative zones.