[12] identified a DCGAN architecture that resulted in stable GAN training on images of sizes 64 × 64 pixels.
Sample text:```Following past work, we show that such basis models can be learned from 2D annotations using techniques adapted from nonrigid structure from motion (SFM) [20, 19].```
Sample intent: method
Sample text:```From a strategic viewpoint, layered modular architectures have the competitive advantage, as well as the challenge, in being doubly distributed [57].```
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Sample text:```Following past work, we show that such basis models can be learned from 2D annotations using techniques adapted from nonrigid structure from motion (SFM) [20, 19].```
Sample intent: method



Sample text:```All MLP analyses [also known as back-propagation artificial neural networks (ANNs)] were carried out with a user-friendly neural network simulation program, NeuFrame version 3.0.0.0 (Neural Computer Sciences, Lulworth Business Centre, Totton, Southampton, UK), which runs under Microsoft Windows NT on an IBMcompatible personal computer.```
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Sample text:```Discrepancies analogous to those between our results and those of simulations have, indeed, recently been observed in the case of the binary perceptron [18], the pspin interaction spin glass [19], and in the case of fluctuating manifolds in random media [20], where dynamically frozen phases were observed in regions of parameter space in which static approaches yielded ergodic phases.```
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Sample text:```05, minimum cluster size 64 voxels) [Woo et al., 2014] for the primary analysis of regions showing increasing activity with distance from the home location.```
Sample intent: method
Sample text:```All MLP analyses [also known as back-propagation artificial neural networks (ANNs)] were carried out with a user-friendly neural network simulation program, NeuFrame version 3.0.0.0 (Neural Computer Sciences, Lulworth Business Centre, Totton, Southampton, UK), which runs under Microsoft Windows NT on an IBMcompatible personal computer.```
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Sample text:```The above estimator generalizes the estimator in Sriperumbudur et al., 2017 to conditional densities.```
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Sample text:```To train our CNN for classification, we set initial learning rate to 0.01 and optimize cross-entropy loss with Adagrad [26].```
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Sample text:```have been proposed as potential alternatives to the Turing Test, specifically targeting its potential weaknesses and inadequacy [1].```
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Sample text:```…methods rely on some additional and often heuristic step to generate an initial solution, such as adversarially-trained neural networks [Conneau et al., 2018, Zhang et al., 2017a,b], robust self-learning [Artetxe et al., 2018b] or
5This can be easily verified by noting that the…```
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Sample text:```In this study, we generalized previous results regarding the quasi-Gaussian shape and the scalar property using the SBF model [13,21,52].```
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Sample text:```Although the time-constants of the learning window used in this study were much shorter than those reported for hippocampal neurons by Bi and Poo [27] (viz.```
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Sample text:```Following past work, we show that such basis models can be learned from 2D annotations using techniques adapted from nonrigid structure from motion (SFM) [20, 19].```
Sample intent: method



Sample text:```Our results confi rm previous studies that reached the same conclusion (Boushey et al 1994; Vermeulen et al 2000; Till et al 2005; Marcucci et al 2005).```
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Sample text:```The present observations support and extend earlier contentions that Parkinson patients prefer to adopt a closedloop control mode, adjusting their movements continuously on the basis of available visual information (Flowers 1976).```
Sample intent: result