Since the deep learning models compared in [20] are based on Convolutional Neural Networks, we additionally compare our model with MV-LSTM [25] and a simple LSTM baseline in our experimental evaluation.
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).```
Sample intent: result
Sample text:```NP3L [10] is consistently outperformed by our methods, while for UPnP [8] our NPnLupC outperforms it in the noisy cases.```
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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).```
Sample intent: result



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:```…(LING)] thalamus (THA), as well as limbic/paralimbic regions [e.g. insula, superior temporal pole and inferior orbitofrontal cortex] (Table 2), which is in accordance with previous findings on functional brain networks constructed using resting-state fMRI (Achard et al., 2006; He et al., 2009b).```
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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:```Note that in their pioneering paper Calvet and Fisher (2002) proposed the simplified version of the construction of Mannersalo et al (2002).```
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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:```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:```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:```NP3L [10] is consistently outperformed by our methods, while for UPnP [8] our NPnLupC outperforms it in the noisy cases.```
Sample intent: background



Sample text:```…(LING)] thalamus (THA), as well as limbic/paralimbic regions [e.g. insula, superior temporal pole and inferior orbitofrontal cortex] (Table 2), which is in accordance with previous findings on functional brain networks constructed using resting-state fMRI (Achard et al., 2006; He et al., 2009b).```
Sample intent: result



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).```
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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).```
Sample intent: result



Sample text:```To train our CNN for classification, we set initial learning rate to 0.01 and optimize cross-entropy loss with Adagrad [26].```
Sample intent: background