We consider the problem of training generative models with deep neural networks as generators, i.e. to map latent codes to data points. Whereas the dominant paradigm combines simple priors over codes with complex deterministic models, we propose instead to use more flexible code distributions. These distributions are estimated non-parametrically by reversing the generator map during training. T...

In the last lecture, we discussed a recursive construction of a pseudorandom generator that is based on expanders: the INW generator. In this lecture we will show that the INW-generator with a suitable instantiation of an expander family produces pseudorandomness for machines that run in logarithmic space. We also introduce Nisan’s generator, which is a pseudorandom generator that has a recursi...

This article presents an information retrieval system (IRS) using genetic algorithm to increase the performance and efficiency of Text to Matrix Generator (TMG).This paper presents an extension in previous work of Text to Matrix Generator (TMG). In this paper, we proposed a genetic algorithm approach in Text to Matrix Generator for information retrieval. This experimental result shows an improv...

Besides achieving an optimal scheduling generator, the operation safety of generator itself needs to be focused on. The development virtual visualization a capability curve simulation visualize condition is proposed in this paper. In paper, neural network applied redraw original generator’s curve. can simulate operating considering limitation constraints on various elements generator. Furthermo...

a 76-yr-old man with unipolar ventricular pacemaker, previously implanted in the right ventricular apex, presented with syncopal attack and twitching of the right pectoralis muscle. he finally was diagnosed as having twiddler's syndrome. the patient had a pulse rate of 36/min and cannon waves in the neck veins. chest radiography showed a dislodged pacing lead completely wrapped around the pulse...