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Found 3 papers, 2 papers with code
Data-Driven Modeling of Noise Time Series with Convolutional Generative Adversarial Networks
Namely, we assess two general-purpose GANs for time series that are based on the popular deep convolutional GAN (DCGAN) architecture, a direct time-series model and an image-based model that uses a short-time Fourier transform (STFT) data representation.
Feasibility of Modeling Orthogonal Frequency-Division Multiplexing Communication Signals with Unsupervised Generative Adversarial Networks
High-quality recordings of radio frequency (RF) emissions from commercial communication hardware in realistic environments are often needed to develop and assess spectrum-sharing technologies and practices, e. g., for training and testing spectrum sensing algorithms and for interference testing.
Improving on Q & A Recurrent Neural Networks Using Noun-Tagging
Often, more time is spent on finding a model that works well, rather than tuning the model and working directly with the dataset.
