GMRF MCVAE embeds Gaussian Markov Random Fields into VAE prior and posterior distributions to explicitly model cross-component relationships, reporting SOTA results on a synthetic Copula dataset and improved coherence on BIKED.
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A TensorFlow-based deep auto-encoder model is proposed for short-term electric load forecasting and claimed to outperform traditional neural networks in accuracy and stability.
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Multi-Component VAE with Gaussian Markov Random Field
GMRF MCVAE embeds Gaussian Markov Random Fields into VAE prior and posterior distributions to explicitly model cross-component relationships, reporting SOTA results on a synthetic Copula dataset and improved coherence on BIKED.
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Short-term Electric Load Forecasting Using TensorFlow and Deep Auto-Encoders
A TensorFlow-based deep auto-encoder model is proposed for short-term electric load forecasting and claimed to outperform traditional neural networks in accuracy and stability.