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Found 4 papers, 0 papers with code
Probabilistic Forecasting with Coherent Aggregation
Our model uses a convolutional neural network to produce parameters for the factors, their loadings and base-level distributions; it produces samples which can be differentiated with respect to the model's parameters; and it can therefore optimize for any sample-based loss function, including the Continuous Ranked Probability Score and quantile losses.
Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures
Hierarchical forecasting problems arise when time series have a natural group structure, and predictions at multiple levels of aggregation and disaggregation across the groups are needed.
PatentNet: A Large-Scale Incomplete Multiview, Multimodal, Multilabel Industrial Goods Image Database
In deep learning area, large-scale image datasets bring a breakthrough in the success of object recognition and retrieval.
Pseudo 3D Auto-Correlation Network for Real Image Denoising
The extraction of auto-correlation in images has shown great potential in deep learning networks, such as the self-attention mechanism in the channel domain and the self-similarity mechanism in the spatial domain.
