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Found 7 papers, 5 papers with code
Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data
In this work, we propose two deep-learning-based regional forecasting methods that can effectively leverage both types of time series (aggregated and individual) with weather data in a region.
When To Grow? A Fitting Risk-Aware Policy for Layer Growing in Deep Neural Networks
Neural growth is the process of growing a small neural network to a large network and has been utilized to accelerate the training of deep neural networks.
GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations
In this work, we propose GINN-LP, an interpretable neural network to discover the form and coefficients of the underlying equation of a dataset, when the equation is assumed to take the form of a multivariate Laurent Polynomial.
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning
However, due to the lack of old data, NECIL methods struggle to discriminate between old and new classes causing their feature representations to overlap.
Incremental Learning
Non-exemplar-based Class Incremental Learning
+1
NAPA-VQ: Neighborhood-Aware Prototype Augmentation with Vector Quantization for Continual Learning
However, due to the lack of old data, NECIL methods struggle to discriminate between old and new classes causing their feature representations to overlap.
Incremental Learning
Non-exemplar-based Class Incremental Learning
+1
DALLE-URBAN: Capturing the urban design expertise of large text to image transformers
However, a detailed analysis capturing the capabilities of such models, specifically with a focus on the built environment, has not been performed to date.
Self Organizing Nebulous Growths for Robust and Incremental Data Visualization
In addition, SONG is capable of handling new data increments, no matter whether they are similar or heterogeneous to the already observed data distribution.
