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Found 6 papers, 1 papers with code
Sparse Training Theory for Scalable and Efficient Agents
A fundamental task for artificial intelligence is learning.
Short Term Electricity Market Designs: Identified Challenges and Promising Solutions
In this regard, a future-proof market design must combine different elements of proposed solutions to comprehensively mitigate market barriers and overcome the identified implementation challenges.
On-line Building Energy Optimization using Deep Reinforcement Learning
Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure.
Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science
Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods.
Energy Disaggregation for Real-Time Building Flexibility Detection
Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid.
A topological insight into restricted Boltzmann machines
Thirdly, we show that, for a fixed number of weights, our proposed sparse models (which by design have a higher number of hidden neurons) achieve better generative capabilities than standard fully connected RBMs and GRBMs (which by design have a smaller number of hidden neurons), at no additional computational costs.
