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Found 4 papers, 1 papers with code
Form follows Function: Text-to-Text Conditional Graph Generation based on Functional Requirements
This work focuses on the novel problem setting of generating graphs conditioned on a description of the graph's functional requirements in a downstream task.
A Hybrid Model for Forecasting Short-Term Electricity Demand
Currently the UK Electric market is guided by load (demand) forecasts published every thirty minutes by the regulator.
Deep Incremental Boosting
This paper introduces Deep Incremental Boosting, a new technique derived from AdaBoost, specifically adapted to work with Deep Learning methods, that reduces the required training time and improves generalisation.
Adapting Resilient Propagation for Deep Learning
The Resilient Propagation (Rprop) algorithm has been very popular for backpropagation training of multilayer feed-forward neural networks in various applications.
