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Found 3 papers, 1 papers with code
Geometry-Informed Neural Networks
We introduce the concept of geometry-informed neural networks (GINNs), which encompass (i) learning under geometric constraints, (ii) neural fields as a suitable representation, and (iii) generating diverse solutions to under-determined systems often encountered in geometric tasks.
Variational Annealing on Graphs for Combinatorial Optimization
Several recent unsupervised learning methods use probabilistic approaches to solve combinatorial optimization (CO) problems based on the assumption of statistically independent solution variables.
Implicit recurrent networks: A novel approach to stationary input processing with recurrent neural networks in deep learning
The brain cortex, which processes visual, auditory and sensory data in the brain, is known to have many recurrent connections within its layers and from higher to lower layers.
