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Found 6 papers, 2 papers with code
PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images
While most state-of-the-art instance segmentation methods produce binary segmentation masks, geographic and cartographic applications typically require precise vector polygons of extracted objects instead of rasterized output.
A dynamic connectome supports the emergence of stable computational function of neural circuits through reward-based learning
These data are inconsistent with common models for network plasticity, and raise the questions how neural circuits can maintain a stable computational function in spite of these continuously ongoing processes, and what functional uses these ongoing processes might have.
Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring
We reexamine in this article the conceptual and mathematical framework for understanding the organization of plasticity in spiking neural networks.
Network Plasticity as Bayesian Inference
General results from statistical learning theory suggest to understand not only brain computations, but also brain plasticity as probabilistic inference.
A theoretical basis for efficient computations with noisy spiking neurons
Furthermore, one can demonstrate for the Traveling Salesman Problem a surprising computational advantage of networks of spiking neurons compared with traditional artificial neural networks and Gibbs sampling.
Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints
Recent spiking network models of Bayesian inference and unsupervised learning frequently assume either inputs to arrive in a special format or employ complex computations in neuronal activation functions and synaptic plasticity rules.
