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Found 6 papers, 5 papers with code
Adapting to time: why nature evolved a diverse set of neurons
Evolution has yielded a diverse set of neurons with varying morphologies and physiological properties that impact their processing of temporal information.
Dynamics of specialization in neural modules under resource constraints
It has long been believed that the brain is highly modular both in terms of structure and function, although recent evidence has led some to question the extent of both types of modularity.
Sparse Spiking Gradient Descent
There is an increasing interest in emulating Spiking Neural Networks (SNNs) on neuromorphic computing devices due to their low energy consumption.
Ranked #4 on
Image Classification
on N-MNIST
Comments on "Towards Unambiguous Edge Bundling: Investigating Confluent Drawings for Network Visualization"
Bach et al. [1] recently presented an algorithm for constructing confluent drawings, by leveraging power graph decomposition to generate an auxiliary routing graph.
Computational Geometry
Graph Drawing by Stochastic Gradient Descent
A popular method of force-directed graph drawing is multidimensional scaling using graph-theoretic distances as input.
Computational Geometry
High-dimensional cluster analysis with the Masked EM Algorithm
Cluster analysis faces two problems in high dimensions: first, the `curse of dimensionality' that can lead to overfitting and poor generalization performance; and second, the sheer time taken for conventional algorithms to process large amounts of high-dimensional data.
