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Research Project: Sparse Neural Networks


Training sparse artificial neural networks from scratch provides not only computational benefits contributing to greener AI, but often allows to achieve better generalisation than dense training. Since 2018 we witnessed the potential of SNN in a variety of learning scenarios and NN architectures, including but not limited to CNNs, LSTMs, GNNs, continual learning, and reinforcement learning. 

See also SNN Workshop series.

Principal Investigator
Decebal Mocanu
Involved members:
Mykola Pechenizkiy
Bram Grooten
Tianjin Huang
Ghada Sokar
Qiao Xiao
Lu Yin