NeurIPS 2015

Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection

NeurIPS 2015 seung-lab/znn-release

Efforts to automate the reconstruction of neural circuits from 3D electron microscopic (EM) brain images are critical for the field of connectomics.


Learning both Weights and Connections for Efficient Neural Networks

NeurIPS 2015 dorlivne/Pruning_RL

On the ImageNet dataset, our method reduced the number of parameters of AlexNet by a factor of 9x, from 61 million to 6.7 million, without incurring accuracy loss.