386 citations · 438 across the 11 of their papers we have counts for
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DDGM: Solving inverse problems by Diffusive Denoising of Gradient-based Minimization
Kyle Luther, H. Sebastian Seung
Inverse problems generally require a regularizer or prior for a good solution. A recent trend is to train a convolutional net to denoise images, and use this net as a prior when so…
Sensitivity of sparse codes to image distortions
Kyle Luther, H. Sebastian Seung
Sparse coding has been proposed as a theory of visual cortex and as an unsupervised algorithm for learning representations. We show empirically with the MNIST dataset that sparse c…
Learning and Segmenting Dense Voxel Embeddings for 3D Neuron Reconstruction
Kisuk Lee, Ran Lu, Kyle Luther +1
We show dense voxel embeddings learned via deep metric learning can be employed to produce a highly accurate segmentation of neurons from 3D electron microscopy images. A "metric g…
Learning Metric Graphs for Neuron Segmentation In Electron Microscopy Images
Kyle Luther, H. Sebastian Seung
In the deep metric learning approach to image segmentation, a convolutional net densely generates feature vectors at the pixels of an image. Pairs of feature vectors are trained to…