386 citations · 438 across the 11 of their papers we have counts for
11 papers
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…
Stretched sinograms for limited-angle tomographic reconstruction with neural networks
Kyle Luther, Sebastian Seung
We present a direct method for limited angle tomographic reconstruction using convolutional networks. The key to our method is to first stretch every tilt view in the direction per…
Stacked unsupervised learning with a network architecture found by supervised meta-learning
Kyle Luther, H. Sebastian Seung
Stacked unsupervised learning (SUL) seems more biologically plausible than backpropagation, because learning is local to each layer. But SUL has fallen far short of backpropagation…
Kernel similarity matching with Hebbian neural networks
Kyle Luther, H. Sebastian Seung
Recent works have derived neural networks with online correlation-based learning rules to perform \textit{kernel similarity matching}. These works applied existing linear similarit…
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…
The HST See Change Program: I. Survey Design, Pipeline, and Supernova Discoveries
Brian Hayden, David Rubin, Kyle Boone +53
The See Change survey was designed to make cosmological measurements by efficiently discovering high-redshift Type Ia supernovae (SNe Ia) and improving cluster mass measureme…
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…
Sample Variance Decay in Randomly Initialized ReLU Networks
Kyle Luther, H. Sebastian Seung
Before training a neural net, a classic rule of thumb is to randomly initialize the weights so the variance of activations is preserved across layers. This is traditionally interpr…
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…
The Discovery of a Gravitationally Lensed Supernova Ia at Redshift 2.22
David Rubin, Brian Hayden, Xiaosheng Huang +32
We present the discovery and measurements of a gravitationally lensed supernova (SN) behind the galaxy cluster MOO J1014+0038. Based on multi-band Hubble Space Telescope and Very L…
Characterizing transiting exoplanet atmospheres with JWST
Thomas P. Greene, Michael R. Line, Cezar Montero +3
We explore how well James Webb Space Telescope (JWST) spectra will likely constrain bulk atmospheric properties of transiting exoplanets. We start by modeling the atmospheres of ar…