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Found 9 papers, 0 papers with code
DDGM: Solving inverse problems by Diffusive Denoising of Gradient-based Minimization
A recent trend is to train a convolutional net to denoise images, and use this net as a prior when solving the inverse problem.
Stretched sinograms for limited-angle tomographic reconstruction with neural networks
We compare our method to using a U-Net to directly reconstruct the unstretched tilt views and show that this simple stretching procedure leads to significantly better reconstructions.
Stacked unsupervised learning with a network architecture found by supervised meta-learning
We regard such dependence of unsupervised learning on prior knowledge implicit in network architecture as biologically plausible, and analogous to the dependence of brain architecture on evolutionary history.
Sensitivity of sparse codes to image distortions
We show empirically with the MNIST dataset that sparse codes can be very sensitive to image distortions, a behavior that may hinder invariant object recognition.
Kernel similarity matching with Hebbian neural networks
Recent works have derived neural networks with online correlation-based learning rules to perform \textit{kernel similarity matching}.
Online approximate factorization of a kernel matrix by a Hebbian neural network
Minimizing this upper bound leads to a minimax optimization, which can be solved via stochastic gradient descent-ascent.
Learning and Segmenting Dense Voxel Embeddings for 3D Neuron Reconstruction
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.
Sample Variance Decay in Randomly Initialized ReLU Networks
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.
Learning Metric Graphs for Neuron Segmentation In Electron Microscopy Images
Both empirically and theoretically, it is unclear whether or when deep metric learning is superior to the more conventional approach of directly predicting an affinity graph with a convolutional net.
Electron Microscopy Image Segmentation
Image Segmentation
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