Search Results for author:
Found 28 papers, 5 papers with code
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.
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}.
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.
Large-scale image segmentation based on distributed clustering algorithms
Many approaches to 3D image segmentation are based on hierarchical clustering of supervoxels into image regions.
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.
Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy
Neural circuits can be reconstructed from brain images acquired by serial section electron microscopy.
Chunkflow: Distributed Hybrid Cloud Processing of Large 3D Images by Convolutional Nets
The tasks are executed by local and cloud GPUs and CPUs.
Synaptic Partner Assignment Using Attentional Voxel Association Networks
The network takes the local image context and a binary mask representing a single cleft as input.
Siamese Encoding and Alignment by Multiscale Learning with Self-Supervision
We show that siamese encoding enables more accurate alignment than the image pyramids of SPyNet, a previous deep learning approach to coarse-to-fine alignment.
PZnet: Efficient 3D ConvNet Inference on Manycore CPUs
Convolutional nets have been shown to achieve state-of-the-art accuracy in many biomedical image analysis tasks.
Reconstructing neuronal anatomy from whole-brain images
Reconstructing multiple molecularly defined neurons from individual brains and across multiple brain regions can reveal organizational principles of the nervous system.
Convergence of gradient descent-ascent analyzed as a Newtonian dynamical system with dissipation
A previous convergence theorem due to Kose and Uzawa required the payoff function to be convex in the descent variables, and concave in the ascent variables.
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
+3
Two "correlation games" for a nonlinear network with Hebbian excitatory neurons and anti-Hebbian inhibitory neurons
A combination of Legendre and Lagrangian duality yields a zero-sum continuous game between excitatory and inhibitory connections that is solved by the neural network.
Unsupervised learning by a nonlinear network with Hebbian excitatory and anti-Hebbian inhibitory neurons
The parameters of competition between I-E connections can be adjusted to set the typical decorrelatedness and sparsity of E neuron activity.
An Error Detection and Correction Framework for Connectomics
Both tasks take as input the raw image and a binary mask representing a candidate object.
Superhuman Accuracy on the SNEMI3D Connectomics Challenge
For the past decade, convolutional networks have been used for 3D reconstruction of neurons from electron microscopic (EM) brain images.
Deep Learning Improves Template Matching by Normalized Cross Correlation
Template matching by normalized cross correlation (NCC) is widely used for finding image correspondences.
A correlation game for unsupervised learning yields computational interpretations of Hebbian excitation, anti-Hebbian inhibition, and synapse elimination
To provide computational interpretations of these aspects of synaptic plasticity, we formulate unsupervised learning as a zero-sum game between Hebbian excitation and anti-Hebbian inhibition in a neural network model.
Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks
Calcium imaging is an important technique for monitoring the activity of thousands of neurons simultaneously.
ZNNi - Maximizing the Inference Throughput of 3D Convolutional Networks on Multi-Core CPUs and GPUs
Other things being equal, processing a larger image tends to increase throughput, because fractionally less computation is wasted on the borders of the image.
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction
Efforts to automate the reconstruction of neural circuits from 3D electron microscopic (EM) brain images are critical for the field of connectomics.
ZNN - A Fast and Scalable Algorithm for Training 3D Convolutional Networks on Multi-Core and Many-Core Shared Memory Machines
Applying Brent's theorem to the task dependency graph implies that linear speedup with the number of processors is attainable within the PRAM model of parallel computation, for wide network architectures.
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection
Efforts to automate the reconstruction of neural circuits from 3D electron microscopic (EM) brain images are critical for the field of connectomics.
Image Segmentation by Size-Dependent Single Linkage Clustering of a Watershed Basin Graph
We present a method for hierarchical image segmentation that defines a disaffinity graph on the image, over-segments it into watershed basins, defines a new graph on the basins, and then merges basins with a modified, size-dependent version of single linkage clustering.
Algorithms for Non-negative Matrix Factorization
Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data.
Learning Generative Models with the Up Propagation Algorithm
Up-propagation is an algorithm for inverting and learning neural network generative models Sensory input is processed by inverting a model that generates patterns from hidden variables using topdown connections The inversion process is iterative utilizing a negative feedback loop that depends on an error signal propagated by bottomup connections The error signal is also used to learn the generative model from examples The algorithm is benchmarked against principal component analysis in experiments on images of handwritten digits.
