24 citations · 40 across the 3 of their papers we have counts for
8 papers
Learning Guided Electron Microscopy with Active Acquisition
Lu Mi, Hao Wang, Yaron Meirovitch +5
Single-beam scanning electron microscopes (SEM) are widely used to acquire massive data sets for biomedical study, material analysis, and fabrication inspection. Datasets are typic…
Teaching deep neural networks to localize single molecules for super-resolution microscopy
Artur Speiser, Lucas-Raphael Müller, Ulf Matti +5
Single-molecule localization fluorescence microscopy constructs super-resolution images by sequential imaging and computational localization of sparsely activated fluorophores. Acc…
Importance Weighted Adversarial Variational Autoencoders for Spike Inference from Calcium Imaging Data
Daniel Jiwoong Im, Sridhama Prakhya, Jinyao Yan +2
The Importance Weighted Auto Encoder (IWAE) objective has been shown to improve the training of generative models over the standard Variational Auto Encoder (VAE) objective. Here,…
Synaptic partner prediction from point annotations in insect brains
Julia Buhmann, Renate Krause, Rodrigo Ceballos Lentini +4
High-throughput electron microscopy allows recording of lar- ge stacks of neural tissue with sufficient resolution to extract the wiring diagram of the underlying neural network. C…
A Connectome Based Hexagonal Lattice Convolutional Network Model of the Drosophila Visual System
Fabian David Tschopp, Michael B. Reiser, Srinivas C. Turaga
What can we learn from a connectome? We constructed a simplified model of the first two stages of the fly visual system, the lamina and medulla. The resulting hexagonal lattice con…
Discrete flow posteriors for variational inference in discrete dynamical systems
Laurence Aitchison, Vincent Adam, Srinivas C. Turaga
Each training step for a variational autoencoder (VAE) requires us to sample from the approximate posterior, so we usually choose simple (e.g. factorised) approximate posteriors in…