239 citations · 269 across the 5 of their papers we have counts for
6 papers
Tuning of Mixture-of-Experts Mixed-Precision Neural Networks
Fabian Tschopp
Deep learning has become a useful data analysis method, however mainstream adaption in distributed computer software and embedded devices has been low so far. Often, adding deep le…
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…
Large Scale Image Segmentation with Structured Loss based Deep Learning for Connectome Reconstruction
Jan Funke, Fabian David Tschopp, William Grisaitis +4
We present a method combining affinity prediction with region agglomeration, which improves significantly upon the state of the art of neuron segmentation from electron microscopy…
Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration
Toufiq Parag, Fabian Tschopp, William Grisaitis +6
The field of connectomics has recently produced neuron wiring diagrams from relatively large brain regions from multiple animals. Most of these neural reconstructions were computed…
Robust Large-Scale Localization in 3D Point Clouds Revisited
Fabian Tschopp, Marco Zorzi
We tackle the problem of getting a full 6-DOF pose estimation of a query image inside a given point cloud. This technical report re-evaluates the algorithms proposed by Y. Li et al…
Efficient Convolutional Neural Networks for Pixelwise Classification on Heterogeneous Hardware Systems
Fabian Tschopp
This work presents and analyzes three convolutional neural network (CNN) models for efficient pixelwise classification of images. When using convolutional neural networks to classi…