Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust neural circuit reconstruction from serial electron microscopy
  with convolutional recurrent networks   Recent successes in deep learning have started to impact neuroscience. Of
particular significance are claims that current segmentation algorithms achieve
"super-human" accuracy in an area known as connectomics. However, as we will
show, these algorithms do not effectively generalize beyond the particular
source and brain tissues used for training -- severely limiting their usability
by the broader neuroscience community. To fill this gap, we describe a novel
connectomics challenge for source- and tissue-agnostic reconstruction of
neurons (STAR), which favors broad generalization over fitting specific
datasets. We first demonstrate that current state-of-the-art approaches to
neuron segmentation perform poorly on the challenge. We further describe a
novel convolutional recurrent neural network module that combines short-range
horizontal connections within a processing stage and long-range top-down
connections between stages. The resulting architecture establishes the state of
the art on the STAR challenge and represents a significant step towards widely
usable and fully-automated connectomics analysis.

13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an