Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learned Initializations for Optimizing Coordinate-Based Neural
  Representations   Coordinate-based neural representations have shown significant promise as an
alternative to discrete, array-based representations for complex low
dimensional signals. However, optimizing a coordinate-based network from
randomly initialized weights for each new signal is inefficient. We propose
applying standard meta-learning algorithms to learn the initial weight
parameters for these fully-connected networks based on the underlying class of
signals being represented (e.g., images of faces or 3D models of chairs).
Despite requiring only a minor change in implementation, using these learned
initial weights enables faster convergence during optimization and can serve as
a strong prior over the signal class being modeled, resulting in better
generalization when only partial observations of a given signal are available.
We explore these benefits across a variety of tasks, including representing 2D
images, reconstructing CT scans, and recovering 3D shapes and scenes from 2D
image observations.

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The categories of this paper are: astro-ph
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
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The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
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The categories of this paper are: math