Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GENNI: Visualising the Geometry of Equivalences for Neural Network
  Identifiability   We propose an efficient algorithm to visualise symmetries in neural networks.
Typically, models are defined with respect to a parameter space, where
non-equal parameters can produce the same input-output map. Our proposed
method, GENNI, allows us to efficiently identify parameters that are
functionally equivalent and then visualise the subspace of the resulting
equivalence class. By doing so, we are now able to better explore questions
surrounding identifiability, with applications to optimisation and
generalizability, for commonly used or newly developed neural network
architectures.

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The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
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The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
16
The categories of this paper are: stat
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The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
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The categories of this paper are: gr-qc
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The categories of this paper are: adap-org
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The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn