Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning for Multi-Model and Multi-Type Fitting   Multi-model fitting has been extensively studied from the random sampling and
clustering perspectives. Most assume that only a single type/class of model is
present and their generalizations to fitting multiple types of
models/structures simultaneously are non-trivial. The inherent challenges
include choice of types and numbers of models, sampling imbalance and parameter
tuning, all of which render conventional approaches ineffective. In this work,
we formulate the multi-model multi-type fitting problem as one of learning deep
feature embedding that is clustering-friendly. In other words, points of the
same clusters are embedded closer together through the network. For inference,
we apply K-means to cluster the data in the embedded feature space and model
selection is enabled by analyzing the K-means residuals. Experiments are
carried out on both synthetic and real world multi-type fitting datasets,
producing state-of-the-art results. Comparisons are also made on single-type
multi-model fitting tasks with promising results as well.

23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
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The categories of this paper are: hep-ex