Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Semi-Supervised Learning for Remaining Useful Lifetime
  Estimation Through Self-Supervision   RUL estimation suffers from a server data imbalance where data from machines
near their end of life is rare. Additionally, the data produced by a machine
can only be labeled after the machine failed. Semi-Supervised Learning (SSL)
can incorporate the unlabeled data produced by machines that did not yet fail.
Previous work on SSL evaluated their approaches under unrealistic conditions
where the data near failure was still available. Even so, only moderate
improvements were made. This paper proposes a novel SSL approach based on
self-supervised pre-training. The method can outperform two competing
approaches from the literature and a supervised baseline under realistic
conditions on the NASA C-MAPSS dataset.

36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph