Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reducing the Long Tail Losses in Scientific Emulations with Active
  Learning   Deep-learning-based models are increasingly used to emulate scientific
simulations to accelerate scientific research. However, accurate, supervised
deep learning models require huge amount of labelled data, and that often
becomes the bottleneck in employing neural networks. In this work, we leveraged
an active learning approach called core-set selection to actively select data,
per a pre-defined budget, to be labelled for training. To further improve the
model performance and reduce the training costs, we also warm started the
training using a shrink-and-perturb trick. We tested on two case studies in
different fields, namely galaxy halo occupation distribution modelling in
astrophysics and x-ray emission spectroscopy in plasma physics, and the results
are promising: we achieved competitive overall performance compared to using a
random sampling baseline, and more importantly, successfully reduced the larger
absolute losses, i.e. the long tail in the loss distribution, at virtually no
overhead costs.

12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an